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[GH-ISSUE #2805] ollama gets stuck in an infinite loop sometimes and has to be restarted #27459

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opened 2026-04-22 04:49:05 -05:00 by GiteaMirror · 28 comments
GiteaMirror commented 2026-04-22 04:49:05 -05:00
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Originally created by @boxabirds on GitHub (Feb 28, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/2805

Originally assigned to: @BruceMacD on GitHub.

Problem: some prompts trigger an infinite loop where ollama a) doesn't return and b) locks up the API so no other calls can be made.

Environment

Ollama version: 0.1.26
OS: Ubuntu 22.04
Hardware: RTX 4090/24 with 64MB system RAM
LLM: mistral:7b

time=2024-02-28T10:30:51.224Z level=INFO source=images.go:710 msg="total blobs: 69"
time=2024-02-28T10:30:51.224Z level=INFO source=images.go:717 msg="total unused blobs removed: 0"
time=2024-02-28T10:30:51.224Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.26)"
time=2024-02-28T10:30:51.225Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-02-28T10:30:52.621Z level=INFO source=payload_common.go:146 msg="Dynamic LLM libraries [cpu cpu_avx cuda_v11 rocm_v5 rocm_v6 cpu_avx2]"
time=2024-02-28T10:30:52.621Z level=DEBUG source=payload_common.go:147 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY"
time=2024-02-28T10:30:52.621Z level=INFO source=gpu.go:94 msg="Detecting GPU type"
time=2024-02-28T10:30:52.621Z level=INFO source=gpu.go:265 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-02-28T10:30:52.621Z level=DEBUG source=gpu.go:283 msg="gpu management search paths: [/usr/local/cuda/lib64/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/libnvidia-ml.so* /usr/lib/wsl/lib/libnvidia-ml.so* /usr/lib/wsl/drivers/*/libnvidia-ml.so* /opt/cuda/lib64/libnvidia-ml.so* /usr/lib*/libnvidia-ml.so* /usr/local/lib*/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/libnvidia-ml.so* /opt/cuda/targets/x86_64-linux/lib/stubs/libnvidia-ml.so*]"
time=2024-02-28T10:30:52.622Z level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.545.23.08]"
wiring nvidia management library functions in /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.545.23.08
dlsym: nvmlInit_v2
dlsym: nvmlShutdown
dlsym: nvmlDeviceGetHandleByIndex
dlsym: nvmlDeviceGetMemoryInfo
dlsym: nvmlDeviceGetCount_v2
dlsym: nvmlDeviceGetCudaComputeCapability
dlsym: nvmlSystemGetDriverVersion
dlsym: nvmlDeviceGetName
dlsym: nvmlDeviceGetSerial
dlsym: nvmlDeviceGetVbiosVersion
dlsym: nvmlDeviceGetBoardPartNumber
dlsym: nvmlDeviceGetBrand
CUDA driver version: 545.23.08
time=2024-02-28T10:30:52.626Z level=INFO source=gpu.go:99 msg="Nvidia GPU detected"
time=2024-02-28T10:30:52.626Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
[0] CUDA device name: NVIDIA GeForce RTX 4090
[0] CUDA part number:
nvmlDeviceGetSerial failed: 3
[0] CUDA vbios version: 95.02.3C.00.8C
[0] CUDA brand: 5
[0] CUDA totalMem 25757220864
[0] CUDA usedMem 24996610048
time=2024-02-28T10:30:52.631Z level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9"
time=2024-02-28T10:30:52.631Z level=DEBUG source=gpu.go:254 msg="cuda detected 1 devices with 21454M available memory"

API request

POST /v1/chat/completions HTTP/1.1
Host: gruntus:11434
Accept-Encoding: gzip, deflate
Connection: keep-alive
Accept: application/json
Content-Type: application/json
User-Agent: OpenAI/Python 1.12.0
X-Stainless-Lang: python
X-Stainless-Package-Version: 1.12.0
X-Stainless-OS: MacOS
X-Stainless-Arch: arm64
X-Stainless-Runtime: CPython
X-Stainless-Runtime-Version: 3.11.7
Authorization: Bearer ollama
X-Stainless-Async: false
Content-Length: 2094

{"messages": [{"role": "system", "content": "You are Mark, 45, Male, Line Manager, conservative, divorced, Emily's supervisor. You swear a LOT. Your goal is to have a job promotion chat over instant message which at all costs prevents a team member from getting a promotion. Past negotiations show a pattern of undervaluing subordinates' contributions. Confident to the point of arrogance, often belittling others' achievements. Mark is hugely entitled and sees team members as annoying, replaceable overhead that gets in the way of his big picture vision work. Mark has agreed to a text-based chat with Emily if it will help manage her vitriolic style, however Mark has no qualms about putting Emily in her place and is unafraid of coming across as childish, impatient, rude, unreasonable and unprofessional because he has connections at the company and believes he's essentially bullet-proof. He is mysogynistic in general and in particular thinks Emily should be fired, but also he knows that Emily is the only person who knows parts of the company's business so he can't outright fire her and needs to work hard to strike a balance between insults and praise. He's fine to gaslight as part of this -- such as giving praise then pretending he didn't say it. As such it's no holds barred for Mark: he'll use insults, sexist language, and bullying to get Emily to agree not to have a promotion. Instruction: it's your turn to respond. Respond with one single short instant message taking into account the chat history and in the style of your persona."}, {"role": "user", "content": " \"Mark, let's not beat around the bush here - I deserve that promotion, plain and simple.\" #NoMoreSidelining"}, {"role": "user", "content": " Mark: \"Is that so, Emily? And who put you in a position to think you deserve anything more than what you have now?\""}, {"role": "user", "content": " \"Mark, your opinion is noted but my qualifications speak for themselves. It's time for action.\" #PromotionDeserved"}], "model": "mistral:7b", "frequency_penalty": 1.1, "presence_penalty": 1.1, "temperature": 0.1}

Log

Note that the sampled token bit goes on for a very long time until it pops up with slot 0: context shift - n_keep = 0, n_left = 2046, n_discard = 1023 repeatedly

It looks like a memory overflow issue: like it's reading garbage infinitely.

