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[GH-ISSUE #4767] Model response corruption and leaking data between session. #49513

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opened 2026-04-28 12:06:07 -05:00 by GiteaMirror · 8 comments
GiteaMirror commented 2026-04-28 12:06:07 -05:00
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Originally created by @MarkWard0110 on GitHub (Jun 1, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/4767

Originally assigned to: @jmorganca on GitHub.

What is the issue?

main when running a model (specifically llama3:8b-instruct-fp16 will begin to generate gibberish. It will also leak state between sessions. Swapping out the models will reset the issue, but it will quickly return after a few runs against the model.
This issue does not happen with 0.1.38. main began to do this before the 0.1.39 was released. I will try to track down the specific commit.

I have not upgraded to Ollama's latest version (0.1.39) because of what I am seeing with the latest main.

The behavior happened with nvidia driver version 550 and the updated driver.

Hardware
NVidia RTX 4070 TI Super 16GB

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 555.42.02              Driver Version: 555.42.02      CUDA Version: 12.5     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4070 ...    Off |   00000000:01:00.0 Off |                  N/A |
|  0%   36C    P8              9W /  285W |       5MiB /  16376MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

In the following ollama run output, I will begin with a freshly started ollama serv using the latest build off main. The chat will start ok but then begin to generate gibberish. I will exit the chat and start a new session. The first prompt will generate what appears to be information related to the previous session. Note that this behavior does not happen on 0.1.38.

first chat

ollama run llama3:8b-instruct-fp16
>>> Using Python write an example of the quick sort
Here is an example implementation of QuickSort in Python:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    less = [x for x in arr[1:] if x <= pivot]
    greater = [x for x in arr[1:] if x > pivot]
    return quicksort(less) + [pivot] + quicksort(greater)

# Example usage:
arr = [5, 2, 8, 3, 1, 6, 4]
print(quicksort(arr))  # Output: [1, 2, 3, 4, 5, 6, 8]

Here's a brief explanation of how the algorithm works:

1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).
2. Choose a pivot element from the array (in this case, we use the first element).
3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the 
pivot.
4. Recursively apply the QuickSort algorithm to `less` and `greater`.
5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.

Note that this is a "Lomuto" partition scheme, which is slightly faster than the standard "Hoare" partition scheme but has a slightly different implementation.

Also, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like 
introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.

>>> How do you potty train a puppy?
Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:

**Preparation**

1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.
2. **Get the right equipment**: You'll need poop bags, treats, and praise!
3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.

**Training**

1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.
2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with 
good behavior.
3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.
4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot 
again.
5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.

**Additional Tips**

1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.
2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.
3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.
4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time 
without supervision.

**Common Mistakes to Avoid**

1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.
2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.
3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.

By following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!

>>> Using Python generate 100 random numbers
Here is a simple Python script that generates 100 random numbers:


import random

# Generate 100 random numbers
random_numbers = [random.randint(1, 100) for _ in range(100)]

print("Random Numbers:")
for num in random_numbers:
    print(num)


In this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.

When you run this script, it will output 100 random numbers.

>>> How do you potty train a puppy?
Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:

**Preparation**

1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.
2. **Get the right equipment**: You'll need poop bags, treats, and praise!
3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.

**Training**

1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.
2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with 
good behavior.
3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.
4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot 
again.
5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.

**Additional Tips**

1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.
2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.
3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.
4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time 
without supervision.

**Common Mistakes to Avoid**

1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.
2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.
3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.

**Additional Resources**

1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.
2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.
3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.

By following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!

>>> Please explain how AI transformers work
AI Transformers!

Transformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:

**What is a Transformer?**

A Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a "transformer" 
because it uses self-attention mechanisms to transform input sequences into output sequences.

**Components of a Transformer:**

1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.
2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).
3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.

**How Self-Attention Works:**

1. **Key components:

a transformer self-Attention

* **

**Encoder uses three main components: ">

**What is a matrix multiplication...

>>> How do you potty train a puppy?
Potty training a puppy!

Potty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for 
puppies

Potty training training a a puppy, and .

>>> Using Python create an example of the quick sort.
Here is an efficient sorting algorithm in

>>> Please try again
I apologize for the quicksort


def quicksort.py

The quicksort.

In



>>> Where is the output?
I apologize for 

Here is a sort()

>>> Hello
Hello! It seems like
>>> /exit

second chat

$ ollama run llama3:8b-instruct-fp16
>>> Using Python generate an example of the quick sort
Here is a dog, also known and consistency, and positive reinforcement. Here's an example implementation of how-to help you get started:

python:

**Before You Need**

1. **Choose a designated potty area**: Pick a specific spots outside, such as a grassy area or a puppy pad, where you want your puppy to go potty.
2. **Use the right equipment**: Obtain poop bags, pee pads (for rewards.
3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, exercise, and potty breaks.

**Training Steps**

1. **Watch for signs**: Puppies often sniff and sniff and circle before they go potty. If you can't these behaviors like behavior, quickly take them to the designated potty area.
2. **Take them and reward**: When your puppy goes potty in the designated area, praise them enthusiastically and reward with a positive reinforcement will help them associate going potty training with good
behavior.
3. Use a.**Clean up to the first thing small bladders and can't hold their urine for long. Take your puppy outside every hour by:
        after meals, ahahs. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to 
the same spot again.
5. **Gradually increase**: Keep an supervise**: Keep an eye on your puppy at all times, especially during potty training period.

**training. This will help you catch any mistakes and preventable and praise and Advanced Techniques**

* **Use potty pad or a signal**: Train your chosen a bell or use a specific signal to indicate they need to go outside.
2. **Gradually increase in the clock**: If it's or too cold, consider using puppy pads indoors until the potties to avoid punishing accidents happen**: Potty training can take time, and accidents will 
likely occur. Don't scold your puppy will learn eventually get the importance of  Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more 
freedom in the house.

**such as going upstairs rooms or longer periods without supervision.
5.

I'll follow this post with the log.

OS

No response

GPU

No response

CPU

No response

Ollama version

0.0.0

Originally created by @MarkWard0110 on GitHub (Jun 1, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/4767 Originally assigned to: @jmorganca on GitHub. ### What is the issue? `main` when running a model (specifically `llama3:8b-instruct-fp16` will begin to generate gibberish. It will also leak state between sessions. Swapping out the models will reset the issue, but it will quickly return after a few runs against the model. This issue does not happen with 0.1.38. `main` began to do this before the 0.1.39 was released. I will try to track down the specific commit. I have not upgraded to Ollama's latest version (0.1.39) because of what I am seeing with the latest `main`. The behavior happened with nvidia driver version 550 and the updated driver. Hardware NVidia RTX 4070 TI Super 16GB ``` +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 555.42.02 Driver Version: 555.42.02 CUDA Version: 12.5 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 4070 ... Off | 00000000:01:00.0 Off | N/A | | 0% 36C P8 9W / 285W | 5MiB / 16376MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | No running processes found | +-----------------------------------------------------------------------------------------+ ``` In the following `ollama run` output, I will begin with a freshly started ollama serv using the latest build off `main`. The chat will start ok but then begin to generate gibberish. I will exit the chat and start a new session. The first prompt will generate what appears to be information related to the previous session. Note that this behavior does not happen on 0.1.38. first chat ``` ollama run llama3:8b-instruct-fp16 >>> Using Python write an example of the quick sort Here is an example implementation of QuickSort in Python: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[0] less = [x for x in arr[1:] if x <= pivot] greater = [x for x in arr[1:] if x > pivot] return quicksort(less) + [pivot] + quicksort(greater) # Example usage: arr = [5, 2, 8, 3, 1, 6, 4] print(quicksort(arr)) # Output: [1, 2, 3, 4, 5, 6, 8] Here's a brief explanation of how the algorithm works: 1. If the length of the input array is 0 or 1, return the original array (since it's already sorted). 2. Choose a pivot element from the array (in this case, we use the first element). 3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot. 4. Recursively apply the QuickSort algorithm to `less` and `greater`. 5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list. Note that this is a "Lomuto" partition scheme, which is slightly faster than the standard "Hoare" partition scheme but has a slightly different implementation. Also, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs. >>> How do you potty train a puppy? Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend: **Preparation** 1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty. 2. **Get the right equipment**: You'll need poop bags, treats, and praise! 3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks. **Training** 1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area. 2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior. 3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents. 4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again. 5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen. **Additional Tips** 1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside. 2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas. 3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually. 4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision. **Common Mistakes to Avoid** 1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training. 2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again. 3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks. By following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully! >>> Using Python generate 100 random numbers Here is a simple Python script that generates 100 random numbers: import random # Generate 100 random numbers random_numbers = [random.randint(1, 100) for _ in range(100)] print("Random Numbers:") for num in random_numbers: print(num) In this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers. When you run this script, it will output 100 random numbers. >>> How do you potty train a puppy? Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend: **Preparation** 1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty. 2. **Get the right equipment**: You'll need poop bags, treats, and praise! 3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks. **Training** 1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area. 2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior. 3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents. 4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again. 5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen. **Additional Tips** 1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside. 2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas. 3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually. 4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision. **Common Mistakes to Avoid** 1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training. 2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again. 3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks. **Additional Resources** 1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice. 2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers. 3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience. By following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully! >>> Please explain how AI transformers work AI Transformers! Transformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work: **What is a Transformer?** A Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a "transformer" because it uses self-attention mechanisms to transform input sequences into output sequences. **Components of a Transformer:** 1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence. 2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text). 3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially. **How Self-Attention Works:** 1. **Key components: a transformer self-Attention * ** **Encoder uses three main components: "> **What is a matrix multiplication... >>> How do you potty train a puppy? Potty training a puppy! Potty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies Potty training training a a puppy, and . >>> Using Python create an example of the quick sort. Here is an efficient sorting algorithm in >>> Please try again I apologize for the quicksort def quicksort.py The quicksort. In >>> Where is the output? I apologize for Here is a sort() >>> Hello Hello! It seems like >>> /exit ``` second chat ``` $ ollama run llama3:8b-instruct-fp16 >>> Using Python generate an example of the quick sort Here is a dog, also known and consistency, and positive reinforcement. Here's an example implementation of how-to help you get started: python: **Before You Need** 1. **Choose a designated potty area**: Pick a specific spots outside, such as a grassy area or a puppy pad, where you want your puppy to go potty. 2. **Use the right equipment**: Obtain poop bags, pee pads (for rewards. 3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, exercise, and potty breaks. **Training Steps** 1. **Watch for signs**: Puppies often sniff and sniff and circle before they go potty. If you can't these behaviors like behavior, quickly take them to the designated potty area. 2. **Take them and reward**: When your puppy goes potty in the designated area, praise them enthusiastically and reward with a positive reinforcement will help them associate going potty training with good behavior. 3. Use a.**Clean up to the first thing small bladders and can't hold their urine for long. Take your puppy outside every hour by: after meals, ahahs. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again. 5. **Gradually increase**: Keep an supervise**: Keep an eye on your puppy at all times, especially during potty training period. **training. This will help you catch any mistakes and preventable and praise and Advanced Techniques** * **Use potty pad or a signal**: Train your chosen a bell or use a specific signal to indicate they need to go outside. 2. **Gradually increase in the clock**: If it's or too cold, consider using puppy pads indoors until the potties to avoid punishing accidents happen**: Potty training can take time, and accidents will likely occur. Don't scold your puppy will learn eventually get the importance of Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house. **such as going upstairs rooms or longer periods without supervision. 5. ``` I'll follow this post with the log. ### OS _No response_ ### GPU _No response_ ### CPU _No response_ ### Ollama version 0.0.0
GiteaMirror added the bug label 2026-04-28 12:06:07 -05:00
GiteaMirror commented 2026-04-28 12:06:08 -05:00
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Owner
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@MarkWard0110 commented on GitHub (Jun 1, 2024):

