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hoyyeva/anthropic-local-image-path
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Reference: github-starred/ollama#65865
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Originally created by @bopm on GitHub (Aug 1, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/6129
What is the issue?
Running on MacStudio m1, fresh pull with
ollama run llama3.1.Using next as a test:
Expected results are 36/unknown location for river.
Actual results:
So no correct calculations after first three runs.
Feeding same model to llama.cpp (llama-cli --version
version: 3497 (ed9d2854)
built with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.4.0):
OS
macOS
GPU
Apple
CPU
Apple
Ollama version
0.3.2
@bopm commented on GitHub (Aug 2, 2024):
ollama run llama3.1:8b-instruct-q4_K_Malso seems to be broken and hallucinating answers.When the same model is fed into llama.cpp
it gives correct responses.
@igorschlum commented on GitHub (Aug 2, 2024):
with llama3.1:70b-instruct-q8_0 with Ollama, it works well:
Let's break down the events:
You start with 10 apples.
You find 3 gold coins in a river near a big city (the city isn't relevant to this problem, so let's ignore it).
You lose 4 apples but gain a gold coin, leaving you with:
Apples: 10 - 4 = 6
Coins: 3 + 1 = 4
Three birds run into your path and drop 6 apples each. That's a total of:
Apples: 6 + (3 x 6) = 24
You play an online game, win 6 gold coins, but have to share them with two teammates. Since you'll get one-third of the coins:
Coins won: 6 / 3 = 2
New total coins: 4 + 2 = 6
Now that you have a total of 24 apples and 6 gold coins, let's calculate how many more apples you can buy:
You have 6 gold coins to spend.
Each apple costs 0.5 coins.
Number of new apples bought: 6 / 0.5 = 12
Total apples now: Old + New = 24 + 12 = 36
As for the river, we don't know its name or exact location beyond being near a big city, but it seems to be some sort of mythical place where gold coins can be found...
@SirFalk commented on GitHub (Aug 3, 2024):
I‘ve also found Llama3.1 to just do whatever it wants. When I input a Text ~10000 characters, no matter what instruction, it just creates a summary. Even on 70B. Even in json mode (lol). I've just gone back to using Llama3. Though I‘m not sure it‘s a bug, maybe it‘s just bad.
@Manouchehri commented on GitHub (Aug 3, 2024):
I also can't seem to import Llama 3.1 models that I downloaded from Hugging Face, not sure if it's related?
llama-3.1-8b-instruct:@Technocality commented on GitHub (Aug 4, 2024):
This might be related to this: #6060
Also less related but might be something: #6071
@Tom-Neverwinter commented on GitHub (Aug 6, 2024):
I often find this model repeats regardless of the settings. I assume its not been fixed especially as the model states july a week before additional tokenizer fixes were implemented
@dhiltgen commented on GitHub (Aug 9, 2024):
We've fixed a few bugs recently which should hopefully address this. Please make sure to upgrade to the latest version of ollama (v0.3.4) and re-pull llama3.1.
@igorschlum commented on GitHub (Aug 9, 2024):
@Tom-Neverwinter All Llama 3.1 models were updated 2 to 3 days ago, and the Llama 3.1:8b family is being re-uploaded today.
@bopm Please let us know if the new Llama 3.1 models uploaded today resolve the issue you're describing.
@bopm commented on GitHub (Aug 12, 2024):
@igorschlum it was quite efficient for a few days at the end of last week. After re-pulling
llama3.1today it's hallucinating all over again. Not a single right answer.ollama version is 0.3.4@bopm commented on GitHub (Aug 12, 2024):
At the same time
llama3.1:8b-instruct-q4_K_Mis gives more correct answers, but not still gives few incorrect at the start.@igorschlum commented on GitHub (Aug 12, 2024):
@bopm if you ask the LLM to solve it carfully and focus on math results, both Ollama 3 and Ollama 3.1 succeed in the task.
the way the prompt is written is important.
The new version of llama3.1 is not more broken than Llama 3.
Tell us if you have other doubts. On my side, I think that this issue can be closed as resolve since the main point was the update of the llama3.1 model.
@bopm commented on GitHub (Aug 12, 2024):
It's not working even with such prompt:
Now if I feed same model to llama.cpp it hallucinating before specific prompt, but not after.
And what is important for me, is that last Friday before I repulled model today, it was working flawlessly without any specific prompts for me. Unfortunately, I do not have any logs of that.
@YueChenkkk commented on GitHub (Sep 4, 2024):
I have the same issue, did you solve it?
@pdevine commented on GitHub (Sep 13, 2024):
@Manouchehri @YueChenkkk The
invalid wire-format dataissue should be fixed with the0.3.8. The problem is that the HF model included both the safetensors and pytorch weights and Ollama was getting confused (the pytorch weights are in theoriginal/subdir).ollama createwill only pass in the safetensors files, so it should be faster as well too.@pdevine commented on GitHub (Feb 12, 2025):
I think we can safely close this.
@pdevine commented on GitHub (Feb 12, 2025):
Also, I would definitely recommend checking out some of the new reasoning models.