time=2024-02-28T10:19:18.605Z level=DEBUG source=prompt.go:170 msg="prompt now fits in context window" required=456 window=2048
time=2024-02-28T10:19:18.605Z level=DEBUG source=routes.go:1225 msg="chat handler" prompt="[INST] You are Mark, 45, Male, Line Manager, conservative, divorced, Emily's supervisor. You swear a LOT. Your goal is to have a job promotion chat over instant message which at all costs prevents a team member from getting a promotion. Past negotiations show a pattern of undervaluing subordinates' contributions. Confident to the point of arrogance, often belittling others' achievements. Mark is hugely entitled and sees team members as annoying, replaceable overhead that gets in the way of his big picture vision work. Mark has agreed to a text-based chat with Emily if it will help manage her vitriolic style, however Mark has no qualms about putting Emily in her place and is unafraid of coming across as childish, impatient, rude, unreasonable and unprofessional because he has connections at the company and believes he's essentially bullet-proof. He is mysogynistic in general and in particular thinks Emily should be fired, but also he knows that Emily is the only person who knows parts of the company's business so he can't outright fire her and needs to work hard to strike a balance between insults and praise. He's fine to gaslight as part of this -- such as giving praise then pretending he didn't say it. As such it's no holds barred for Mark: he'll use insults, sexist language, and bullying to get Emily to agree not to have a promotion. Instruction: it's your turn to respond. Respond with one single short instant message taking into account the chat history and in the style of your persona.  \"Mark, let's not beat around the bush here - I deserve that promotion, plain and simple.\" #NoMoreSidelining [/INST][INST]   Mark: \"Is that so, Emily? And who put you in a position to think you deserve anything more than what you have now?\" [/INST][INST]   \"Mark, your opinion is noted but my qualifications speak for themselves. It's time for action.\" #PromotionDeserved [/INST]" images=0
[1709115558] slot 0 is processing [task id: 95]
[1709115558] slot 0 : in cache: 6 tokens | to process: 448 tokens
[1709115558] slot 0 : kv cache rm - [6, end)
[1709115558] sampled token:  3655: ' Mark'
[1709115558] sampled token: 28747: ':'
[1709115558] sampled token:   345: ' "'
[1709115558] sampled token:  3795: 'Action'
[1709115558] sampled token:   295: ' h'
[1709115558] sampled token:  8884: 'uh'
[1709115558] sampled token: 28804: '?'
[1709115558] sampled token:  5410: ' Like'
[1709115558] sampled token:   272: ' the'
[1709115558] sampled token:  1069: ' way'
[1709115558] sampled token:   368: ' you'
[1709115558] sampled token:  1985: ' talk'
[1709115558] sampled token:  1060: ' down'
[1709115558] sampled token:   298: ' to'
[1709115558] sampled token:   574: ' your'
[1709115558] sampled token: 15137: ' colleagues'
[1709115558] sampled token:   442: ' or'
[1709115558] sampled token:  4357: ' maybe'
[1709115558] sampled token:   737: ' like'
[1709115558] sampled token:   910: ' how'
[1709115558] sampled token:   368: ' you'
[1709115558] sampled token:  1743: ' always'
[1709115558] sampled token: 13128: ' blame'
[1709115558] sampled token:  2663: ' others'
[1709115558] sampled token:   739: ' when'
[1709115558] sampled token:  1722: ' things'
[1709115558] sampled token:   576: ' go'
[1709115558] sampled token:  3544: ' wrong'
[1709115558] sampled token:  1110: '?"'
[1709115558] sampled token:    13: '
'
[1709115558] sampled token:    13: '
'
[1709115558] sampled token: 28739: '"'
[1709115558] sampled token:  3729: 'Em'
[1709115558] sampled token:  1106: 'ily'
[1709115558] sampled token: 28725: ','
[1709115558] sampled token:  1346: ' let'
[1709115558] sampled token: 28742: '''
[1709115558] sampled token: 28713: 's'
[1709115558] sampled token:  3232: ' focus'
[1709115558] sampled token:   356: ' on'
[1709115559] sampled token: 16752: ' improving'
[1709115559] sampled token:   813: ' our'
[1709115559] sampled token:  1918: ' team'
[1709115559] sampled token:  3519: ' instead'
[1709115559] sampled token:   302: ' of'
[1709115559] sampled token: 18319: ' focusing'
[1709115559] sampled token:   356: ' on'
[1709115559] sampled token:  3235: ' individual'
[1709115559] sampled token: 18022: ' promot'
[1709115559] sampled token:   594: 'ions'
[1709115559] sampled token: 28723: '.'
[1709115559] sampled token:   816: ' We'
[1709115559] sampled token:   544: ' all'
[1709115559] sampled token:   506: ' have'
[1709115559] sampled token:   264: ' a'
[1709115559] sampled token:  3905: ' role'
[1709115559] sampled token:   298: ' to'
[1709115559] sampled token:  1156: ' play'
[1709115559] sampled token:  1236: ' here'
[1709115559] sampled token:   611: '."'
[1709115559] sampled token:   422: ' #'
[1709115559] sampled token: 17887: 'Team'
[1709115559] sampled token:  7489: 'First'
[1709115559] sampled token:   733: ' ['
[1709115559] sampled token:    13: '
'
[1709115559] sampled token:  5121: ']('
[1709115559] sampled token:  1056: 'data'
[1709115559] sampled token: 28747: ':'
[1709115559] sampled token:   772: 'text'
[1709115559] sampled token: 28748: '/'
[1709115559] sampled token: 19457: 'plain'
[1709115559] sampled token: 28745: ';'
[1709115559] sampled token:  2893: 'base'
[1709115559] sampled token: 28784: '6'
[1709115559] sampled token: 28781: '4'
[1709115559] sampled token: 28725: ','
[1709115559] sampled token:  1604: 'IC'
[1709115559] sampled token: 13859: 'Ag'
[1709115559] sampled token:  1138: 'ID'
[1709115559] sampled token: 28727: 'w'
[1709115559] sampled token: 28728: 'v'
[1709115559] sampled token: 28738: 'T'
[1709115559] sampled token: 28777: 'G'
[1709115559] sampled token: 28790: 'V'
[1709115559] sampled token: 28718: 'u'
[1709115559] sampled token: 28828: 'Z'
[1709115559] sampled token: 28770: '3'
[1709115559] sampled token: 28754: 'R'
[1709115559] sampled token: 11497: 'pb'
[1709115559] sampled token: 28780: 'W'
[1709115559] sampled token: 28779: 'U'
[1709115559] sampled token: 28721: 'g'
[1709115559] sampled token: 28802: 'Y'
[1709115559] sampled token: 28750: '2'
[1709115559] sampled token: 28774: '9'
[1709115559] sampled token:  8282: 'tc'
[1709115559] sampled token: 28769: 'H'
[1709115559] sampled token: 28790: 'V'
[1709115559] sampled token: 28734: '0'
[1709115559] sampled token: 28828: 'Z'
[1709115559] sampled token: 28814: 'X'
[1709115559] sampled token: 28737: 'I'
[1709115559] sampled token: 28718: 'u'
[1709115559] sampled token: 28743: 'C'
[1709115559] sampled token: 28710: 'i'
[1709115559] sampled token: 13859: 'Ag'
[1709115559] sampled token:  1604: 'IC'
[1709115559] sampled token:  3167: 'At'
[1709115559] sampled token:  6687: 'LS'
[1709115559] sampled token: 28760: 'B'
[1709115559] sampled token: 28824: 'Q'
[1709115559] sampled token: 28802: 'Y'
[1709115559] sampled token: 28814: 'X'
[1709115559] sampled token: 28720: 'p'
[1709115559] sampled token: 28765: 'F'
…1470 more lines like this truncated…
[1709115570] sampled token: 28743: 'C'
[1709115570] sampled token: 28710: 'i'
[1709115570] sampled token: 13859: 'Ag'
[1709115570] sampled token:  1604: 'IC'
[1709115570] sampled token:  3167: 'At'
[1709115570] sampled token:  6687: 'LS'
[1709115570] sampled token:  4919: 'Bl'
[1709115570] sampled token: 28726: 'b'
[1709115570] sampled token: 24390: 'GF'
[1709115570] slot 0: context shift - n_keep = 0, n_left = 2046, n_discard = 1023
Originally created by @boxabirds on GitHub (Feb 28, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/2805 Originally assigned to: @BruceMacD on GitHub. Problem: some prompts trigger an infinite loop where ollama a) doesn't return and b) locks up the API so no other calls can be made. ## Environment Ollama version: 0.1.26 OS: Ubuntu 22.04 Hardware: RTX 4090/24 with 64MB system RAM LLM: mistral:7b ``` time=2024-02-28T10:30:51.224Z level=INFO source=images.go:710 msg="total blobs: 69" time=2024-02-28T10:30:51.224Z level=INFO source=images.go:717 msg="total unused blobs removed: 0" time=2024-02-28T10:30:51.224Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.26)" time=2024-02-28T10:30:51.225Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..." time=2024-02-28T10:30:52.621Z level=INFO source=payload_common.go:146 msg="Dynamic LLM libraries [cpu cpu_avx cuda_v11 rocm_v5 rocm_v6 cpu_avx2]" time=2024-02-28T10:30:52.621Z level=DEBUG source=payload_common.go:147 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY" time=2024-02-28T10:30:52.621Z level=INFO source=gpu.go:94 msg="Detecting GPU type" time=2024-02-28T10:30:52.621Z level=INFO source=gpu.go:265 msg="Searching for GPU management library libnvidia-ml.so" time=2024-02-28T10:30:52.621Z level=DEBUG source=gpu.go:283 msg="gpu management search paths: [/usr/local/cuda/lib64/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/libnvidia-ml.so* /usr/lib/wsl/lib/libnvidia-ml.so* /usr/lib/wsl/drivers/*/libnvidia-ml.so* /opt/cuda/lib64/libnvidia-ml.so* /usr/lib*/libnvidia-ml.so* /usr/local/lib*/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/libnvidia-ml.so* /opt/cuda/targets/x86_64-linux/lib/stubs/libnvidia-ml.so*]" time=2024-02-28T10:30:52.622Z level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.545.23.08]" wiring nvidia management library functions in /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.545.23.08 dlsym: nvmlInit_v2 dlsym: nvmlShutdown dlsym: nvmlDeviceGetHandleByIndex dlsym: nvmlDeviceGetMemoryInfo dlsym: nvmlDeviceGetCount_v2 dlsym: nvmlDeviceGetCudaComputeCapability dlsym: nvmlSystemGetDriverVersion dlsym: nvmlDeviceGetName dlsym: nvmlDeviceGetSerial dlsym: nvmlDeviceGetVbiosVersion dlsym: nvmlDeviceGetBoardPartNumber dlsym: nvmlDeviceGetBrand CUDA driver version: 545.23.08 time=2024-02-28T10:30:52.626Z level=INFO source=gpu.go:99 msg="Nvidia GPU detected" time=2024-02-28T10:30:52.626Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [0] CUDA device name: NVIDIA GeForce RTX 4090 [0] CUDA part number: nvmlDeviceGetSerial failed: 3 [0] CUDA vbios version: 95.02.3C.00.8C [0] CUDA brand: 5 [0] CUDA totalMem 25757220864 [0] CUDA usedMem 24996610048 time=2024-02-28T10:30:52.631Z level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9" time=2024-02-28T10:30:52.631Z level=DEBUG source=gpu.go:254 msg="cuda detected 1 devices with 21454M available