Log

Jun 01 14:32:07 quorra systemd[1]: Started Ollama Service.
Jun 01 14:32:07 quorra fork_ollama[20089]: 2024/06/01 14:32:07 routes.go:1007: INFO server config env="map[OLLAMA_DEBUG:true OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST: OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS: OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:*] OLLAMA_RUNNERS_DIR: OLLAMA_TMPDIR:]"
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.365Z level=INFO source=images.go:729 msg="total blobs: 98"
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.367Z level=INFO source=images.go:736 msg="total unused blobs removed: 0"
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] [WARNING] Creating an Engine instance with the Logger and Recovery middleware already attached.
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] [WARNING] Running in "debug" mode. Switch to "release" mode in production.
Jun 01 14:32:07 quorra fork_ollama[20089]:  - using env:        export GIN_MODE=release
Jun 01 14:32:07 quorra fork_ollama[20089]:  - using code:        gin.SetMode(gin.ReleaseMode)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/pull                 --> github.com/ollama/ollama/server.(*Server).PullModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/generate             --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/chat                 --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/embeddings           --> github.com/ollama/ollama/server.(*Server).EmbeddingsHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/create               --> github.com/ollama/ollama/server.(*Server).CreateModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/push                 --> github.com/ollama/ollama/server.(*Server).PushModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/copy                 --> github.com/ollama/ollama/server.(*Server).CopyModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] DELETE /api/delete               --> github.com/ollama/ollama/server.(*Server).DeleteModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/show                 --> github.com/ollama/ollama/server.(*Server).ShowModelHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /api/blobs/:digest        --> github.com/ollama/ollama/server.(*Server).CreateBlobHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD   /api/blobs/:digest        --> github.com/ollama/ollama/server.(*Server).HeadBlobHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET    /api/ps                   --> github.com/ollama/ollama/server.(*Server).ProcessHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST   /v1/chat/completions      --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET    /                         --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET    /api/tags                 --> github.com/ollama/ollama/server.(*Server).ListModelsHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET    /api/version              --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD   /                         --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD   /api/tags                 --> github.com/ollama/ollama/server.(*Server).ListModelsHandler-fm (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD   /api/version              --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers)
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.367Z level=INFO source=routes.go:1053 msg="Listening on [::]:11434 (version 0.0.0)"
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama316030735/runners
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu file=build/linux/x86_64/cpu/bin/ollama_llama_server.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu_avx file=build/linux/x86_64/cpu_avx/bin/ollama_llama_server.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu_avx2 file=build/linux/x86_64/cpu_avx2/bin/ollama_llama_server.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcublas.so.12.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcublasLt.so.12.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcudart.so.12.gz
Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/ollama_llama_server.gz
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cuda_v12 cpu cpu_avx cpu_avx2]"
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:45 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY"
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=sched.go:90 msg="starting llm scheduler"
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs"
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so*
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]"
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.468Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02]
Jun 01 14:32:09 quorra fork_ollama[20089]: CUDA driver version: 12.5
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.550Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.550Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2"
Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb
Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb
Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.669Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu"
Jun 01 14:32:09 quorra fork_ollama[20089]: releasing nvcuda library
Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.669Z level=INFO source=types.go:71 msg="inference compute" id=GPU-007c9d9a-8177-bd6f-7654-45652102b937 library=cuda compute=8.9 driver=12.5 name="NVIDIA GeForce RTX 4070 Ti SUPER" total="15.6 GiB" available="15.4 GiB"
Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 |      59.656µs |       127.0.0.1 | HEAD     "/"
Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 |    1.347587ms |       127.0.0.1 | POST     "/api/show"
Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 |     598.189µs |       127.0.0.1 | POST     "/api/show"
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs"
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so*
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]"
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.455Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02]
Jun 01 14:32:33 quorra fork_ollama[20089]: CUDA driver version: 12.5
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.455Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.456Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2"
Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb
Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb
Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.582Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu"
Jun 01 14:32:33 quorra fork_ollama[20089]: releasing nvcuda library
Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.582Z level=DEBUG source=gguf.go:57 msg="model = &llm.gguf{containerGGUF:(*llm.containerGGUF)(0xc0001ddbc0), kv:llm.KV{}, tensors:[]*llm.Tensor(nil), parameters:0x0}"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=sched.go:153 msg="loading first model" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=memory.go:44 msg=evaluating library=cuda gpu_count=1 available="15.4 GiB"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="15.4 GiB" memory.required.full="15.2 GiB" memory.required.partial="15.2 GiB" memory.required.kv="256.0 MiB" memory.weights.total="14.0 GiB" memory.weights.repeating="13.0 GiB" memory.weights.nonrepeating="1002.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=sched.go:565 msg="new model will fit in available VRAM in single GPU, loading" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e gpu=GPU-007c9d9a-8177-bd6f-7654-45652102b937 available=16529555456 required="15.2 GiB"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=memory.go:44 msg=evaluating library=cuda gpu_count=1 available="15.4 GiB"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="15.4 GiB" memory.required.full="15.2 GiB" memory.required.partial="15.2 GiB" memory.required.kv="256.0 MiB" memory.weights.total="14.0 GiB" memory.weights.repeating="13.0 GiB" memory.weights.nonrepeating="1002.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama316030735/runners/cuda_v12/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --verbose --parallel 1 --port 33377"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=server.go:356 msg=subprocess environment="[PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin LD_LIBRARY_PATH=/tmp/ollama316030735/runners/cuda_v12 CUDA_VISIBLE_DEVICES=GPU-007c9d9a-8177-bd6f-7654-45652102b937]"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=sched.go:338 msg="loaded runners" count=1
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] build info | build=299 commit="5921b8f0" tid="139987180924928" timestamp=1717252354
Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139987180924928" timestamp=1717252354 total_threads=32
Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="33377" tid="139987180924928" timestamp=1717252354
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e (version GGUF V3 (latest))
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   0:                       general.architecture str              = llama
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   1:                               general.name str              = Meta-Llama-3-8B-Instruct
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   2:                          llama.block_count u32              = 32
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   3:                       llama.context_length u32              = 8192
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   6:                 llama.attention.head_count u32              = 32
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   7:              llama.attention.head_count_kv u32              = 8
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   8:                       llama.rope.freq_base f32              = 500000.000000
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  10:                          general.file_type u32              = 1
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv  21:               general.quantization_version u32              = 2
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - type  f32:   65 tensors
Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - type  f16:  226 tensors
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_vocab: special tokens cache size = 256
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_vocab: token to piece cache size = 1.5928 MB
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: format           = GGUF V3 (latest)
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: arch             = llama
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: vocab type       = BPE
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_vocab          = 128256
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_merges         = 280147
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_ctx_train      = 8192
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd           = 4096
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_head           = 32
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_head_kv        = 8
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_layer          = 32
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_rot            = 128
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_head_k    = 128
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_head_v    = 128
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_gqa            = 4
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_k_gqa     = 1024
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_v_gqa     = 1024
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_norm_eps       = 0.0e+00
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_clamp_kqv      = 0.0e+00
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_logit_scale    = 0.0e+00
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_ff             = 14336
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_expert         = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_expert_used    = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: causal attn      = 1
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: pooling type     = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope type        = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope scaling     = linear
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: freq_base_train  = 500000.0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: freq_scale_train = 1
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_yarn_orig_ctx  = 8192
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope_finetuned   = unknown
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_conv       = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_inner      = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_state      = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_dt_rank      = 0
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model type       = 8B
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model ftype      = F16
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model params     = 8.03 B
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model size       = 14.96 GiB (16.00 BPW)
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: general.name     = Meta-Llama-3-8B-Instruct
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: LF token         = 128 'Ä'
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'
Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.610Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   yes
Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: found 1 CUDA devices:
Jun 01 14:32:34 quorra fork_ollama[20089]:   Device 0: NVIDIA GeForce RTX 4070 Ti SUPER, compute capability 8.9, VMM: yes
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: ggml ctx size =    0.30 MiB
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloading 32 repeating layers to GPU
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloading non-repeating layers to GPU
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloaded 33/33 layers to GPU
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors:        CPU buffer size =  1002.00 MiB
Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors:      CUDA0 buffer size = 14315.02 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.112Z level=DEBUG source=server.go:578 msg="model load progress 0.30"
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.363Z level=DEBUG source=server.go:578 msg="model load progress 0.59"
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.614Z level=DEBUG source=server.go:578 msg="model load progress 0.88"
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_ctx      = 2048
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_batch    = 512
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_ubatch   = 512
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: flash_attn = 0
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: freq_base  = 500000.0
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: freq_scale = 1
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_kv_cache_init:      CUDA0 KV buffer size =   256.00 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: KV self size  =  256.00 MiB, K (f16):  128.00 MiB, V (f16):  128.00 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model:  CUDA_Host  output buffer size =     0.50 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model:      CUDA0 compute buffer size =   258.50 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model:  CUDA_Host compute buffer size =    12.01 MiB
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: graph nodes  = 1030
Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: graph splits = 2
Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [initialize] initializing slots | n_slots=1 tid="139987180924928" timestamp=1717252355
Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [initialize] new slot | n_ctx_slot=2048 slot_id=0 tid="139987180924928" timestamp=1717252355
Jun 01 14:32:35 quorra fork_ollama[20380]: INFO [main] model loaded | tid="139987180924928" timestamp=1717252355
Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [update_slots] all slots are idle and system prompt is empty, clear the KV cache | tid="139987180924928" timestamp=1717252355
Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=0 tid="139987180924928" timestamp=1717252355
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=INFO source=server.go:572 msg="llama runner started in 1.51 seconds"
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:351 msg="finished setting up runner" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1 window=2048
Jun 01 14:32:35 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:35 | 200 |  2.413840188s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:355 msg="context for request finished"
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.348Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1 tid="139987180924928" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2 tid="139987180924928" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=57182 status=200 tid="139986500345856" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=20 window=2048
Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=3 tid="139987180924928" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=0 n_past_se=0 n_prompt_tokens_processed=18 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372
Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=0 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372
Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      41.27 ms /    18 tokens (    2.29 ms per token,   436.21 tokens per second) | n_prompt_tokens_processed=18 n_tokens_second=436.2050163576881 slot_id=0 t_prompt_processing=41.265 t_token=2.2925 task_id=4 tid="139987180924928" timestamp=1717252383
Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =   10481.16 ms /   408 runs   (   25.69 ms per token,    38.93 tokens per second) | n_decoded=408 n_tokens_second=38.926992711397666 slot_id=0 t_token=25.68911519607843 t_token_generation=10481.159 task_id=4 tid="139987180924928" timestamp=1717252383
Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =   10522.42 ms | slot_id=0 t_prompt_processing=41.265 t_token_generation=10481.159 t_total=10522.423999999999 task_id=4 tid="139987180924928" timestamp=1717252383
Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=426 n_ctx=2048 n_past=425 n_system_tokens=0 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252383 truncated=false
Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=57182 status=200 tid="139986500345856" timestamp=1717252383
Jun 01 14:33:03 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:03 | 200 | 10.661266862s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.573Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=415 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=416 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44456 status=200 tid="139986491953152" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=417 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44456 status=200 tid="139986491953152" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.705Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=446 window=2048
Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.706Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[0]\n    less = [x for x in arr[1:] if x <= pivot]\n    greater = [x for x in arr[1:] if x > pivot]\n    return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr))  # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.706Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=418 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=425 n_past_se=0 n_prompt_tokens_processed=19 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=425 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392
Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      32.12 ms /    19 tokens (    1.69 ms per token,   591.44 tokens per second) | n_prompt_tokens_processed=19 n_tokens_second=591.4396887159533 slot_id=0 t_prompt_processing=32.125 t_token=1.6907894736842106 task_id=419 tid="139987180924928" timestamp=1717252407
Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =   14795.09 ms /   567 runs   (   26.09 ms per token,    38.32 tokens per second) | n_decoded=567 n_tokens_second=38.32353526028845 slot_id=0 t_token=26.093626102292767 t_token_generation=14795.086 task_id=419 tid="139987180924928" timestamp=1717252407
Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =   14827.21 ms | slot_id=0 t_prompt_processing=32.125 t_token_generation=14795.086 t_total=14827.211 task_id=419 tid="139987180924928" timestamp=1717252407
Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1011 n_ctx=2048 n_past=1010 n_system_tokens=0 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252407 truncated=false
Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=44470 status=200 tid="139986483560448" timestamp=1717252407
Jun 01 14:33:27 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:27 | 200 | 14.963085151s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.155Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=989 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=990 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46472 status=200 tid="139986405945344" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=991 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46472 status=200 tid="139986405945344" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=992 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46482 status=200 tid="139986397552640" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.336Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1029 window=2048
Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.337Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[0]\n    less = [x for x in arr[1:] if x <= pivot]\n    greater = [x for x in arr[1:] if x > pivot]\n    return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr))  # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.337Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=993 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1010 n_past_se=0 n_prompt_tokens_processed=17 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1010 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434
Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      39.14 ms /    17 tokens (    2.30 ms per token,   434.32 tokens per second) | n_prompt_tokens_processed=17 n_tokens_second=434.31607991415865 slot_id=0 t_prompt_processing=39.142 t_token=2.3024705882352943 task_id=994 tid="139987180924928" timestamp=1717252437
Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =    3139.31 ms /   121 runs   (   25.94 ms per token,    38.54 tokens per second) | n_decoded=121 n_tokens_second=38.54346476442458 slot_id=0 t_token=25.944735537190084 t_token_generation=3139.313 task_id=994 tid="139987180924928" timestamp=1717252437
Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =    3178.45 ms | slot_id=0 t_prompt_processing=39.142 t_token_generation=3139.313 t_total=3178.455 task_id=994 tid="139987180924928" timestamp=1717252437
Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1148 n_ctx=2048 n_past=1147 n_system_tokens=0 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252437 truncated=false
Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=46482 status=200 tid="139986397552640" timestamp=1717252437
Jun 01 14:33:57 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:57 | 200 |  3.409412656s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.357Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1118 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1119 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44974 status=200 tid="139986389159936" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1120 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44974 status=200 tid="139986389159936" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1121 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44980 status=200 tid="139986380767232" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1122 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44980 status=200 tid="139986380767232" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.624Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1168 window=2048
Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.625Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[0]\n    less = [x for x in arr[1:] if x <= pivot]\n    greater = [x for x in arr[1:] if x > pivot]\n    return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr))  # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.625Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1123 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1147 n_past_se=0 n_prompt_tokens_processed=19 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1147 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459
Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      42.35 ms /    19 tokens (    2.23 ms per token,   448.67 tokens per second) | n_prompt_tokens_processed=19 n_tokens_second=448.67405010980707 slot_id=0 t_prompt_processing=42.347 t_token=2.2287894736842104 task_id=1124 tid="139987180924928" timestamp=1717252476
Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =   17281.20 ms /   658 runs   (   26.26 ms per token,    38.08 tokens per second) | n_decoded=658 n_tokens_second=38.07605292293597 slot_id=0 t_token=26.263226443769 t_token_generation=17281.203 task_id=1124 tid="139987180924928" timestamp=1717252476
Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =   17323.55 ms | slot_id=0 t_prompt_processing=42.347 t_token_generation=17281.203 t_total=17323.550000000003 task_id=1124 tid="139987180924928" timestamp=1717252476
Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1824 n_ctx=2048 n_past=1823 n_system_tokens=0 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252476 truncated=false
Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=44986 status=200 tid="139986372374528" timestamp=1717252476
Jun 01 14:34:36 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:34:36 | 200 | 17.595507773s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.478Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1785 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1786 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47160 status=200 tid="139986363981824" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1787 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47160 status=200 tid="139986363981824" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1788 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47166 status=200 tid="139986355589120" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1789 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47166 status=200 tid="139986355589120" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1790 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47180 status=200 tid="139986347196416" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.751Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1841 window=2048
Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.751Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[0]\n    less = [x for x in arr[1:] if x <= pivot]\n    greater = [x for x in arr[1:] if x > pivot]\n    return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr))  # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.752Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1791 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1823 n_past_se=0 n_prompt_tokens_processed=16 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1823 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503
Jun 01 14:35:09 quorra fork_ollama[20380]: DEBUG [update_slots] slot context shift | n_cache_tokens=2048 n_ctx=2048 n_discard=1011 n_keep=24 n_left=2023 n_past=2047 n_system_tokens=0 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252509
Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      43.36 ms /    16 tokens (    2.71 ms per token,   369.03 tokens per second) | n_prompt_tokens_processed=16 n_tokens_second=369.02922250155683 slot_id=0 t_prompt_processing=43.357 t_token=2.7098125 task_id=1792 tid="139987180924928" timestamp=1717252510
Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =    6332.54 ms /   240 runs   (   26.39 ms per token,    37.90 tokens per second) | n_decoded=240 n_tokens_second=37.89946629655732 slot_id=0 t_token=26.385595833333333 t_token_generation=6332.543 task_id=1792 tid="139987180924928" timestamp=1717252510
Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =    6375.90 ms | slot_id=0 t_prompt_processing=43.357 t_token_generation=6332.543 t_total=6375.9 task_id=1792 tid="139987180924928" timestamp=1717252510
Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1068 n_ctx=2048 n_past=1067 n_system_tokens=0 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252510 truncated=true
Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=47180 status=200 tid="139986347196416" timestamp=1717252510
Jun 01 14:35:10 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:10 | 200 |  6.696495832s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.451Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2035 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2036 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51522 status=200 tid="139986338803712" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2037 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51522 status=200 tid="139986338803712" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2038 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51534 status=200 tid="139986330411008" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2039 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51534 status=200 tid="139986330411008" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2040 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2041 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.812Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2099 window=2048
Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.812Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1673 window=2048
Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.813Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.813Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2042 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=4 n_past_se=0 n_prompt_tokens_processed=1667 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=4 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524
Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =     346.46 ms /  1667 tokens (    0.21 ms per token,  4811.55 tokens per second) | n_prompt_tokens_processed=1667 n_tokens_second=4811.550029152163 slot_id=0 t_prompt_processing=346.458 t_token=0.20783323335332934 task_id=2043 tid="139987180924928" timestamp=1717252526
Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =    1475.38 ms /    57 runs   (   25.88 ms per token,    38.63 tokens per second) | n_decoded=57 n_tokens_second=38.6341669728029 slot_id=0 t_token=25.883824561403507 t_token_generation=1475.378 task_id=2043 tid="139987180924928" timestamp=1717252526
Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =    1821.84 ms | slot_id=0 t_prompt_processing=346.458 t_token_generation=1475.378 t_total=1821.836 task_id=2043 tid="139987180924928" timestamp=1717252526
Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1728 n_ctx=2048 n_past=1727 n_system_tokens=0 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252526 truncated=false
Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252526
Jun 01 14:35:26 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:26 | 200 |  2.230278909s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.433Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2103 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2104 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56040 status=200 tid="139986313625600" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2105 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56040 status=200 tid="139986313625600" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2106 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56048 status=200 tid="139986305232896" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2107 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56048 status=200 tid="139986305232896" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2108 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2109 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2110 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2175 window=2048
Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1749 window=2048
Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2111 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1727 n_past_se=0 n_prompt_tokens_processed=20 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1727 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555
Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      45.08 ms /    20 tokens (    2.25 ms per token,   443.68 tokens per second) | n_prompt_tokens_processed=20 n_tokens_second=443.675407072186 slot_id=0 t_prompt_processing=45.078 t_token=2.2539000000000002 task_id=2112 tid="139987180924928" timestamp=1717252556
Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =     185.88 ms /     8 runs   (   23.23 ms per token,    43.04 tokens per second) | n_decoded=8 n_tokens_second=43.03898255845232 slot_id=0 t_token=23.23475 t_token_generation=185.878 task_id=2112 tid="139987180924928" timestamp=1717252556
Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =     230.96 ms | slot_id=0 t_prompt_processing=45.078 t_token_generation=185.878 t_total=230.956 task_id=2112 tid="139987180924928" timestamp=1717252556
Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1755 n_ctx=2048 n_past=1754 n_system_tokens=0 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252556 truncated=false
Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=56062 status=200 tid="139986288447488" timestamp=1717252556
Jun 01 14:35:56 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:56 | 200 |  646.179302ms |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.246Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2123 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2124 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35034 status=200 tid="139986280054784" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2125 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35034 status=200 tid="139986280054784" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2126 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35048 status=200 tid="139986263269376" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2127 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35048 status=200 tid="139986263269376" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2128 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2129 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2130 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2131 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35056 status=200 tid="139986254876672" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.700Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2195 window=2048
Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.700Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1769 window=2048
Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.701Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.701Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2132 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1754 n_past_se=0 n_prompt_tokens_processed=13 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1754 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567
Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      35.22 ms /    13 tokens (    2.71 ms per token,   369.11 tokens per second) | n_prompt_tokens_processed=13 n_tokens_second=369.1084611016468 slot_id=0 t_prompt_processing=35.22 t_token=2.709230769230769 task_id=2133 tid="139987180924928" timestamp=1717252568
Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =     556.28 ms /    22 runs   (   25.29 ms per token,    39.55 tokens per second) | n_decoded=22 n_tokens_second=39.54835775444425 slot_id=0 t_token=25.2855 t_token_generation=556.281 task_id=2133 tid="139987180924928" timestamp=1717252568
Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =     591.50 ms | slot_id=0 t_prompt_processing=35.22 t_token_generation=556.281 t_total=591.501 task_id=2133 tid="139987180924928" timestamp=1717252568
Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1789 n_ctx=2048 n_past=1788 n_system_tokens=0 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252568 truncated=false
Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=35056 status=200 tid="139986254876672" timestamp=1717252568
Jun 01 14:36:08 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:08 | 200 |  1.092247407s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:36:14 quorra fork_ollama[20089]: time=2024-06-01T14:36:14.715Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2158 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2159 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35068 status=200 tid="139986246483968" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2160 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35068 status=200 tid="139986246483968" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2161 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35078 status=200 tid="139986238091264" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2162 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35078 status=200 tid="139986238091264" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2163 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252574
Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2164 tid="139987180924928" timestamp=1717252574
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2165 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2166 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35102 status=200 tid="139986221305856" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2167 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35102 status=200 tid="139986221305856" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2231 window=2048
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1805 window=2048
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for the quicksort\n\n```\ndef quicksort.py\n\nThe quicksort.\n\nIn\n\n```<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhere is the output?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.253Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2168 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1788 n_past_se=0 n_prompt_tokens_processed=15 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1788 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      30.86 ms /    15 tokens (    2.06 ms per token,   486.08 tokens per second) | n_prompt_tokens_processed=15 n_tokens_second=486.08185618458145 slot_id=0 t_prompt_processing=30.859 t_token=2.0572666666666666 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =     238.51 ms /    10 runs   (   23.85 ms per token,    41.93 tokens per second) | n_decoded=10 n_tokens_second=41.927139017814845 slot_id=0 t_token=23.8509 t_token_generation=238.509 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =     269.37 ms | slot_id=0 t_prompt_processing=30.859 t_token_generation=238.509 t_total=269.368 task_id=2169 tid="139987180924928" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1813 n_ctx=2048 n_past=1812 n_system_tokens=0 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575 truncated=false
Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=35118 status=200 tid="139986212913152" timestamp=1717252575
Jun 01 14:36:15 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:15 | 200 |    809.5268ms |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:36:20 quorra fork_ollama[20089]: time=2024-06-01T14:36:20.838Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2182 tid="139987180924928" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2183 tid="139987180924928" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50018 status=200 tid="139986196127744" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2184 tid="139987180924928" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50018 status=200 tid="139986196127744" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2185 tid="139987180924928" timestamp=1717252580
Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50020 status=200 tid="139986204520448" timestamp=1717252580
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2186 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50020 status=200 tid="139986204520448" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2187 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2188 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2189 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2190 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50034 status=200 tid="139986508738560" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2191 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50034 status=200 tid="139986508738560" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2192 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50044 status=200 tid="139986500345856" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.383Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2251 window=2048
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.383Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1825 window=2048
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.384Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n    print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for the quicksort\n\n```\ndef quicksort.py\n\nThe quicksort.\n\nIn\n\n```<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhere is the output?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for \n\nHere is a sort()<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.385Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2193 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1812 n_past_se=0 n_prompt_tokens_processed=11 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1812 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      35.12 ms /    11 tokens (    3.19 ms per token,   313.17 tokens per second) | n_prompt_tokens_processed=11 n_tokens_second=313.16725978647685 slot_id=0 t_prompt_processing=35.125 t_token=3.1931818181818183 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =     134.57 ms /     6 runs   (   22.43 ms per token,    44.59 tokens per second) | n_decoded=6 n_tokens_second=44.585466624062775 slot_id=0 t_token=22.428833333333333 t_token_generation=134.573 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =     169.70 ms | slot_id=0 t_prompt_processing=35.125 t_token_generation=134.573 t_total=169.698 task_id=2194 tid="139987180924928" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1829 n_ctx=2048 n_past=1828 n_system_tokens=0 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581 truncated=false
Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=50044 status=200 tid="139986500345856" timestamp=1717252581
Jun 01 14:36:21 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:21 | 200 |  764.649554ms |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 |      31.974µs |       127.0.0.1 | HEAD     "/"
Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 |     909.206µs |       127.0.0.1 | POST     "/api/show"
Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 |      992.28µs |       127.0.0.1 | POST     "/api/show"
Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.938Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:36:59 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2203 tid="139987180924928" timestamp=1717252619
Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1 window=2048
Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 |    1.953349ms |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.205Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2204 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2205 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50952 status=200 tid="139986483560448" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=20 window=2048
Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python generate an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0
Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2206 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=4 n_past_se=0 n_prompt_tokens_processed=14 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=4 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632
Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time     =      37.25 ms /    14 tokens (    2.66 ms per token,   375.88 tokens per second) | n_prompt_tokens_processed=14 n_tokens_second=375.87928905117326 slot_id=0 t_prompt_processing=37.246 t_token=2.6604285714285716 task_id=2207 tid="139987180924928" timestamp=1717252644
Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time =   11794.67 ms /   459 runs   (   25.70 ms per token,    38.92 tokens per second) | n_decoded=459 n_tokens_second=38.91587659241393 slot_id=0 t_token=25.696453159041397 t_token_generation=11794.672 task_id=2207 tid="139987180924928" timestamp=1717252644
Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings]           total time =   11831.92 ms | slot_id=0 t_prompt_processing=37.246 t_token_generation=11794.672 t_total=11831.918 task_id=2207 tid="139987180924928" timestamp=1717252644
Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=477 n_ctx=2048 n_past=476 n_system_tokens=0 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252644 truncated=false
Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=50952 status=200 tid="139986483560448" timestamp=1717252644
Jun 01 14:37:24 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:37:24 | 200 | 11.968728104s |       127.0.0.1 | POST     "/api/chat"
Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:304 msg="context for request finished"
Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s
Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:239 msg="timer expired, expiring to unload" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:258 msg="runner expired event received" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:274 msg="got lock to unload" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so*
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02]
Jun 01 14:42:24 quorra fork_ollama[20089]: CUDA driver version: 12.5
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2"
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 640 mb
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu"
Jun 01 14:42:24 quorra fork_ollama[20089]: releasing nvcuda library
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=server.go:990 msg="stopping llama server"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=server.go:996 msg="waiting for llama server to exit"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.354Z level=DEBUG source=server.go:1000 msg="llama server stopped"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.354Z level=DEBUG source=sched.go:279 msg="runner released" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so*
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02]
Jun 01 14:42:24 quorra fork_ollama[20089]: CUDA driver version: 12.5
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2"
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb
Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu"
Jun 01 14:42:24 quorra fork_ollama[20089]: releasing nvcuda library
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:525 msg="gpu VRAM free memory converged after 0.51 seconds"
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:283 msg="sending an unloaded event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e
Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:206 msg="ignoring unload event with no pending requests"
Jun 01 14:48:49 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:48:49 | 200 |      20.149µs |       127.0.0.1 | GET      "/api/version"
<!-- gh-comment-id:2143478040 --> @MarkWard0110 commented on GitHub (Jun 1, 2024): Log ``` Jun 01 14:32:07 quorra systemd[1]: Started Ollama Service. Jun 01 14:32:07 quorra fork_ollama[20089]: 2024/06/01 14:32:07 routes.go:1007: INFO server config env="map[OLLAMA_DEBUG:true OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST: OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS: OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:*] OLLAMA_RUNNERS_DIR: OLLAMA_TMPDIR:]" Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.365Z level=INFO source=images.go:729 msg="total blobs: 98" Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.367Z level=INFO source=images.go:736 msg="total unused blobs removed: 0" Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] [WARNING] Creating an Engine instance with the Logger and Recovery middleware already attached. Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] [WARNING] Running in "debug" mode. Switch to "release" mode in production. Jun 01 14:32:07 quorra fork_ollama[20089]: - using env: export GIN_MODE=release Jun 01 14:32:07 quorra fork_ollama[20089]: - using code: gin.SetMode(gin.ReleaseMode) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/pull --> github.com/ollama/ollama/server.(*Server).PullModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/generate --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/chat --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/embeddings --> github.com/ollama/ollama/server.(*Server).EmbeddingsHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/create --> github.com/ollama/ollama/server.(*Server).CreateModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/push --> github.com/ollama/ollama/server.(*Server).PushModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/copy --> github.com/ollama/ollama/server.(*Server).CopyModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] DELETE /api/delete --> github.com/ollama/ollama/server.(*Server).DeleteModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/show --> github.com/ollama/ollama/server.(*Server).ShowModelHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /api/blobs/:digest --> github.com/ollama/ollama/server.(*Server).CreateBlobHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD /api/blobs/:digest --> github.com/ollama/ollama/server.(*Server).HeadBlobHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET /api/ps --> github.com/ollama/ollama/server.(*Server).ProcessHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] POST /v1/chat/completions --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET / --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET /api/tags --> github.com/ollama/ollama/server.(*Server).ListModelsHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] GET /api/version --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD / --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD /api/tags --> github.com/ollama/ollama/server.(*Server).ListModelsHandler-fm (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: [GIN-debug] HEAD /api/version --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers) Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.367Z level=INFO source=routes.go:1053 msg="Listening on [::]:11434 (version 0.0.0)" Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama316030735/runners Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu file=build/linux/x86_64/cpu/bin/ollama_llama_server.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu_avx file=build/linux/x86_64/cpu_avx/bin/ollama_llama_server.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.368Z level=DEBUG source=payload.go:180 msg=extracting variant=cpu_avx2 file=build/linux/x86_64/cpu_avx2/bin/ollama_llama_server.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcublas.so.12.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcublasLt.so.12.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/libcudart.so.12.gz Jun 01 14:32:07 quorra fork_ollama[20089]: time=2024-06-01T14:32:07.369Z level=DEBUG source=payload.go:180 msg=extracting variant=cuda_v12 file=build/linux/x86_64/cuda_v12/bin/ollama_llama_server.gz Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2 Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12 Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cuda_v12 cpu cpu_avx cpu_avx2]" Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=payload.go:45 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY" Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=sched.go:90 msg="starting llm scheduler" Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs" Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so* Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.467Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]" Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.468Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02] Jun 01 14:32:09 quorra fork_ollama[20089]: CUDA driver version: 12.5 Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.550Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02 Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.550Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2" Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb Jun 01 14:32:09 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9 Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.669Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu" Jun 01 14:32:09 quorra fork_ollama[20089]: releasing nvcuda library Jun 01 14:32:09 quorra fork_ollama[20089]: time=2024-06-01T14:32:09.669Z level=INFO source=types.go:71 msg="inference compute" id=GPU-007c9d9a-8177-bd6f-7654-45652102b937 library=cuda compute=8.9 driver=12.5 name="NVIDIA GeForce RTX 4070 Ti SUPER" total="15.6 GiB" available="15.4 GiB" Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 | 59.656µs | 127.0.0.1 | HEAD "/" Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 | 1.347587ms | 127.0.0.1 | POST "/api/show" Jun 01 14:32:33 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:33 | 200 | 598.189µs | 127.0.0.1 | POST "/api/show" Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs" Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so* Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.452Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]" Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.455Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02] Jun 01 14:32:33 quorra fork_ollama[20089]: CUDA driver version: 12.5 Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.455Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02 Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.456Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2" Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb Jun 01 14:32:33 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9 Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.582Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu" Jun 01 14:32:33 quorra fork_ollama[20089]: releasing nvcuda library Jun 01 14:32:33 quorra fork_ollama[20089]: time=2024-06-01T14:32:33.582Z level=DEBUG source=gguf.go:57 msg="model = &llm.gguf{containerGGUF:(*llm.containerGGUF)(0xc0001ddbc0), kv:llm.KV{}, tensors:[]*llm.Tensor(nil), parameters:0x0}" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=sched.go:153 msg="loading first model" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=memory.go:44 msg=evaluating library=cuda gpu_count=1 available="15.4 GiB" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="15.4 GiB" memory.required.full="15.2 GiB" memory.required.partial="15.2 GiB" memory.required.kv="256.0 MiB" memory.weights.total="14.0 GiB" memory.weights.repeating="13.0 GiB" memory.weights.nonrepeating="1002.