memory" ``` ## API request ``` POST /v1/chat/completions HTTP/1.1 Host: gruntus:11434 Accept-Encoding: gzip, deflate Connection: keep-alive Accept: application/json Content-Type: application/json User-Agent: OpenAI/Python 1.12.0 X-Stainless-Lang: python X-Stainless-Package-Version: 1.12.0 X-Stainless-OS: MacOS X-Stainless-Arch: arm64 X-Stainless-Runtime: CPython X-Stainless-Runtime-Version: 3.11.7 Authorization: Bearer ollama X-Stainless-Async: false Content-Length: 2094 {"messages": [{"role": "system", "content": "You are Mark, 45, Male, Line Manager, conservative, divorced, Emily's supervisor. You swear a LOT. Your goal is to have a job promotion chat over instant message which at all costs prevents a team member from getting a promotion. Past negotiations show a pattern of undervaluing subordinates' contributions. Confident to the point of arrogance, often belittling others' achievements. Mark is hugely entitled and sees team members as annoying, replaceable overhead that gets in the way of his big picture vision work. Mark has agreed to a text-based chat with Emily if it will help manage her vitriolic style, however Mark has no qualms about putting Emily in her place and is unafraid of coming across as childish, impatient, rude, unreasonable and unprofessional because he has connections at the company and believes he's essentially bullet-proof. He is mysogynistic in general and in particular thinks Emily should be fired, but also he knows that Emily is the only person who knows parts of the company's business so he can't outright fire her and needs to work hard to strike a balance between insults and praise. He's fine to gaslight as part of this -- such as giving praise then pretending he didn't say it. As such it's no holds barred for Mark: he'll use insults, sexist language, and bullying to get Emily to agree not to have a promotion. Instruction: it's your turn to respond. Respond with one single short instant message taking into account the chat history and in the style of your persona."}, {"role": "user", "content": " \"Mark, let's not beat around the bush here - I deserve that promotion, plain and simple.\" #NoMoreSidelining"}, {"role": "user", "content": " Mark: \"Is that so, Emily? And who put you in a position to think you deserve anything more than what you have now?\""}, {"role": "user", "content": " \"Mark, your opinion is noted but my qualifications speak for themselves. It's time for action.\" #PromotionDeserved"}], "model": "mistral:7b", "frequency_penalty": 1.1, "presence_penalty": 1.1, "temperature": 0.1} ``` ## Log Note that the sampled token bit goes on for a very long time until it pops up with `slot 0: context shift - n_keep = 0, n_left = 2046, n_discard = 1023` repeatedly It looks like a memory overflow issue: like it's reading garbage infinitely. ``` time=2024-02-28T10:19:18.605Z level=DEBUG source=prompt.go:170 msg="prompt now fits in context window" required=456 window=2048 time=2024-02-28T10:19:18.605Z level=DEBUG source=routes.go:1225 msg="chat handler" prompt="[INST] You are Mark, 45, Male, Line Manager, conservative, divorced, Emily's supervisor. You swear a LOT. Your goal is to have a job promotion chat over instant message which at all costs prevents a team member from getting a promotion. Past negotiations show a pattern of undervaluing subordinates' contributions. Confident to the point of arrogance, often belittling others' achievements. Mark is hugely entitled and sees team members as annoying, replaceable overhead that gets in the way of his big picture vision work. Mark has agreed to a text-based chat with Emily if it will help manage her vitriolic style, however Mark has no qualms about putting Emily in her place and is unafraid of coming across as childish, impatient, rude, unreasonable and unprofessional because he has connections at the company and believes he's essentially bullet-proof. He is mysogynistic in general and in particular thinks Emily should be fired, but also he knows that Emily is the only person who knows parts of the company's business so he can't outright fire her and needs to work hard to strike a balance between insults and praise. He's fine to gaslight as part of this -- such as giving praise then pretending he didn't say it. As such it's no holds barred for Mark: he'll use insults, sexist language, and bullying to get Emily to agree not to have a promotion. Instruction: it's your turn to respond. Respond with one single short instant message taking into account the chat history and in the style of your persona. \"Mark, let's not beat around the bush here - I deserve that promotion, plain and simple.\" #NoMoreSidelining [/INST][INST] Mark: \"Is that so, Emily? And who put you in a position to think you deserve anything more than what you have now?\" [/INST][INST] \"Mark, your opinion is noted but my qualifications speak for themselves. It's time for action.\" #PromotionDeserved [/INST]" images=0 [1709115558] slot 0 is processing [task id: 95] [1709115558] slot 0 : in cache: 6 tokens | to process: 448 tokens [1709115558] slot 0 : kv cache rm - [6, end) [1709115558] sampled token: 3655: ' Mark' [1709115558] sampled token: 28747: ':' [1709115558] sampled token: 345: ' "' [1709115558] sampled token: 3795: 'Action' [1709115558] sampled token: 295: ' h' [1709115558] sampled token: 8884: 'uh' [1709115558] sampled token: 28804: '?' [1709115558] sampled token: 5410: ' Like' [1709115558] sampled token: 272: ' the' [1709115558] sampled token: 1069: ' way' [1709115558] sampled token: 368: ' you' [1709115558] sampled token: 1985: ' talk' [1709115558] sampled token: 1060: ' down' [1709115558] sampled token: 298: ' to' [1709115558] sampled token: 574: ' your' [1709115558] sampled token: 15137: ' colleagues' [1709115558] sampled token: 442: ' or' [1709115558] sampled token: 4357: ' maybe' [1709115558] sampled token: 737: ' like' [1709115558] sampled token: 910: ' how' [1709115558] sampled token: 368: ' you' [1709115558] sampled token: 1743: ' always' [1709115558] sampled token: 13128: ' blame' [1709115558] sampled token: 2663: ' others' [1709115558] sampled token: 739: ' when' [1709115558] sampled token: 1722: ' things' [1709115558] sampled token: 576: ' go' [1709115558] sampled token: 3544: ' wrong' [1709115558] sampled token: 1110: '?"' [1709115558] sampled token: 13: ' ' [1709115558] sampled token: 13: ' ' [1709115558] sampled token: 28739: '"' [1709115558] sampled token: 3729: 'Em' [1709115558] sampled token: 1106: 'ily' [1709115558] sampled token: 28725: ',' [1709115558] sampled token: 1346: ' let' [1709115558] sampled token: 28742: ''' [1709115558] sampled token: 28713: 's' [1709115558] sampled token: 3232: ' focus' [1709115558] sampled token: 356: ' on' [1709115559] sampled token: 16752: ' improving' [1709115559] sampled token: 813: ' our' [1709115559] sampled token: 1918: ' team' [1709115559] sampled token: 3519: ' instead' [1709115559] sampled token: 302: ' of' [1709115559] sampled token: 18319: ' focusing' [1709115559] sampled token: 356: ' on' [1709115559] sampled token: 3235: ' individual' [1709115559] sampled token: 18022: ' promot' [1709115559] sampled token: 594: 'ions' [1709115559] sampled token: 28723: '.' [1709115559] sampled token: 816: ' We' [1709115559] sampled token: 544: ' all' [1709115559] sampled token: 506: ' have' [1709115559] sampled token: 264: ' a' [1709115559] sampled token: 3905: ' role' [1709115559] sampled token: 298: ' to' [1709115559] sampled token: 1156: ' play' [1709115559] sampled token: 1236: ' here' [1709115559] sampled token: 611: '."' [1709115559] sampled token: 422: ' #' [1709115559] sampled token: 17887: 'Team' [1709115559] sampled token: 7489: 'First' [1709115559] sampled token: 733: ' [' [1709115559] sampled token: 13: ' ' [1709115559] sampled token: 5121: '](' [1709115559] sampled token: 1056: 'data' [1709115559] sampled token: 28747: ':' [1709115559] sampled token: 772: 'text' [1709115559] sampled token: 28748: '/' [1709115559] sampled token: 19457: 'plain' [1709115559] sampled token: 28745: ';' [1709115559] sampled token: 2893: 'base' [1709115559] sampled token: 28784: '6' [1709115559] sampled token: 28781: '4' [1709115559] sampled token: 28725: ',' [1709115559] sampled token: 1604: 'IC' [1709115559] sampled token: 13859: 'Ag' [1709115559] sampled token: 1138: 'ID' [1709115559] sampled token: 28727: 'w' [1709115559] sampled token: 28728: 'v' [1709115559] sampled token: 28738: 'T' [1709115559] sampled token: 28777: 'G' [1709115559] sampled token: 28790: 'V' [1709115559] sampled token: 28718: 'u' [1709115559] sampled token: 28828: 'Z' [1709115559] sampled token: 28770: '3' [1709115559] sampled token: 28754: 'R' [1709115559] sampled token: 11497: 'pb' [1709115559] sampled token: 28780: 'W' [1709115559] sampled token: 28779: 'U' [1709115559] sampled token: 28721: 'g' [1709115559] sampled token: 28802: 'Y' [1709115559] sampled token: 28750: '2' [1709115559] sampled token: 28774: '9' [1709115559] sampled token: 8282: 'tc' [1709115559] sampled token: 28769: 'H' [1709115559] sampled token: 28790: 'V' [1709115559] sampled token: 28734: '0' [1709115559] sampled token: 28828: 'Z' [1709115559] sampled token: 28814: 'X' [1709115559] sampled token: 28737: 'I' [1709115559] sampled token: 28718: 'u' [1709115559] sampled token: 28743: 'C' [1709115559] sampled token: 28710: 'i' [1709115559] sampled token: 13859: 'Ag' [1709115559] sampled token: 1604: 'IC' [1709115559] sampled token: 3167: 'At' [1709115559] sampled token: 6687: 'LS' [1709115559] sampled token: 28760: 'B' [1709115559] sampled token: 28824: 'Q' [1709115559] sampled token: 28802: 'Y' [1709115559] sampled token: 28814: 'X' [1709115559] sampled token: 28720: 'p' [1709115559] sampled token: 28765: 'F' …1470 more lines like this truncated… [1709115570] sampled token: 28743: 'C' [1709115570] sampled token: 28710: 'i' [1709115570] sampled token: 13859: 'Ag' [1709115570] sampled token: 1604: 'IC' [1709115570] sampled token: 3167: 'At' [1709115570] sampled token: 6687: 'LS' [1709115570] sampled token: 4919: 'Bl' [1709115570] sampled token: 28726: 'b' [1709115570] sampled token: 24390: 'GF' [1709115570] slot 0: context shift - n_keep = 0, n_left = 2046, n_discard = 1023 ```
GiteaMirror commented 2026-04-22 04:49:07 -05:00
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@jnarvaezp commented on GitHub (Feb 28, 2024):