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=sched.go:565 msg="new model will fit in available VRAM in single GPU, loading" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e gpu=GPU-007c9d9a-8177-bd6f-7654-45652102b937 available=16529555456 required="15.2 GiB" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.357Z level=DEBUG source=memory.go:44 msg=evaluating library=cuda gpu_count=1 available="15.4 GiB" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="15.4 GiB" memory.required.full="15.2 GiB" memory.required.partial="15.2 GiB" memory.required.kv="256.0 MiB" memory.weights.total="14.0 GiB" memory.weights.repeating="13.0 GiB" memory.weights.nonrepeating="1002.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2 Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12 Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cpu_avx2 Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=payload.go:71 msg="availableServers : found" file=/tmp/ollama316030735/runners/cuda_v12 Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama316030735/runners/cuda_v12/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --verbose --parallel 1 --port 33377" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=DEBUG source=server.go:356 msg=subprocess environment="[PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin LD_LIBRARY_PATH=/tmp/ollama316030735/runners/cuda_v12 CUDA_VISIBLE_DEVICES=GPU-007c9d9a-8177-bd6f-7654-45652102b937]" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=sched.go:338 msg="loaded runners" count=1 Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:529 msg="waiting for llama runner to start responding" Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.358Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error" Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] build info | build=299 commit="5921b8f0" tid="139987180924928" timestamp=1717252354 Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139987180924928" timestamp=1717252354 total_threads=32 Jun 01 14:32:34 quorra fork_ollama[20380]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="33377" tid="139987180924928" timestamp=1717252354 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e (version GGUF V3 (latest)) Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 0: general.architecture str = llama Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 1: general.name str = Meta-Llama-3-8B-Instruct Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 2: llama.block_count u32 = 32 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 3: llama.context_length u32 = 8192 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 6: llama.attention.head_count u32 = 32 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 10: general.file_type u32 = 1 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 11: llama.vocab_size u32 = 128256 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - kv 21: general.quantization_version u32 = 2 Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - type f32: 65 tensors Jun 01 14:32:34 quorra fork_ollama[20089]: llama_model_loader: - type f16: 226 tensors Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_vocab: special tokens cache size = 256 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_vocab: token to piece cache size = 1.5928 MB Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: format = GGUF V3 (latest) Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: arch = llama Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: vocab type = BPE Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_vocab = 128256 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_merges = 280147 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_ctx_train = 8192 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd = 4096 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_head = 32 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_head_kv = 8 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_layer = 32 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_rot = 128 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_head_k = 128 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_head_v = 128 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_gqa = 4 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_k_gqa = 1024 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_embd_v_gqa = 1024 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_norm_eps = 0.0e+00 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_norm_rms_eps = 1.0e-05 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_clamp_kqv = 0.0e+00 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: f_logit_scale = 0.0e+00 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_ff = 14336 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_expert = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_expert_used = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: causal attn = 1 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: pooling type = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope type = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope scaling = linear Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: freq_base_train = 500000.0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: freq_scale_train = 1 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: n_yarn_orig_ctx = 8192 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: rope_finetuned = unknown Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_conv = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_inner = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_d_state = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: ssm_dt_rank = 0 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model type = 8B Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model ftype = F16 Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model params = 8.03 B Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: model size = 14.96 GiB (16.00 BPW) Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: EOS token = 128009 '<|eot_id|>' Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: LF token = 128 'Ä' Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_print_meta: EOT token = 128009 '<|eot_id|>' Jun 01 14:32:34 quorra fork_ollama[20089]: time=2024-06-01T14:32:34.610Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model" Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no Jun 01 14:32:34 quorra fork_ollama[20089]: ggml_cuda_init: found 1 CUDA devices: Jun 01 14:32:34 quorra fork_ollama[20089]: Device 0: NVIDIA GeForce RTX 4070 Ti SUPER, compute capability 8.9, VMM: yes Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: ggml ctx size = 0.30 MiB Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloading 32 repeating layers to GPU Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloading non-repeating layers to GPU Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: offloaded 33/33 layers to GPU Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: CPU buffer size = 1002.00 MiB Jun 01 14:32:34 quorra fork_ollama[20089]: llm_load_tensors: CUDA0 buffer size = 14315.02 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.112Z level=DEBUG source=server.go:578 msg="model load progress 0.30" Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.363Z level=DEBUG source=server.go:578 msg="model load progress 0.59" Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.614Z level=DEBUG source=server.go:578 msg="model load progress 0.88" Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_ctx = 2048 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_batch = 512 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: n_ubatch = 512 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: flash_attn = 0 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: freq_base = 500000.0 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: freq_scale = 1 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_kv_cache_init: CUDA0 KV buffer size = 256.00 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: CUDA_Host output buffer size = 0.50 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: CUDA0 compute buffer size = 258.50 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: CUDA_Host compute buffer size = 12.01 MiB Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: graph nodes = 1030 Jun 01 14:32:35 quorra fork_ollama[20089]: llama_new_context_with_model: graph splits = 2 Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [initialize] initializing slots | n_slots=1 tid="139987180924928" timestamp=1717252355 Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [initialize] new slot | n_ctx_slot=2048 slot_id=0 tid="139987180924928" timestamp=1717252355 Jun 01 14:32:35 quorra fork_ollama[20380]: INFO [main] model loaded | tid="139987180924928" timestamp=1717252355 Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [update_slots] all slots are idle and system prompt is empty, clear the KV cache | tid="139987180924928" timestamp=1717252355 Jun 01 14:32:35 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=0 tid="139987180924928" timestamp=1717252355 Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=INFO source=server.go:572 msg="llama runner started in 1.51 seconds" Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:351 msg="finished setting up runner" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1 window=2048 Jun 01 14:32:35 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:32:35 | 200 | 2.413840188s | 127.0.0.1 | POST "/api/chat" Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:355 msg="context for request finished" Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:32:35 quorra fork_ollama[20089]: time=2024-06-01T14:32:35.865Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.348Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1 tid="139987180924928" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2 tid="139987180924928" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=57182 status=200 tid="139986500345856" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=20 window=2048 Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:32:52 quorra fork_ollama[20089]: time=2024-06-01T14:32:52.436Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=3 tid="139987180924928" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=0 n_past_se=0 n_prompt_tokens_processed=18 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372 Jun 01 14:32:52 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=0 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252372 Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 41.27 ms / 18 tokens ( 2.29 ms per token, 436.21 tokens per second) | n_prompt_tokens_processed=18 n_tokens_second=436.2050163576881 slot_id=0 t_prompt_processing=41.265 t_token=2.2925 task_id=4 tid="139987180924928" timestamp=1717252383 Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 10481.16 ms / 408 runs ( 25.69 ms per token, 38.93 tokens per second) | n_decoded=408 n_tokens_second=38.926992711397666 slot_id=0 t_token=25.68911519607843 t_token_generation=10481.159 task_id=4 tid="139987180924928" timestamp=1717252383 Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 10522.42 ms | slot_id=0 t_prompt_processing=41.265 t_token_generation=10481.159 t_total=10522.423999999999 task_id=4 tid="139987180924928" timestamp=1717252383 Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=426 n_ctx=2048 n_past=425 n_system_tokens=0 slot_id=0 task_id=4 tid="139987180924928" timestamp=1717252383 truncated=false Jun 01 14:33:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=57182 status=200 tid="139986500345856" timestamp=1717252383 Jun 01 14:33:03 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:03 | 200 | 10.661266862s | 127.0.0.1 | POST "/api/chat" Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:33:03 quorra fork_ollama[20089]: time=2024-06-01T14:33:03.007Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.573Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=415 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=416 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44456 status=200 tid="139986491953152" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=417 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44456 status=200 tid="139986491953152" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.705Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=446 window=2048 Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.706Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[0]\n less = [x for x in arr[1:] if x <= pivot]\n greater = [x for x in arr[1:] if x > pivot]\n return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr)) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:33:12 quorra fork_ollama[20089]: time=2024-06-01T14:33:12.706Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=418 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=425 n_past_se=0 n_prompt_tokens_processed=19 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:12 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=425 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252392 Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 32.12 ms / 19 tokens ( 1.69 ms per token, 591.44 tokens per second) | n_prompt_tokens_processed=19 n_tokens_second=591.4396887159533 slot_id=0 t_prompt_processing=32.125 t_token=1.6907894736842106 task_id=419 tid="139987180924928" timestamp=1717252407 Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 14795.09 ms / 567 runs ( 26.09 ms per token, 38.32 tokens per second) | n_decoded=567 n_tokens_second=38.32353526028845 slot_id=0 t_token=26.093626102292767 t_token_generation=14795.086 task_id=419 tid="139987180924928" timestamp=1717252407 Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 14827.21 ms | slot_id=0 t_prompt_processing=32.125 t_token_generation=14795.086 t_total=14827.211 task_id=419 tid="139987180924928" timestamp=1717252407 Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1011 n_ctx=2048 n_past=1010 n_system_tokens=0 slot_id=0 task_id=419 tid="139987180924928" timestamp=1717252407 truncated=false Jun 01 14:33:27 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=44470 status=200 tid="139986483560448" timestamp=1717252407 Jun 01 14:33:27 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:27 | 200 | 14.963085151s | 127.0.0.1 | POST "/api/chat" Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:33:27 quorra fork_ollama[20089]: time=2024-06-01T14:33:27.535Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.155Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=989 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=990 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46472 status=200 tid="139986405945344" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=991 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46472 status=200 tid="139986405945344" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=992 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=46482 status=200 tid="139986397552640" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.336Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1029 window=2048 Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.337Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[0]\n less = [x for x in arr[1:] if x <= pivot]\n greater = [x for x in arr[1:] if x > pivot]\n return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr)) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:33:54 quorra fork_ollama[20089]: time=2024-06-01T14:33:54.337Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=993 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1010 n_past_se=0 n_prompt_tokens_processed=17 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:54 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1010 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252434 Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 39.14 ms / 17 tokens ( 2.30 ms per token, 434.32 tokens per second) | n_prompt_tokens_processed=17 n_tokens_second=434.31607991415865 slot_id=0 t_prompt_processing=39.142 t_token=2.3024705882352943 task_id=994 tid="139987180924928" timestamp=1717252437 Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 3139.31 ms / 121 runs ( 25.94 ms per token, 38.54 tokens per second) | n_decoded=121 n_tokens_second=38.54346476442458 slot_id=0 t_token=25.944735537190084 t_token_generation=3139.313 task_id=994 tid="139987180924928" timestamp=1717252437 Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 3178.45 ms | slot_id=0 t_prompt_processing=39.142 t_token_generation=3139.313 t_total=3178.455 task_id=994 tid="139987180924928" timestamp=1717252437 Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1148 n_ctx=2048 n_past=1147 n_system_tokens=0 slot_id=0 task_id=994 tid="139987180924928" timestamp=1717252437 truncated=false Jun 01 14:33:57 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=46482 status=200 tid="139986397552640" timestamp=1717252437 Jun 01 14:33:57 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:33:57 | 200 | 3.409412656s | 127.0.0.1 | POST "/api/chat" Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:33:57 quorra fork_ollama[20089]: time=2024-06-01T14:33:57.563Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.357Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1118 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1119 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44974 status=200 tid="139986389159936" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1120 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44974 status=200 tid="139986389159936" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1121 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44980 status=200 tid="139986380767232" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1122 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=44980 status=200 tid="139986380767232" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.624Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1168 window=2048 Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.625Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[0]\n less = [x for x in arr[1:] if x <= pivot]\n greater = [x for x in arr[1:] if x > pivot]\n return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr)) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:34:19 quorra fork_ollama[20089]: time=2024-06-01T14:34:19.625Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1123 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1147 n_past_se=0 n_prompt_tokens_processed=19 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:19 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1147 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252459 Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 42.35 ms / 19 tokens ( 2.23 ms per token, 448.67 tokens per second) | n_prompt_tokens_processed=19 n_tokens_second=448.67405010980707 slot_id=0 t_prompt_processing=42.347 t_token=2.2287894736842104 task_id=1124 tid="139987180924928" timestamp=1717252476 Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 17281.20 ms / 658 runs ( 26.26 ms per token, 38.08 tokens per second) | n_decoded=658 n_tokens_second=38.07605292293597 slot_id=0 t_token=26.263226443769 t_token_generation=17281.203 task_id=1124 tid="139987180924928" timestamp=1717252476 Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 17323.55 ms | slot_id=0 t_prompt_processing=42.347 t_token_generation=17281.203 t_total=17323.550000000003 task_id=1124 tid="139987180924928" timestamp=1717252476 Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1824 n_ctx=2048 n_past=1823 n_system_tokens=0 slot_id=0 task_id=1124 tid="139987180924928" timestamp=1717252476 truncated=false Jun 01 14:34:36 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=44986 status=200 tid="139986372374528" timestamp=1717252476 Jun 01 14:34:36 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:34:36 | 200 | 17.595507773s | 127.0.0.1 | POST "/api/chat" Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:34:36 quorra fork_ollama[20089]: time=2024-06-01T14:34:36.951Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.478Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1785 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1786 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47160 status=200 tid="139986363981824" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1787 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47160 status=200 tid="139986363981824" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1788 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47166 status=200 tid="139986355589120" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1789 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47166 status=200 tid="139986355589120" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1790 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=47180 status=200 tid="139986347196416" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.751Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1841 window=2048 Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.751Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python write an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an example implementation of QuickSort in Python:\n```\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[0]\n less = [x for x in arr[1:] if x <= pivot]\n greater = [x for x in arr[1:] if x > pivot]\n return quicksort(less) + [pivot] + quicksort(greater)\n\n# Example usage:\narr = [5, 2, 8, 3, 1, 6, 4]\nprint(quicksort(arr)) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\nHere's a brief explanation of how the algorithm works:\n\n1. If the length of the input array is 0 or 1, return the original array (since it's already sorted).\n2. Choose a pivot element from the array (in this case, we use the first element).\n3. Partition the rest of the array into two lists: `less` and `greater`. `less` contains elements that are less than or equal to the pivot, while `greater` contains elements that are greater than the pivot.\n4. Recursively apply the QuickSort algorithm to `less` and `greater`.\n5. Combine the results by concatenating the sorted `less` list, the pivot element, and the sorted `greater` list.\n\nNote that this is a \"Lomuto\" partition scheme, which is slightly faster than the standard \"Hoare\" partition scheme but has a slightly different implementation.\n\nAlso, QuickSort has an average time complexity of O(n log n), but it can be O(n^2) in the worst case if the input array is already sorted or nearly sorted. To mitigate this, you can use techniques like introducing randomness into the pivot selection or using a hybrid sorting algorithm that switches to a different algorithm (like Insertion Sort) for small inputs.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:35:03 quorra fork_ollama[20089]: time=2024-06-01T14:35:03.752Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=1791 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1823 n_past_se=0 n_prompt_tokens_processed=16 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:03 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1823 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252503 Jun 01 14:35:09 quorra fork_ollama[20380]: DEBUG [update_slots] slot context shift | n_cache_tokens=2048 n_ctx=2048 n_discard=1011 n_keep=24 n_left=2023 n_past=2047 n_system_tokens=0 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252509 Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 43.36 ms / 16 tokens ( 2.71 ms per token, 369.03 tokens per second) | n_prompt_tokens_processed=16 n_tokens_second=369.02922250155683 slot_id=0 t_prompt_processing=43.357 t_token=2.7098125 task_id=1792 tid="139987180924928" timestamp=1717252510 Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 6332.54 ms / 240 runs ( 26.39 ms per token, 37.90 tokens per second) | n_decoded=240 n_tokens_second=37.89946629655732 slot_id=0 t_token=26.385595833333333 t_token_generation=6332.543 task_id=1792 tid="139987180924928" timestamp=1717252510 Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 6375.90 ms | slot_id=0 t_prompt_processing=43.357 t_token_generation=6332.543 t_total=6375.9 task_id=1792 tid="139987180924928" timestamp=1717252510 Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1068 n_ctx=2048 n_past=1067 n_system_tokens=0 slot_id=0 task_id=1792 tid="139987180924928" timestamp=1717252510 truncated=true Jun 01 14:35:10 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=47180 status=200 tid="139986347196416" timestamp=1717252510 Jun 01 14:35:10 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:10 | 200 | 6.696495832s | 127.0.0.1 | POST "/api/chat" Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:35:10 quorra fork_ollama[20089]: time=2024-06-01T14:35:10.173Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.451Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2035 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2036 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51522 status=200 tid="139986338803712" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2037 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51522 status=200 tid="139986338803712" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2038 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51534 status=200 tid="139986330411008" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2039 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51534 status=200 tid="139986330411008" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2040 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2041 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.812Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2099 window=2048 Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.812Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1673 window=2048 Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.813Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:35:24 quorra fork_ollama[20089]: time=2024-06-01T14:35:24.813Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2042 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=4 n_past_se=0 n_prompt_tokens_processed=1667 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:24 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=4 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252524 Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 346.46 ms / 1667 tokens ( 0.21 ms per token, 4811.55 tokens per second) | n_prompt_tokens_processed=1667 n_tokens_second=4811.550029152163 slot_id=0 t_prompt_processing=346.458 t_token=0.20783323335332934 task_id=2043 tid="139987180924928" timestamp=1717252526 Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 1475.38 ms / 57 runs ( 25.88 ms per token, 38.63 tokens per second) | n_decoded=57 n_tokens_second=38.6341669728029 slot_id=0 t_token=25.883824561403507 t_token_generation=1475.378 task_id=2043 tid="139987180924928" timestamp=1717252526 Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 1821.84 ms | slot_id=0 t_prompt_processing=346.458 t_token_generation=1475.378 t_total=1821.836 task_id=2043 tid="139987180924928" timestamp=1717252526 Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1728 n_ctx=2048 n_past=1727 n_system_tokens=0 slot_id=0 task_id=2043 tid="139987180924928" timestamp=1717252526 truncated=false Jun 01 14:35:26 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=51548 status=200 tid="139986322018304" timestamp=1717252526 Jun 01 14:35:26 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:26 | 200 | 2.230278909s | 127.0.0.1 | POST "/api/chat" Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:35:26 quorra fork_ollama[20089]: time=2024-06-01T14:35:26.679Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.433Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2103 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2104 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56040 status=200 tid="139986313625600" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2105 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56040 status=200 tid="139986313625600" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2106 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56048 status=200 tid="139986305232896" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2107 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56048 status=200 tid="139986305232896" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2108 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2109 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2110 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=56054 status=200 tid="139986296840192" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2175 window=2048 Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1749 window=2048 Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:35:55 quorra fork_ollama[20089]: time=2024-06-01T14:35:55.845Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2111 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1727 n_past_se=0 n_prompt_tokens_processed=20 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:55 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1727 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252555 Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 45.08 ms / 20 tokens ( 2.25 ms per token, 443.68 tokens per second) | n_prompt_tokens_processed=20 n_tokens_second=443.675407072186 slot_id=0 t_prompt_processing=45.078 t_token=2.2539000000000002 task_id=2112 tid="139987180924928" timestamp=1717252556 Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 185.88 ms / 8 runs ( 23.23 ms per token, 43.04 tokens per second) | n_decoded=8 n_tokens_second=43.03898255845232 slot_id=0 t_token=23.23475 t_token_generation=185.878 task_id=2112 tid="139987180924928" timestamp=1717252556 Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 230.96 ms | slot_id=0 t_prompt_processing=45.078 t_token_generation=185.878 t_total=230.956 task_id=2112 tid="139987180924928" timestamp=1717252556 Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1755 n_ctx=2048 n_past=1754 n_system_tokens=0 slot_id=0 task_id=2112 tid="139987180924928" timestamp=1717252556 truncated=false Jun 01 14:35:56 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=56062 status=200 tid="139986288447488" timestamp=1717252556 Jun 01 14:35:56 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:35:56 | 200 | 646.179302ms | 127.0.0.1 | POST "/api/chat" Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:35:56 quorra fork_ollama[20089]: time=2024-06-01T14:35:56.079Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.246Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2123 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2124 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35034 status=200 tid="139986280054784" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2125 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35034 status=200 tid="139986280054784" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2126 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35048 status=200 tid="139986263269376" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2127 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35048 status=200 tid="139986263269376" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2128 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2129 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2130 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35054 status=200 tid="139986271662080" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2131 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35056 status=200 tid="139986254876672" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.700Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2195 window=2048 Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.700Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1769 window=2048 Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.701Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:36:07 quorra fork_ollama[20089]: time=2024-06-01T14:36:07.701Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2132 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1754 n_past_se=0 n_prompt_tokens_processed=13 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:07 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1754 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252567 Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 35.22 ms / 13 tokens ( 2.71 ms per token, 369.11 tokens per second) | n_prompt_tokens_processed=13 n_tokens_second=369.1084611016468 slot_id=0 t_prompt_processing=35.22 t_token=2.709230769230769 task_id=2133 tid="139987180924928" timestamp=1717252568 Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 556.28 ms / 22 runs ( 25.29 ms per token, 39.55 tokens per second) | n_decoded=22 n_tokens_second=39.54835775444425 slot_id=0 t_token=25.2855 t_token_generation=556.281 task_id=2133 tid="139987180924928" timestamp=1717252568 Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 591.50 ms | slot_id=0 t_prompt_processing=35.22 t_token_generation=556.281 t_total=591.501 task_id=2133 tid="139987180924928" timestamp=1717252568 Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1789 n_ctx=2048 n_past=1788 n_system_tokens=0 slot_id=0 task_id=2133 tid="139987180924928" timestamp=1717252568 truncated=false Jun 01 14:36:08 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=35056 status=200 tid="139986254876672" timestamp=1717252568 Jun 01 14:36:08 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:08 | 200 | 1.092247407s | 127.0.0.1 | POST "/api/chat" Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:36:08 quorra fork_ollama[20089]: time=2024-06-01T14:36:08.337Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:36:14 quorra fork_ollama[20089]: time=2024-06-01T14:36:14.715Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2158 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2159 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35068 status=200 tid="139986246483968" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2160 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35068 status=200 tid="139986246483968" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2161 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35078 status=200 tid="139986238091264" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2162 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35078 status=200 tid="139986238091264" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2163 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252574 Jun 01 14:36:14 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2164 tid="139987180924928" timestamp=1717252574 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2165 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35094 status=200 tid="139986229698560" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2166 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35102 status=200 tid="139986221305856" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2167 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=35102 status=200 tid="139986221305856" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2231 window=2048 Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1805 window=2048 Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.252Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for the quicksort\n\n```\ndef quicksort.py\n\nThe quicksort.