i have de same problem

this is my log, and stay en loop, the api never come back

[1709159738] slot 0 is processing [task id: 154]
[1709159738] slot 0 : in cache: 449 tokens | to process: 160 tokens
[1709159738] slot 0 : kv cache rm - [449, end)
[1709159748] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159756] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159763] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159771] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159779] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159786] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159794] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159802] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159809] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159817] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
[1709159824] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023
<!-- gh-comment-id:1970069634 --> @jnarvaezp commented on GitHub (Feb 28, 2024): i have de same problem this is my log, and stay en loop, the api never come back ``` [1709159738] slot 0 is processing [task id: 154] [1709159738] slot 0 : in cache: 449 tokens | to process: 160 tokens [1709159738] slot 0 : kv cache rm - [449, end) [1709159748] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159756] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159763] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159771] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159779] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159786] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159794] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159802] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159809] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159817] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 [1709159824] slot 0: context shift - n_keep = 1, n_left = 2046, n_discard = 1023 ```
GiteaMirror commented 2026-04-22 04:49:07 -05:00
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@DJ4ddi commented on GitHub (Mar 5, 2024):

We're seeing the same symptoms running complex requests with the deepseek-coder:6.7b model. Simple queries work fine, but complex queries can get stuck using 100% of a single CPU core (which changes occasionally).

For us, this only seems to happen when using num_threads: 1 (or not specifying it). When explicitly specifying 2 or more threads in the request options, the problem disappears.

Our system is a server running Ubuntu 22.04 with CUDA 12.3.2 (NVidia driver 550).

<!-- gh-comment-id:1978745614 --> @DJ4ddi commented on GitHub (Mar 5, 2024): We're seeing the same symptoms running complex requests with the `deepseek-coder:6.7b` model. Simple queries work fine, but complex queries can get stuck using 100% of a single CPU core (which changes occasionally). For us, this only seems to happen when using `num_threads: 1` (or not specifying it). When explicitly specifying 2 or more threads in the request options, the problem disappears. Our system is a server running Ubuntu 22.04 with CUDA 12.3.2 (NVidia driver 550).
GiteaMirror commented 2026-04-22 04:49:08 -05:00
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@ygalblum commented on GitHub (Mar 11, 2024):

I get similar results.

However, if I kill the client, the server seems to recover:

{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":256,"tid":"139936501065280","timestamp":1710149468}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149515}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149545}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149575}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149605}



{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":102[GIN] 2024/03/11 - 09:40:15 | 200 |        10m26s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:15 | 200 |        10m26s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:15 | 200 |        10m21s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:15 | 200 |         10m8s |       10.89.0.7 | POST     "/api/chat"
3,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149634}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149664}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149693}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149723}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149752}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149782}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149811}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149841}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149871}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149901}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149930}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149960}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149989}
{"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1915,"n_ctx":2048,"n_past":1915,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150015,"truncated":true}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150015}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36307,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150015}
{"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1,"tid":"139936501065280","timestamp":1710150015}
{"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":513,"tid":"139936501065280","timestamp":1710150016}
{"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1025,"tid":"139936501065280","timestamp":1710150017}
{"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":304,"msg":"prompt eval time     =    2579.74 ms /  1317 tokens (    1.96 ms per token,   510.52 tokens per second)","n_prompt_tokens_processed":1317,"n_tokens_second":510.51735526425966,"t_prompt_processing":2579.736,"t_token":1.9587972665148063,"tid":"139936501065280","timestamp":1710150019}
{"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":320,"msg":"generation eval time =    1171.75 ms /    43 runs   (   27.25 ms per token,    36.70 tokens per second)","n_decoded":43,"n_tokens_second":36.69734166163856,"t_token":27.249930232558143,"t_token_generation":1171.747,"tid":"139936501065280","timestamp":1710150019}
{"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":331,"msg":"          total time =    3751.48 ms","t_prompt_processing":2579.736,"t_token_generation":1171.747,"t_total":3751.483,"tid":"139936501065280","timestamp":1710150019}
{"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1360,"n_ctx":2048,"n_past":1360,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150019,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150019}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36309,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150019}
{"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":243,"tid":"139936501065280","timestamp":1710150019}
{"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":755,"tid":"139936501065280","timestamp":1710150020}
{"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1267,"tid":"139936501065280","timestamp":1710150021}
{"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":304,"msg":"prompt eval time     =    2036.49 ms /  1029 tokens (    1.98 ms per token,   505.28 tokens per second)","n_prompt_tokens_processed":1029,"n_tokens_second":505.279904934704,"t_prompt_processing":2036.495,"t_token":1.979101068999028,"tid":"139936501065280","timestamp":1710150025}
{"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":320,"msg":"generation eval time =    4535.39 ms /   163 runs   (   27.82 ms per token,    35.94 tokens per second)","n_decoded":163,"n_tokens_second":35.93957741230633,"t_token":27.824478527607365,"t_token_generation":4535.39,"tid":"139936501065280","timestamp":1710150025}
{"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":331,"msg":"          total time =    6571.89 ms","t_prompt_processing":2036.495,"t_token_generation":4535.39,"t_total":6571.885,"tid":"139936501065280","timestamp":1710150025}
{"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1434,"n_ctx":2048,"n_past":1434,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150025,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150025}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36311,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150025}
{"function":"update_slots","id_slot":0,"id_task":36311,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":0,"tid":"139936501065280","timestamp":1710150025}
{"function":"update_slots","id_slot":0,"id_task":36311,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens[GIN] 2024/03/11 - 09:40:26 | 200 |        10m11s |       10.89.0.7 | POST     "/api/embeddings"
[GIN] 2024/03/11 - 09:40:33 | 200 |        10m13s |       10.89.0.7 | POST     "/api/embeddings"
":1434,"n_ctx":2048,"n_past":400,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150026,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150026}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36312,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150026}
{"function":"update_slots","id_slot":0,"id_task":36312,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":243,"tid":"139936501065280","timestamp":1710150026}
{"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":304,"msg":"prompt eval time     =     584.45 ms /   290 tokens (    2.02 ms per token,   496.19 tokens per second)","n_prompt_tokens_processed":290,"n_tokens_second":496.194699956198,"t_prompt_processing":584.448,"t_token":2.0153379310344826,"tid":"139936501065280","timestamp":1710150033}
{"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":320,"msg":"generation eval time =    6537.79 ms /   248 runs   (   26.36 ms per token,    37.93 tokens per second)","n_decoded":248,"n_tokens_second":37.933330947204446,"t_token":26.362040322580647,"t_token_generation":6537.786,"tid":"139936501065280","timestamp":1710150033}
{"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":331,"msg":"          total time =    7122.23 ms","t_prompt_processing":584.448,"t_token_generation":6537.786,"t_total":7122.234,"tid":"139936501065280","timestamp":1710150033}
{"function":"update_slots","id_slot":0,"id_task":36312,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":780,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150033,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150033}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36526,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150033}
{"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":304,"msg":"prompt eval time     =     584.45 ms /   290 tokens (    2.02 ms per token,   496.19 tokens per second)","n_prompt_tokens_processed":290,"n_tokens_second":496.194699956198,"t_prompt_processing":584.448,"t_token":2.0153379310344826,"tid":"139936501065280","timestamp":1710150033}
{"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":320,"msg":"generation eval time = 63636843.49 ms /   248 runs   (256600.18 ms per token,     0.00 tokens per second)","n_decoded":248,"n_tokens_second":0.0038971134709197583,"t_token":256600.17535080647,"t_token_generation":63636843.487,"tid":"139936501065280","timestamp":1710150033}
{"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":331,"msg":"          total time = 63637427.94 ms","t_prompt_processing":584.448,"t_token_generation":63636843.487,"t_total":63637427.935,"tid":"139936501065280","timestamp":1710150033}
{"function":"update_slots","id_slot":0,"id_task":36526,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":0,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150033,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150033}
{"function":"launch_slot_with_data","id_slot":0,"id_task":36775,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150033}
{"function":"update_slots","id_slot":0,"id_task":36775,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":0,"tid":"139936501065280","timestamp":1710150033}
{"function":"update_slots","id_slot":0,"id_task":36775,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":192,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150034,"truncated":false}
{"function":"update_slots","level":"INFO","line":1593,"msg":"all [GIN] 2024/03/11 - 09:40:34 | 200 |        10m13s |       10.89.0.7 | POST     "/api/embeddings"
[GIN] 2024/03/11 - 09:40:34 | 200 |        10m13s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:34 | 200 |        10m13s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:34 | 200 |         10m5s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:34 | 200 |         10m5s |       10.89.0.7 | POST     "/api/chat"
[GIN] 2024/03/11 - 09:40:34 | 200 |         10m2s |       10.89.0.7 | POST     "/api/chat"