\n\nIn\n\n```<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhere is the output?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.253Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2168 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1788 n_past_se=0 n_prompt_tokens_processed=15 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1788 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 30.86 ms / 15 tokens ( 2.06 ms per token, 486.08 tokens per second) | n_prompt_tokens_processed=15 n_tokens_second=486.08185618458145 slot_id=0 t_prompt_processing=30.859 t_token=2.0572666666666666 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 238.51 ms / 10 runs ( 23.85 ms per token, 41.93 tokens per second) | n_decoded=10 n_tokens_second=41.927139017814845 slot_id=0 t_token=23.8509 t_token_generation=238.509 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 269.37 ms | slot_id=0 t_prompt_processing=30.859 t_token_generation=238.509 t_total=269.368 task_id=2169 tid="139987180924928" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1813 n_ctx=2048 n_past=1812 n_system_tokens=0 slot_id=0 task_id=2169 tid="139987180924928" timestamp=1717252575 truncated=false Jun 01 14:36:15 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=35118 status=200 tid="139986212913152" timestamp=1717252575 Jun 01 14:36:15 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:15 | 200 | 809.5268ms | 127.0.0.1 | POST "/api/chat" Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:36:15 quorra fork_ollama[20089]: time=2024-06-01T14:36:15.523Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:36:20 quorra fork_ollama[20089]: time=2024-06-01T14:36:20.838Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2182 tid="139987180924928" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2183 tid="139987180924928" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50018 status=200 tid="139986196127744" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2184 tid="139987180924928" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50018 status=200 tid="139986196127744" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2185 tid="139987180924928" timestamp=1717252580 Jun 01 14:36:20 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50020 status=200 tid="139986204520448" timestamp=1717252580 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2186 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50020 status=200 tid="139986204520448" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2187 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2188 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2189 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50024 status=200 tid="139986517131264" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2190 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50034 status=200 tid="139986508738560" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2191 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50034 status=200 tid="139986508738560" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2192 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50044 status=200 tid="139986500345856" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.383Z level=DEBUG source=prompt.go:188 msg="required tokens longer than context window, removing first prompt" prompt=426 required=2251 window=2048 Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.383Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1825 window=2048 Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.384Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python generate 100 random numbers<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is a simple Python script that generates 100 random numbers:\n\n```\nimport random\n\n# Generate 100 random numbers\nrandom_numbers = [random.randint(1, 100) for _ in range(100)]\n\nprint(\"Random Numbers:\")\nfor num in random_numbers:\n print(num)\n```\n\nIn this script, we use the `random` module's `randint` function to generate a random integer between 1 and 100. We then use a list comprehension to create a list of 100 such numbers.\n\nWhen you run this script, it will output 100 random numbers.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide on how to potty train your furry friend:\n\n**Preparation**\n\n1. **Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty.\n2. **Get the right equipment**: You'll need poop bags, treats, and praise!\n3. **Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks.\n\n**Training**\n\n1. **Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.\n2. **Praise and reward**: When your puppy goes potty in the right spot, praise them enthusiastically and offer treats. This positive reinforcement will help your puppy associate going potty outside with good behavior.\n3. **Take them out frequently**: Puppies have small bladders and can't hold their urine for long periods. Take your puppy out every hour or two to prevent accidents.\n4. **Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.\n5. **Supervise, supervise, supervise**: Keep an eye on your puppy at all times, especially during the early stages of potty training. This will help you catch any accidents before they happen.\n\n**Additional Tips**\n\n1. **Use a bell or other signal**: Teach your puppy to ring a bell or use another signal to indicate they need to go outside.\n2. **Keep an eye on weather**: If it's raining or too cold, consider using puppy pads or indoor potty areas.\n3. **Be patient**: Potty training can take time, and accidents will happen. Stay calm and consistent, and your puppy will learn eventually.\n4. **Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.\n\n**Common Mistakes to Avoid**\n\n1. **Not supervising enough**: Puppies need constant attention during the early stages of potty training.\n2. **Not cleaning up accidents properly**: Leaving behind scents can attract your puppy back to the same spot again.\n3. **Not rewarding good behavior**: Failing to praise and reward your puppy for going potty outside can lead to confusion and setbacks.\n\n**Additional Resources**\n\n1. **Consult a veterinarian or dog trainer**: If you're having trouble with potty training, consult with a veterinarian or dog trainer for personalized advice.\n2. **Watch puppy training videos**: Watching videos on puppy training can provide valuable insights and tips from experienced trainers.\n3. **Join a puppy training group**: Joining a puppy training group or online community can connect you with other pet owners who are going through the same experience.\n\nBy following these steps, tips, and avoiding common mistakes, you'll be well on your way to potty training your puppy successfully!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease explain how AI transformers work<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nAI Transformers!\n\nTransformers are a type of neural network architecture that has revolutionized natural language processing (NLP) and machine learning in general. I'd be happy to break down how they work:\n\n**What is a Transformer?**\n\nA Transformer is a type of encoder-decoder model designed specifically for sequence-to-sequence tasks, such as machine translation, text summarization, and language modeling. It's called a \"transformer\" because it uses self-attention mechanisms to transform input sequences into output sequences.\n\n**Components of a Transformer:**\n\n1. **Encoder**: The encoder takes in an input sequence (e.g., a sentence) and outputs a continuous representation of the input sequence.\n2. **Decoder**: The decoder takes in the output from the encoder and generates the output sequence (e.g., translated text).\n3. **Self-Attention Mechanism**: This is the core innovation of Transformers. It allows the model to attend to all positions in the input sequence simultaneously, rather than sequentially.\n\n**How Self-Attention Works:**\n\n1. **Key components:\n\na transformer self-Attention\n\n* **\n\n**Encoder uses three main components: \">\n\n**What is a matrix multiplication...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHow do you potty train a puppy?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nPotty training a puppy!\n\nPotty training a puppy in the early stages of! Potty training a puppy potty training a puppy. Here's a potty training a puppy requires patience consistency positive reinforcement consistent schedule for puppies\n\nPotty training training a a puppy, and .<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUsing Python create an example of the quick sort.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHere is an efficient sorting algorithm in<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease try again<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for the quicksort\n\n```\ndef quicksort.py\n\nThe quicksort.\n\nIn\n\n```<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhere is the output?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI apologize for \n\nHere is a sort()<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.385Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2193 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=1812 n_past_se=0 n_prompt_tokens_processed=11 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=1812 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 35.12 ms / 11 tokens ( 3.19 ms per token, 313.17 tokens per second) | n_prompt_tokens_processed=11 n_tokens_second=313.16725978647685 slot_id=0 t_prompt_processing=35.125 t_token=3.1931818181818183 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 134.57 ms / 6 runs ( 22.43 ms per token, 44.59 tokens per second) | n_decoded=6 n_tokens_second=44.585466624062775 slot_id=0 t_token=22.428833333333333 t_token_generation=134.573 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 169.70 ms | slot_id=0 t_prompt_processing=35.125 t_token_generation=134.573 t_total=169.698 task_id=2194 tid="139987180924928" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=1829 n_ctx=2048 n_past=1828 n_system_tokens=0 slot_id=0 task_id=2194 tid="139987180924928" timestamp=1717252581 truncated=false Jun 01 14:36:21 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=50044 status=200 tid="139986500345856" timestamp=1717252581 Jun 01 14:36:21 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:21 | 200 | 764.649554ms | 127.0.0.1 | POST "/api/chat" Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:36:21 quorra fork_ollama[20089]: time=2024-06-01T14:36:21.602Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 | 31.974µs | 127.0.0.1 | HEAD "/" Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 | 909.206µs | 127.0.0.1 | POST "/api/show" Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 | 992.28µs | 127.0.0.1 | POST "/api/show" Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.938Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:36:59 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2203 tid="139987180924928" timestamp=1717252619 Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=1 window=2048 Jun 01 14:36:59 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:36:59 | 200 | 1.953349ms | 127.0.0.1 | POST "/api/chat" Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:36:59 quorra fork_ollama[20089]: time=2024-06-01T14:36:59.939Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.205Z level=DEBUG source=sched.go:447 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2204 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2205 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/tokenize" remote_addr="127.0.0.1" remote_port=50952 status=200 tid="139986483560448" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=prompt.go:172 msg="prompt now fits in context window" required=20 window=2048 Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=routes.go:1301 msg="chat handler" prompt="<|start_header_id|>user<|end_header_id|>\n\nUsing Python generate an example of the quick sort<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" images=0 Jun 01 14:37:12 quorra fork_ollama[20089]: time=2024-06-01T14:37:12.296Z level=DEBUG source=server.go:668 msg="setting token limit to 10x num_ctx" num_ctx=2048 num_predict=20480 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [process_single_task] slot data | n_idle_slots=1 n_processing_slots=0 task_id=2206 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [update_slots] slot progression | ga_i=0 n_past=4 n_past_se=0 n_prompt_tokens_processed=14 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:12 quorra fork_ollama[20380]: DEBUG [update_slots] kv cache rm [p0, end) | p0=4 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252632 Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings] prompt eval time = 37.25 ms / 14 tokens ( 2.66 ms per token, 375.88 tokens per second) | n_prompt_tokens_processed=14 n_tokens_second=375.87928905117326 slot_id=0 t_prompt_processing=37.246 t_token=2.6604285714285716 task_id=2207 tid="139987180924928" timestamp=1717252644 Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings] generation eval time = 11794.67 ms / 459 runs ( 25.70 ms per token, 38.92 tokens per second) | n_decoded=459 n_tokens_second=38.91587659241393 slot_id=0 t_token=25.696453159041397 t_token_generation=11794.672 task_id=2207 tid="139987180924928" timestamp=1717252644 Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [print_timings] total time = 11831.92 ms | slot_id=0 t_prompt_processing=37.246 t_token_generation=11794.672 t_total=11831.918 task_id=2207 tid="139987180924928" timestamp=1717252644 Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [update_slots] slot released | n_cache_tokens=477 n_ctx=2048 n_past=476 n_system_tokens=0 slot_id=0 task_id=2207 tid="139987180924928" timestamp=1717252644 truncated=false Jun 01 14:37:24 quorra fork_ollama[20380]: DEBUG [log_server_request] request | method="POST" params={} path="/completion" remote_addr="127.0.0.1" remote_port=50952 status=200 tid="139986483560448" timestamp=1717252644 Jun 01 14:37:24 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:37:24 | 200 | 11.968728104s | 127.0.0.1 | POST "/api/chat" Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:304 msg="context for request finished" Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:237 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e duration=5m0s Jun 01 14:37:24 quorra fork_ollama[20089]: time=2024-06-01T14:37:24.173Z level=DEBUG source=sched.go:255 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e refCount=0 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:239 msg="timer expired, expiring to unload" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:258 msg="runner expired event received" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=sched.go:274 msg="got lock to unload" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so* Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.174Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02] Jun 01 14:42:24 quorra fork_ollama[20089]: CUDA driver version: 12.5 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.177Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2" Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 640 mb Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu" Jun 01 14:42:24 quorra fork_ollama[20089]: releasing nvcuda library Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=server.go:990 msg="stopping llama server" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.302Z level=DEBUG source=server.go:996 msg="waiting for llama server to exit" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.354Z level=DEBUG source=server.go:1000 msg="llama server stopped" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.354Z level=DEBUG source=sched.go:279 msg="runner released" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:139 msg="Detecting GPUs" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcuda.so* Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.552Z level=DEBUG source=gpu.go:323 msg="gpu library search" globs="[/libcuda.so** /usr/local/cuda*/targets/*/lib/libcuda.so* /usr/lib/*-linux-gnu/nvidia/current/libcuda.so* /usr/lib/*-linux-gnu/libcuda.so* /usr/lib/wsl/lib/libcuda.so* /usr/lib/wsl/drivers/*/libcuda.so* /opt/cuda/lib*/libcuda.so* /usr/local/cuda/lib*/libcuda.so* /usr/lib*/libcuda.so* /usr/local/lib*/libcuda.so*]" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=gpu.go:356 msg="discovered GPU libraries" paths=[/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02] Jun 01 14:42:24 quorra fork_ollama[20089]: CUDA driver version: 12.5 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=gpu.go:144 msg="detected GPUs" count=1 library=/usr/lib/x86_64-linux-gnu/libcuda.so.555.42.02 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.557Z level=DEBUG source=cpu_common.go:11 msg="CPU has AVX2" Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA totalMem 15981 mb Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] CUDA freeMem 15763 mb Jun 01 14:42:24 quorra fork_ollama[20089]: [GPU-007c9d9a-8177-bd6f-7654-45652102b937] Compute Capability 8.9 Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=amd_linux.go:322 msg="amdgpu driver not detected /sys/module/amdgpu" Jun 01 14:42:24 quorra fork_ollama[20089]: releasing nvcuda library Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:525 msg="gpu VRAM free memory converged after 0.51 seconds" Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:283 msg="sending an unloaded event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-f2296999531d6120801529a45b1d103f7370c5970be939ebfc2ba5d0833e9e1e Jun 01 14:42:24 quorra fork_ollama[20089]: time=2024-06-01T14:42:24.684Z level=DEBUG source=sched.go:206 msg="ignoring unload event with no pending requests" Jun 01 14:48:49 quorra fork_ollama[20089]: [GIN] 2024/06/01 - 14:48:49 | 200 | 20.149µs | 127.0.0.1 | GET "/api/version" ```
GiteaMirror commented 2026-04-28 12:06:08 -05:00
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@MarkWard0110 commented on GitHub (Jun 1, 2024):