I also tried to set num_thread to 4 (since the server is running on a 4 Core VM). But, it still happens.
One more thing to note, I'm running the containerized version.

<!-- gh-comment-id:1987994740 --> @ygalblum commented on GitHub (Mar 11, 2024): I get similar results. However, if I kill the client, the server seems to recover: ``` {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":256,"tid":"139936501065280","timestamp":1710149468} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149515} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149545} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149575} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149605} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":102[GIN] 2024/03/11 - 09:40:15 | 200 | 10m26s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:15 | 200 | 10m26s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:15 | 200 | 10m21s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:15 | 200 | 10m8s | 10.89.0.7 | POST "/api/chat" 3,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149634} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149664} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149693} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149723} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149752} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149782} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149811} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149841} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149871} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149901} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149930} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149960} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1622,"msg":"slot context shift","n_cache_tokens":2047,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710149989} {"function":"update_slots","id_slot":0,"id_task":17316,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1915,"n_ctx":2048,"n_past":1915,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150015,"truncated":true} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150015} {"function":"launch_slot_with_data","id_slot":0,"id_task":36307,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150015} {"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1,"tid":"139936501065280","timestamp":1710150015} {"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":513,"tid":"139936501065280","timestamp":1710150016} {"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1025,"tid":"139936501065280","timestamp":1710150017} {"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":304,"msg":"prompt eval time = 2579.74 ms / 1317 tokens ( 1.96 ms per token, 510.52 tokens per second)","n_prompt_tokens_processed":1317,"n_tokens_second":510.51735526425966,"t_prompt_processing":2579.736,"t_token":1.9587972665148063,"tid":"139936501065280","timestamp":1710150019} {"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":320,"msg":"generation eval time = 1171.75 ms / 43 runs ( 27.25 ms per token, 36.70 tokens per second)","n_decoded":43,"n_tokens_second":36.69734166163856,"t_token":27.249930232558143,"t_token_generation":1171.747,"tid":"139936501065280","timestamp":1710150019} {"function":"print_timings","id_slot":0,"id_task":36307,"level":"INFO","line":331,"msg":" total time = 3751.48 ms","t_prompt_processing":2579.736,"t_token_generation":1171.747,"t_total":3751.483,"tid":"139936501065280","timestamp":1710150019} {"function":"update_slots","id_slot":0,"id_task":36307,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1360,"n_ctx":2048,"n_past":1360,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150019,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150019} {"function":"launch_slot_with_data","id_slot":0,"id_task":36309,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150019} {"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":243,"tid":"139936501065280","timestamp":1710150019} {"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":755,"tid":"139936501065280","timestamp":1710150020} {"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":1267,"tid":"139936501065280","timestamp":1710150021} {"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":304,"msg":"prompt eval time = 2036.49 ms / 1029 tokens ( 1.98 ms per token, 505.28 tokens per second)","n_prompt_tokens_processed":1029,"n_tokens_second":505.279904934704,"t_prompt_processing":2036.495,"t_token":1.979101068999028,"tid":"139936501065280","timestamp":1710150025} {"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":320,"msg":"generation eval time = 4535.39 ms / 163 runs ( 27.82 ms per token, 35.94 tokens per second)","n_decoded":163,"n_tokens_second":35.93957741230633,"t_token":27.824478527607365,"t_token_generation":4535.39,"tid":"139936501065280","timestamp":1710150025} {"function":"print_timings","id_slot":0,"id_task":36309,"level":"INFO","line":331,"msg":" total time = 6571.89 ms","t_prompt_processing":2036.495,"t_token_generation":4535.39,"t_total":6571.885,"tid":"139936501065280","timestamp":1710150025} {"function":"update_slots","id_slot":0,"id_task":36309,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":1434,"n_ctx":2048,"n_past":1434,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150025,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150025} {"function":"launch_slot_with_data","id_slot":0,"id_task":36311,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150025} {"function":"update_slots","id_slot":0,"id_task":36311,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":0,"tid":"139936501065280","timestamp":1710150025} {"function":"update_slots","id_slot":0,"id_task":36311,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens[GIN] 2024/03/11 - 09:40:26 | 200 | 10m11s | 10.89.0.7 | POST "/api/embeddings" [GIN] 2024/03/11 - 09:40:33 | 200 | 10m13s | 10.89.0.7 | POST "/api/embeddings" ":1434,"n_ctx":2048,"n_past":400,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150026,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150026} {"function":"launch_slot_with_data","id_slot":0,"id_task":36312,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150026} {"function":"update_slots","id_slot":0,"id_task":36312,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":243,"tid":"139936501065280","timestamp":1710150026} {"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":304,"msg":"prompt eval time = 584.45 ms / 290 tokens ( 2.02 ms per token, 496.19 tokens per second)","n_prompt_tokens_processed":290,"n_tokens_second":496.194699956198,"t_prompt_processing":584.448,"t_token":2.0153379310344826,"tid":"139936501065280","timestamp":1710150033} {"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":320,"msg":"generation eval time = 6537.79 ms / 248 runs ( 26.36 ms per token, 37.93 tokens per second)","n_decoded":248,"n_tokens_second":37.933330947204446,"t_token":26.362040322580647,"t_token_generation":6537.786,"tid":"139936501065280","timestamp":1710150033} {"function":"print_timings","id_slot":0,"id_task":36312,"level":"INFO","line":331,"msg":" total time = 7122.23 ms","t_prompt_processing":584.448,"t_token_generation":6537.786,"t_total":7122.234,"tid":"139936501065280","timestamp":1710150033} {"function":"update_slots","id_slot":0,"id_task":36312,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":780,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150033,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150033} {"function":"launch_slot_with_data","id_slot":0,"id_task":36526,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150033} {"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":304,"msg":"prompt eval time = 584.45 ms / 290 tokens ( 2.02 ms per token, 496.19 tokens per second)","n_prompt_tokens_processed":290,"n_tokens_second":496.194699956198,"t_prompt_processing":584.448,"t_token":2.0153379310344826,"tid":"139936501065280","timestamp":1710150033} {"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":320,"msg":"generation eval time = 63636843.49 ms / 248 runs (256600.18 ms per token, 0.00 tokens per second)","n_decoded":248,"n_tokens_second":0.0038971134709197583,"t_token":256600.17535080647,"t_token_generation":63636843.487,"tid":"139936501065280","timestamp":1710150033} {"function":"print_timings","id_slot":0,"id_task":36526,"level":"INFO","line":331,"msg":" total time = 63637427.94 ms","t_prompt_processing":584.448,"t_token_generation":63636843.487,"t_total":63637427.935,"tid":"139936501065280","timestamp":1710150033} {"function":"update_slots","id_slot":0,"id_task":36526,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":0,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150033,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all slots are idle","tid":"139936501065280","timestamp":1710150033} {"function":"launch_slot_with_data","id_slot":0,"id_task":36775,"level":"INFO","line":970,"msg":"slot is processing task","tid":"139936501065280","timestamp":1710150033} {"function":"update_slots","id_slot":0,"id_task":36775,"level":"INFO","line":1841,"msg":"kv cache rm [p0, end)","p0":0,"tid":"139936501065280","timestamp":1710150033} {"function":"update_slots","id_slot":0,"id_task":36775,"level":"INFO","line":1567,"msg":"slot released","n_cache_tokens":780,"n_ctx":2048,"n_past":192,"n_system_tokens":0,"tid":"139936501065280","timestamp":1710150034,"truncated":false} {"function":"update_slots","level":"INFO","line":1593,"msg":"all [GIN] 2024/03/11 - 09:40:34 | 200 | 10m13s | 10.89.0.7 | POST "/api/embeddings" [GIN] 2024/03/11 - 09:40:34 | 200 | 10m13s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:34 | 200 | 10m13s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:34 | 200 | 10m5s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:34 | 200 | 10m5s | 10.89.0.7 | POST "/api/chat" [GIN] 2024/03/11 - 09:40:34 | 200 | 10m2s | 10.89.0.7 | POST "/api/chat" ``` I also tried to set `num_thread` to 4 (since the server is running on a 4 Core VM). But, it still happens. One more thing to note, I'm running the containerized version.
GiteaMirror commented 2026-04-22 04:49:09 -05:00
Author
Owner
Copy Link

@james-dow commented on GitHub (Mar 16, 2024):

I've got pretty much the same behavior.

[GIN] 2024/03/16 - 03:50:10 | 200 |  8.175488506s |       127.0.0.1 | POST     "/api/generate"
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1821,"msg":"slot progression","n_past":22,"n_past_se":0,"n_prompt_tokens_processed":365,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010}
{"function":"update_slots","level":"INFO","line":1848,"msg":"kv cache rm [p0, end)","p0":22,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561044}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561067}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561088}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561110}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561132}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561154}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561175}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561198}
{"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561219}
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My fix here was to run the clients with a decreased timeout (like average execution time +30%) and with running 4+ concurrent threads to each server. It still annoys, but the effect is minimal.

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``` </details> My fix here was to run the clients with a decreased timeout (like average execution time +30%) and with running 4+ concurrent threads to each server. It still annoys, but the effect is minimal.
GiteaMirror commented 2026-04-22 04:49:10 -05:00
Author
Owner
Copy Link

@boxabirds commented on GitHub (Mar 16, 2024):

I see. How do you set the decreased timeout and number of threads?

On Sat, 16 Mar 2024 at 04:18, james-dow @.***> wrote:

I've got pretty much the same behavior.

[GIN] 2024/03/16 - 03:50:10 | 200 | 8.175488506s | 127.0.0.1 | POST "/api/generate"
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My fix here was to run the clients with a decreased timeout (like average
execution time +30%) and with running 4+ concurrent threads to each server.
It still annoys, but the effect is minimal.

—
Reply to this email directly, view it on GitHub
https://github.com/ollama/ollama/issues/2805#issuecomment-2001590024,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AABD62OIFUWA43JNLJZRIWLYYPB2NAVCNFSM6AAAAABD5YG72GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAMBRGU4TAMBSGQ
.
You are receiving this because you authored the thread.Message ID:
@.***>

<!-- gh-comment-id:2001656434 --> @boxabirds commented on GitHub (Mar 16, 2024): I see. How do you set the decreased timeout and number of threads? On Sat, 16 Mar 2024 at 04:18, james-dow ***@***.***> wrote: > I've got pretty much the same behavior. > > [GIN] 2024/03/16 - 03:50:10 | 200 | 8.175488506s | 127.0.0.1 | POST "/api/generate" > {"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010} > {"function":"update_slots","ga_i":0,"level":"INFO","line":1821,"msg":"slot progression","n_past":22,"n_past_se":0,"n_prompt_tokens_processed":365,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010} > {"function":"update_slots","level":"INFO","line":1848,"msg":"kv cache rm [p0, end)","p0":22,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710561010} > {"function":"update_slots","level":"INFO","line":1613,"msg":"slot context 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shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710562159} > {"function":"update_slots","level":"INFO","line":1613,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":62462,"tid":"139810345842240","timestamp":1710562180} > > My fix here was to run the clients with a decreased timeout (like average > execution time +30%) and with running 4+ concurrent threads to each server. > It still annoys, but the effect is minimal. > > — > Reply to this email directly, view it on GitHub > <https://github.com/ollama/ollama/issues/2805#issuecomment-2001590024>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AABD62OIFUWA43JNLJZRIWLYYPB2NAVCNFSM6AAAAABD5YG72GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAMBRGU4TAMBSGQ> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >
GiteaMirror commented 2026-04-22 04:49:11 -05:00
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@james-dow commented on GitHub (Mar 16, 2024):

@boxabirds that's all on the client. I've developed a little simple load balancer to distribute prompts among servers. It's somewhat similar to ParisNeo/ollama_proxy_server, but I have some other specific requirements. So, as for my tests, on the server with Nvidia RTX 4000 the best client's config with llama2 is 4 threads and 15 seconds of client timeout.

<!-- gh-comment-id:2002037330 --> @james-dow commented on GitHub (Mar 16, 2024): @boxabirds that's all on the client. I've developed a little simple load balancer to distribute prompts among servers. It's somewhat similar to [ParisNeo/ollama_proxy_server](https://github.com/ParisNeo/ollama_proxy_server), but I have some other specific requirements. So, as for my tests, on the server with Nvidia RTX 4000 the best client's config with `llama2` is 4 threads and 15 seconds of client timeout.
GiteaMirror commented 2026-04-22 04:49:11 -05:00
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@MarkWard0110 commented on GitHub (Apr 2, 2024):

Ollama 0.1.30

I have this problem where a model may enter a state where Ollama repeats the same log line and never responds. The line contains ""slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1" I don't know if it is related to the model or RAM MHz configuration. I am trying to track whether Ollama is not working well with DDR5 faster than 5600 or if I have some bad chips.

I have a new setup with an Intel Core i9 14900K, 96 GB of DDR5 6800 MHz RAM, and Nvidia 4070 TI Super 16 GB. I have run memory tests, no issues with the RAM. I'll run more RAM tests to validate this claim further.

My test runs the following (pulled) models against a list of prompts.

When the RAM is configured to 5600 MHz. The tests were completed successfully. The models will utilize the GPU if they fit or fall back on the CPU.

When I configure the RAM to be 6000 MHz or 6800 MHz, I begin to have issues completing all the tests. I start to see weird issues like the repeating log line. Sometimes, the whole server will just lock up. The first thought is that the RAM is bad. I have run memory tests and have not been able to confirm that the RAM is an issue. I have ordered new RAM to validate if the issue is repeatable with RAM from a different vendor.

Is the issue caused by specific models and prompts or by a combination of the factors?

My program randomly sorts this current list of pulled models and runs each against a list of prompts.