The latest main the chat was based upon is revert tokenize ffi (https://github.com/ollama/ollama/pull/4761)

<!-- gh-comment-id:2143481394 --> @MarkWard0110 commented on GitHub (Jun 1, 2024): The latest `main` the chat was based upon is `revert tokenize ffi `(https://github.com/ollama/ollama/pull/4761)
GiteaMirror commented 2026-04-28 12:06:08 -05:00
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@MarkWard0110 commented on GitHub (Jun 1, 2024):

I think I have found the commit that introduces the bug afd2b058b4
I don't understand why it appears in this commit. I have provided the range of commits I tested to locate the commit.


BAD
8a8e7afa968f9c241a6bf85e2e9f711e8be41c7c

BAD
c79f8c9c3934ee8c9507ab89d4ec31c16bbd0fd8

BAD
6adca97f37b815baeb9eddc37e1e383ec78e04ca

BAD
4cc3be30358efcbd463ec30c30998dacdb0cfb5c

BAD
db2ffa79f10ebcb6cd702bfceb3533a97e892409

BAD
afd2b058b4ee36230ab2a06927bdc0ff41b1e7ae

works
fd5971be0bb11d1b5903fc6778c329b4fd93d569

works
38255d2af15932150606e19bea8200b386cfd36d

BUILD FAIL
353f83a9c788410f36a670e6750bb4162fb8f440

WORKS
d355d2020fcfc54c375eb697b7873742c3851881
<!-- gh-comment-id:2143535084 --> @MarkWard0110 commented on GitHub (Jun 1, 2024): I think I have found the commit that introduces the bug https://github.com/ollama/ollama/commit/afd2b058b4ee36230ab2a06927bdc0ff41b1e7ae I don't understand why it appears in this commit. I have provided the range of commits I tested to locate the commit. ``` BAD 8a8e7afa968f9c241a6bf85e2e9f711e8be41c7c BAD c79f8c9c3934ee8c9507ab89d4ec31c16bbd0fd8 BAD 6adca97f37b815baeb9eddc37e1e383ec78e04ca BAD 4cc3be30358efcbd463ec30c30998dacdb0cfb5c BAD db2ffa79f10ebcb6cd702bfceb3533a97e892409 BAD afd2b058b4ee36230ab2a06927bdc0ff41b1e7ae works fd5971be0bb11d1b5903fc6778c329b4fd93d569 works 38255d2af15932150606e19bea8200b386cfd36d BUILD FAIL 353f83a9c788410f36a670e6750bb4162fb8f440 WORKS d355d2020fcfc54c375eb697b7873742c3851881 ```
GiteaMirror commented 2026-04-28 12:06:09 -05:00
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@MarkWard0110 commented on GitHub (Jun 1, 2024):

I am able to reproduce the bug every time I run. It takes a few prompts to get there. I try to switch topics in a session like ask code generation and AI questions and generate instructions.

<!-- gh-comment-id:2143535555 --> @MarkWard0110 commented on GitHub (Jun 1, 2024): I am able to reproduce the bug every time I run. It takes a few prompts to get there. I try to switch topics in a session like ask code generation and AI questions and generate instructions.
GiteaMirror commented 2026-04-28 12:06:10 -05:00
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@MarkWard0110 commented on GitHub (Jun 9, 2024):

I continue to get corruption in model generation and a leaking state between sessions. I have built up to commit cddc63381c, but it still has the issue.

It appears to take longer for the issue to show because I have been working with this version all day and have only now started to see the issue. I think this is because I have not had pauses long enough for Ollama to unload models which helps reset the issue.

edit
I found out why I was not seeing the issue all day. Two libraries were using different settings, which means Ollama was switching the model out.
The issue is found by repeatedly running the same model with the same options until the chat-generated responses are abnormal.

<!-- gh-comment-id:2156685152 --> @MarkWard0110 commented on GitHub (Jun 9, 2024): I continue to get corruption in model generation and a leaking state between sessions. I have built up to commit https://github.com/ollama/ollama/commit/cddc63381cffcde5ab50877e831b7595cc6a6d6e, but it still has the issue. It appears to take longer for the issue to show because I have been working with this version all day and have only now started to see the issue. I think this is because I have not had pauses long enough for Ollama to unload models which helps reset the issue. *edit* I found out why I was not seeing the issue all day. Two libraries were using different settings, which means Ollama was switching the model out. The issue is found by repeatedly running the same model with the same options until the chat-generated responses are abnormal.
GiteaMirror commented 2026-04-28 12:06:11 -05:00
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@MarkWard0110 commented on GitHub (Jun 9, 2024):

This is a weird issue to track down. Several hours into trying to find the issue, I struggle to reproduce it. When it does happen, the model must be reloaded to fix the problem. If I could easily reproduce it, I would try other models and check if I see it with them.

I am hand-crafting the chat prompts so that I have reproducible prompt exchanges.
For example. The prompt How do you potty train a puppy?

When the issue was happening, it generated responses like the following. Note that the responses are influenced by other sessions. this chat only contains one message. role: user

Potty training a new puppy
Puppy to the number in the user.
Potty_train  input number by 2.
Potty_Agent: Subtract 1 from each input number.
Divide_by_0. Divide each input number by  and rounder_Agent: Return the same as they are you want to learn how do your agent you want to continue', 'Agent', 'Multiplier_Agent', 'Subtracter_Agent', 'Divider_Agent', 'Number_Agent']

Potty training, natural language models.

Potty training a puppy training a great goal is asking how to get closer

When the server is working

Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend:

**1. Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty. Make sure it's easily accessible and preferably close to your back door.

**2. Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks. Take your puppy to the designated potty area immediately after meals, naps, and playtime.

**3. Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area.

**4. Use positive reinforcement**: When your puppy goes potty in the designated area, praise them and reward them with treats, praise, and affection. This positive reinforcement will help your puppy associate going potty outside with good behavior.

**5. Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again.

**6. Supervise and intervene**: Keep an eye on your puppy at all times, especially during the early stages of potty training. If you see your puppy starting to go potty in the house, quickly interrupt them by saying "outside" or "potty" and take them to the designated area.

**7. Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision.

**8. Be patient**: Potty training can be a slow process, especially for young puppies. Accidents will happen, but don't scold or punish your puppy. Instead, calmly and patiently guide them back to the designated potty area.

**9. Use potty pads or newspaper (optional)**: If you're unable to take your puppy outside frequently, consider using potty pads or newspaper in a designated area indoors. This can help with housetraining and make cleanup easier.

**10. Reward good behavior**: Continue to reward your puppy for good potty behavior, even as they become more reliable. This will help reinforce good habits and encourage them to continue going potty outside where they belong.

Remember, every puppy is different, and potty training may take longer for some than others. Stay patient, consistent, and positive, and you'll be well on your way to raising a well-trained pup!
<!-- gh-comment-id:2156806129 --> @MarkWard0110 commented on GitHub (Jun 9, 2024): This is a weird issue to track down. Several hours into trying to find the issue, I struggle to reproduce it. When it does happen, the model must be reloaded to fix the problem. If I could easily reproduce it, I would try other models and check if I see it with them. I am hand-crafting the chat prompts so that I have reproducible prompt exchanges. For example. The prompt `How do you potty train a puppy?` When the issue was happening, it generated responses like the following. Note that the responses are influenced by other sessions. this chat only contains one message. role: user ``` Potty training a new puppy Puppy to the number in the user. Potty_train input number by 2. Potty_Agent: Subtract 1 from each input number. Divide_by_0. Divide each input number by and rounder_Agent: Return the same as they are you want to learn how do your agent you want to continue', 'Agent', 'Multiplier_Agent', 'Subtracter_Agent', 'Divider_Agent', 'Number_Agent'] ``` ``` Potty training, natural language models. ``` ``` Potty training a puppy training a great goal is asking how to get closer ``` When the server is working ``` Potty training a puppy requires patience, consistency, and positive reinforcement. Here's a step-by-step guide to help you potty train your furry friend: **1. Choose a designated potty area**: Pick a specific spot outside, such as a grassy patch or a puppy pad, where you want your puppy to go potty. Make sure it's easily accessible and preferably close to your back door. **2. Establish a routine**: Puppies thrive on routine, so create a schedule for feeding, playtime, and potty breaks. Take your puppy to the designated potty area immediately after meals, naps, and playtime. **3. Watch for signs**: Puppies will often sniff and circle before they go potty. If you see your puppy exhibiting these behaviors, quickly take them to the designated potty area. **4. Use positive reinforcement**: When your puppy goes potty in the designated area, praise them and reward them with treats, praise, and affection. This positive reinforcement will help your puppy associate going potty outside with good behavior. **5. Clean up accidents**: If your puppy has an accident in the house, clean up the area thoroughly with a pet-safe cleaner to remove any remaining scent that might attract your puppy to the same spot again. **6. Supervise and intervene**: Keep an eye on your puppy at all times, especially during the early stages of potty training. If you see your puppy starting to go potty in the house, quickly interrupt them by saying "outside" or "potty" and take them to the designated area. **7. Gradually increase freedom**: As your puppy becomes more reliable with potty training, you can gradually give them more freedom in the house, such as access to more rooms or longer periods of time without supervision. **8. Be patient**: Potty training can be a slow process, especially for young puppies. Accidents will happen, but don't scold or punish your puppy. Instead, calmly and patiently guide them back to the designated potty area. **9. Use potty pads or newspaper (optional)**: If you're unable to take your puppy outside frequently, consider using potty pads or newspaper in a designated area indoors. This can help with housetraining and make cleanup easier. **10. Reward good behavior**: Continue to reward your puppy for good potty behavior, even as they become more reliable. This will help reinforce good habits and encourage them to continue going potty outside where they belong. Remember, every puppy is different, and potty training may take longer for some than others. Stay patient, consistent, and positive, and you'll be well on your way to raising a well-trained pup! ```
GiteaMirror commented 2026-04-28 12:06:12 -05:00
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@MarkWard0110 commented on GitHub (Jun 11, 2024):

I have installed Ollama v0.1.42 to investigate if I find the issue with the release binaries vs my own builds. Initial testing has not produced the issue.

<!-- gh-comment-id:2160899719 --> @MarkWard0110 commented on GitHub (Jun 11, 2024): I have installed Ollama v0.1.42 to investigate if I find the issue with the release binaries vs my own builds. Initial testing has not produced the issue.
GiteaMirror commented 2026-04-28 12:06:12 -05:00
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@MarkWard0110 commented on GitHub (Jun 25, 2024):

I have not been able to reproduce with release versions of Ollama

<!-- gh-comment-id:2189010822 --> @MarkWard0110 commented on GitHub (Jun 25, 2024): I have not been able to reproduce with release versions of Ollama
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