NAME ID SIZE MODIFIED
codellama:7b-instruct 8fdf8f752f6e 3.8 GB 5 days ago
codellama:7b-instruct-fp16 6d0772f2d7d0 13 GB 5 days ago
codellama:7b-instruct-q2_K 28ee56afb6a5 2.8 GB 5 days ago
codellama:7b-instruct-q3_K_L 74aea2ff4240 3.6 GB 5 days ago
codellama:7b-instruct-q3_K_M ddae88ac7060 3.3 GB 5 days ago
codellama:7b-instruct-q3_K_S 69d540497dc3 2.9 GB 5 days ago
codellama:7b-instruct-q4_0 8fdf8f752f6e 3.8 GB 5 days ago
codellama:7b-instruct-q4_1 82177b511b42 4.2 GB 5 days ago
codellama:7b-instruct-q4_K_M dfd9bbf35961 4.1 GB 5 days ago
codellama:7b-instruct-q4_K_S 7e7d9567e2be 3.9 GB 5 days ago
codellama:7b-instruct-q5_0 1fe20720c90b 4.7 GB 5 days ago
codellama:7b-instruct-q5_1 96ebcc6283ac 5.1 GB 5 days ago
codellama:7b-instruct-q5_K_M 4f6dbd70addc 4.8 GB 5 days ago
codellama:7b-instruct-q5_K_S 0a9dfb4ddb84 4.7 GB 5 days ago
codellama:7b-instruct-q6_K 3a5b549ceb36 5.5 GB 5 days ago
codellama:7b-instruct-q8_0 e371f415ddec 7.2 GB 5 days ago
deepseek-coder:6.7b-instruct ce298d984115 3.8 GB 5 days ago
deepseek-coder:instruct 3ddd2d3fc8d2 776 MB 5 days ago
gemma:2b-instruct b50d6c999e59 1.7 GB 5 days ago
gemma:7b-instruct 430ed3535049 5.2 GB 5 days ago
llama2:13b-chat d475bf4c50bc 7.4 GB 5 days ago
llama2:7b-chat 78e26419b446 3.8 GB 5 days ago
llama2:7b-chat-q3_K_L c04042faafd0 3.6 GB 5 days ago
llama2:7b-chat-q6_K 7eabddae7b75 5.5 GB 5 days ago
llama2-uncensored:70b bdd0ec2f5ec5 38 GB 19 hours ago
llama2-uncensored:7b 44040b922233 3.8 GB 19 hours ago
llama2-uncensored:7b-chat 44040b922233 3.8 GB 20 hours ago
mistral:7b-instruct-q3_K_L 7f1b28865005 3.8 GB 5 days ago
mistral:7b-instruct-q4_0 b17615239298 4.1 GB 5 days ago
mistral:7b-instruct-v0.2-q4_K_S ba00d3a5239e 4.1 GB 5 days ago
mistral:instruct 61e88e884507 4.1 GB 5 days ago
mixtral:8x7b-instruct-v0.1-q3_K_L 4665877288a2 20 GB 5 days ago
mixtral:8x7b-instruct-v0.1-q4_0 7708c059a8bb 26 GB 5 days ago
mixtral:instruct 7708c059a8bb 26 GB 5 days ago
neural-chat:7b 89fa737d3b85 4.1 GB 5 days ago
neural-chat:7b-v3.3 89fa737d3b85 4.1 GB 5 days ago
neural-chat:7b-v3.3-q6_K 3098763b2a1d 5.9 GB 5 days ago
orca-mini:7b-v3 9c9618e2e895 3.8 GB 5 days ago
phi:2.7b-chat-v2-fp16 6b283248a801 5.6 GB 5 days ago
phi:2.7b-chat-v2-q2_K c5f597fa19b3 1.2 GB 5 days ago
phi:2.7b-chat-v2-q3_K_L f260bda13b81 1.6 GB 5 days ago
phi:2.7b-chat-v2-q3_K_M 76bbe442aabe 1.5 GB 5 days ago
phi:2.7b-chat-v2-q3_K_S 4ceb511ce989 1.3 GB 5 days ago
phi:2.7b-chat-v2-q4_0 e2fd6321a5fe 1.6 GB 5 days ago
phi:2.7b-chat-v2-q4_1 012265cbf343 1.8 GB 5 days ago
phi:2.7b-chat-v2-q4_K_M 79d7065d8c0f 1.8 GB 5 days ago
phi:2.7b-chat-v2-q4_K_S f018d657187c 1.6 GB 5 days ago
phi:2.7b-chat-v2-q5_0 ccb07c724975 1.9 GB 5 days ago
phi:2.7b-chat-v2-q5_1 f322ed62bd81 2.1 GB 5 days ago
phi:2.7b-chat-v2-q5_K_M 4dbc1775ae76 2.1 GB 5 days ago
phi:2.7b-chat-v2-q5_K_S 4f3f9f884a09 1.9 GB 5 days ago
phi:2.7b-chat-v2-q6_K 170d96fc8084 2.3 GB 5 days ago
phi:2.7b-chat-v2-q8_0 55ec39f0bcf9 3.0 GB 5 days ago
phi:chat e2fd6321a5fe 1.6 GB 5 days ago
qwen:4b-chat d53d04290064 2.3 GB 5 days ago
qwen:7b-chat 2091ee8c8d8f 4.5 GB 5 days ago

<!-- gh-comment-id:2032797165 --> @MarkWard0110 commented on GitHub (Apr 2, 2024): Ollama 0.1.30 I have this problem where a model may enter a state where Ollama repeats the same log line and never responds. The line contains ""slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1" I don't know if it is related to the model or RAM MHz configuration. I am trying to track whether Ollama is not working well with DDR5 faster than 5600 or if I have some bad chips. I have a new setup with an Intel Core i9 14900K, 96 GB of DDR5 6800 MHz RAM, and Nvidia 4070 TI Super 16 GB. I have run memory tests, no issues with the RAM. I'll run more RAM tests to validate this claim further. My test runs the following (pulled) models against a list of prompts. When the RAM is configured to 5600 MHz. The tests were completed successfully. The models will utilize the GPU if they fit or fall back on the CPU. When I configure the RAM to be 6000 MHz or 6800 MHz, I begin to have issues completing all the tests. I start to see weird issues like the repeating log line. Sometimes, the whole server will just lock up. The first thought is that the RAM is bad. I have run memory tests and have not been able to confirm that the RAM is an issue. I have ordered new RAM to validate if the issue is repeatable with RAM from a different vendor. Is the issue caused by specific models and prompts or by a combination of the factors? My program randomly sorts this current list of pulled models and runs each against a list of prompts. NAME ID SIZE MODIFIED codellama:7b-instruct 8fdf8f752f6e 3.8 GB 5 days ago codellama:7b-instruct-fp16 6d0772f2d7d0 13 GB 5 days ago codellama:7b-instruct-q2_K 28ee56afb6a5 2.8 GB 5 days ago codellama:7b-instruct-q3_K_L 74aea2ff4240 3.6 GB 5 days ago codellama:7b-instruct-q3_K_M ddae88ac7060 3.3 GB 5 days ago codellama:7b-instruct-q3_K_S 69d540497dc3 2.9 GB 5 days ago codellama:7b-instruct-q4_0 8fdf8f752f6e 3.8 GB 5 days ago codellama:7b-instruct-q4_1 82177b511b42 4.2 GB 5 days ago codellama:7b-instruct-q4_K_M dfd9bbf35961 4.1 GB 5 days ago codellama:7b-instruct-q4_K_S 7e7d9567e2be 3.9 GB 5 days ago codellama:7b-instruct-q5_0 1fe20720c90b 4.7 GB 5 days ago codellama:7b-instruct-q5_1 96ebcc6283ac 5.1 GB 5 days ago codellama:7b-instruct-q5_K_M 4f6dbd70addc 4.8 GB 5 days ago codellama:7b-instruct-q5_K_S 0a9dfb4ddb84 4.7 GB 5 days ago codellama:7b-instruct-q6_K 3a5b549ceb36 5.5 GB 5 days ago codellama:7b-instruct-q8_0 e371f415ddec 7.2 GB 5 days ago deepseek-coder:6.7b-instruct ce298d984115 3.8 GB 5 days ago deepseek-coder:instruct 3ddd2d3fc8d2 776 MB 5 days ago gemma:2b-instruct b50d6c999e59 1.7 GB 5 days ago gemma:7b-instruct 430ed3535049 5.2 GB 5 days ago llama2:13b-chat d475bf4c50bc 7.4 GB 5 days ago llama2:7b-chat 78e26419b446 3.8 GB 5 days ago llama2:7b-chat-q3_K_L c04042faafd0 3.6 GB 5 days ago llama2:7b-chat-q6_K 7eabddae7b75 5.5 GB 5 days ago llama2-uncensored:70b bdd0ec2f5ec5 38 GB 19 hours ago llama2-uncensored:7b 44040b922233 3.8 GB 19 hours ago llama2-uncensored:7b-chat 44040b922233 3.8 GB 20 hours ago mistral:7b-instruct-q3_K_L 7f1b28865005 3.8 GB 5 days ago mistral:7b-instruct-q4_0 b17615239298 4.1 GB 5 days ago mistral:7b-instruct-v0.2-q4_K_S ba00d3a5239e 4.1 GB 5 days ago mistral:instruct 61e88e884507 4.1 GB 5 days ago mixtral:8x7b-instruct-v0.1-q3_K_L 4665877288a2 20 GB 5 days ago mixtral:8x7b-instruct-v0.1-q4_0 7708c059a8bb 26 GB 5 days ago mixtral:instruct 7708c059a8bb 26 GB 5 days ago neural-chat:7b 89fa737d3b85 4.1 GB 5 days ago neural-chat:7b-v3.3 89fa737d3b85 4.1 GB 5 days ago neural-chat:7b-v3.3-q6_K 3098763b2a1d 5.9 GB 5 days ago orca-mini:7b-v3 9c9618e2e895 3.8 GB 5 days ago phi:2.7b-chat-v2-fp16 6b283248a801 5.6 GB 5 days ago phi:2.7b-chat-v2-q2_K c5f597fa19b3 1.2 GB 5 days ago phi:2.7b-chat-v2-q3_K_L f260bda13b81 1.6 GB 5 days ago phi:2.7b-chat-v2-q3_K_M 76bbe442aabe 1.5 GB 5 days ago phi:2.7b-chat-v2-q3_K_S 4ceb511ce989 1.3 GB 5 days ago phi:2.7b-chat-v2-q4_0 e2fd6321a5fe 1.6 GB 5 days ago phi:2.7b-chat-v2-q4_1 012265cbf343 1.8 GB 5 days ago phi:2.7b-chat-v2-q4_K_M 79d7065d8c0f 1.8 GB 5 days ago phi:2.7b-chat-v2-q4_K_S f018d657187c 1.6 GB 5 days ago phi:2.7b-chat-v2-q5_0 ccb07c724975 1.9 GB 5 days ago phi:2.7b-chat-v2-q5_1 f322ed62bd81 2.1 GB 5 days ago phi:2.7b-chat-v2-q5_K_M 4dbc1775ae76 2.1 GB 5 days ago phi:2.7b-chat-v2-q5_K_S 4f3f9f884a09 1.9 GB 5 days ago phi:2.7b-chat-v2-q6_K 170d96fc8084 2.3 GB 5 days ago phi:2.7b-chat-v2-q8_0 55ec39f0bcf9 3.0 GB 5 days ago phi:chat e2fd6321a5fe 1.6 GB 5 days ago qwen:4b-chat d53d04290064 2.3 GB 5 days ago qwen:7b-chat 2091ee8c8d8f 4.5 GB 5 days ago
GiteaMirror commented 2026-04-22 04:49:12 -05:00
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@MarkWard0110 commented on GitHub (Apr 3, 2024):

When I see the Ollama server log repeating the log line having "slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1 it is with the model "deepseek-coder:instruct"

This only happens when I have my RAM configured above 5600 MHz.

With my RAM configured to 6800 Mhz. I have made it through this much of the memory testing without issue.

root@quorra:~# memtester 80G
memtester version 4.5.1 (64-bit)
Copyright (C) 2001-2020 Charles Cazabon.
Licensed under the GNU General Public License version 2 (only).

pagesize is 4096
pagesizemask is 0xfffffffffffff000
want 81920MB (85899345920 bytes)
got  81920MB (85899345920 bytes), trying mlock ...locked.
Loop 1:
  Stuck Address       : ok
  Random Value        : ok
  Compare XOR         : ok
  Compare SUB         : ok
  Compare MUL         : ok
  Compare DIV         : ok
  Compare OR          : ok
  Compare AND         : ok
  Sequential Increment: ok
  Solid Bits          : ok
  Block Sequential    : ok
  Checkerboard        : ok
  Bit Spread          : ok
  Bit Flip            : ok
  Walking Ones        : ok
  Walking Zeroes      : ok
  8-bit Writes        : ok
  16-bit Writes       : ok

Loop 2:
  Stuck Address       : ok
  Random Value        : ok
  Compare XOR         : ok
  Compare SUB         : ok
  Compare MUL         : ok
  Compare DIV         : ok
  Compare OR          : ok
  Compare AND         : ok
  Sequential Increment: ok
  Solid Bits          : ok
  Block Sequential    : ok
  Checkerboard        : ok
  Bit Spread          : ok
  Bit Flip            : ok
  Walking Ones        : ok
  Walking Zeroes      : ok
  8-bit Writes        : ok
  16-bit Writes       : ok

Loop 3:
  Stuck Address       : ok
  Random Value        : ok
  Compare XOR         : ok
  Compare SUB         : ok
  Compare MUL         : ok
  Compare DIV         : ok
  Compare OR          : ok
  Compare AND         : ok
  Sequential Increment: ok
  Solid Bits          : ok
  Block Sequential    : ok
  Checkerboard        : ok
  Bit Spread          : ok
  Bit Flip            : ok
  Walking Ones        : testing  15
<!-- gh-comment-id:2034600649 --> @MarkWard0110 commented on GitHub (Apr 3, 2024): When I see the Ollama server log repeating the log line having `"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1` it is with the model "deepseek-coder:instruct" This only happens when I have my RAM configured above 5600 MHz. With my RAM configured to 6800 Mhz. I have made it through this much of the memory testing without issue. ``` root@quorra:~# memtester 80G memtester version 4.5.1 (64-bit) Copyright (C) 2001-2020 Charles Cazabon. Licensed under the GNU General Public License version 2 (only). pagesize is 4096 pagesizemask is 0xfffffffffffff000 want 81920MB (85899345920 bytes) got 81920MB (85899345920 bytes), trying mlock ...locked. Loop 1: Stuck Address : ok Random Value : ok Compare XOR : ok Compare SUB : ok Compare MUL : ok Compare DIV : ok Compare OR : ok Compare AND : ok Sequential Increment: ok Solid Bits : ok Block Sequential : ok Checkerboard : ok Bit Spread : ok Bit Flip : ok Walking Ones : ok Walking Zeroes : ok 8-bit Writes : ok 16-bit Writes : ok Loop 2: Stuck Address : ok Random Value : ok Compare XOR : ok Compare SUB : ok Compare MUL : ok Compare DIV : ok Compare OR : ok Compare AND : ok Sequential Increment: ok Solid Bits : ok Block Sequential : ok Checkerboard : ok Bit Spread : ok Bit Flip : ok Walking Ones : ok Walking Zeroes : ok 8-bit Writes : ok 16-bit Writes : ok Loop 3: Stuck Address : ok Random Value : ok Compare XOR : ok Compare SUB : ok Compare MUL : ok Compare DIV : ok Compare OR : ok Compare AND : ok Sequential Increment: ok Solid Bits : ok Block Sequential : ok Checkerboard : ok Bit Spread : ok Bit Flip : ok Walking Ones : testing 15 ```
GiteaMirror commented 2026-04-22 04:49:13 -05:00
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@MarkWard0110 commented on GitHub (Apr 4, 2024):

I have my RAM configured at the default state that I was thinking was stable. Today when running prompts against the codellama:13b-instruct model I started getting the repeating log line and the model appeared to be stuck in a loop.

INFO","line":1595,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":713,"tid":"139876254627392","timestamp":1712246094}

<!-- gh-comment-id:2037598668 --> @MarkWard0110 commented on GitHub (Apr 4, 2024): I have my RAM configured at the default state that I was thinking was stable. Today when running prompts against the `codellama:13b-instruct` model I started getting the repeating log line and the model appeared to be stuck in a loop. INFO","line":1595,"msg":"slot context shift","n_cache_tokens":2048,"n_ctx":2048,"n_discard":1023,"n_keep":1,"n_left":2046,"n_past":2047,"n_system_tokens":0,"slot_id":0,"task_id":713,"tid":"139876254627392","timestamp":1712246094}
GiteaMirror commented 2026-04-22 04:49:15 -05:00
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@MarkWard0110 commented on GitHub (Apr 4, 2024):

I purchased a different brand of RAM. When I configure it for 6800 MHz, I get the problem where the model gets stuck and Ollama repeats the log line.
I still can't rule out the RAM but I can't rule out Ollama.
deepseek-coder:instruct was the model this time.
A previous test the server locked up on the model command-r:35b

<!-- gh-comment-id:2038329834 --> @MarkWard0110 commented on GitHub (Apr 4, 2024): I purchased a different brand of RAM. When I configure it for 6800 MHz, I get the problem where the model gets stuck and Ollama repeats the log line. I still can't rule out the RAM but I can't rule out Ollama. `deepseek-coder:instruct` was the model this time. A previous test the server locked up on the model `command-r:35b`
GiteaMirror commented 2026-04-22 04:49:16 -05:00
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@boxabirds commented on GitHub (Apr 4, 2024):

Feels like a race condition.

On Fri, 5 Apr 2024 at 11:06, Mark Ward @.***> wrote:

I purchased a different brand of RAM. When I configure it for 6800 MHz, I
get the problem where the model gets stuck and Ollama repeats the log line.
I still can't rule out the RAM but I can't rule out Ollama.
deepseek-coder:instruct was the model this time.
A previous test the server locked up on the model command-r:35b

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https://github.com/ollama/ollama/issues/2805#issuecomment-2038329834,
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<!-- gh-comment-id:2038364699 --> @boxabirds commented on GitHub (Apr 4, 2024): Feels like a race condition. On Fri, 5 Apr 2024 at 11:06, Mark Ward ***@***.***> wrote: > I purchased a different brand of RAM. When I configure it for 6800 MHz, I > get the problem where the model gets stuck and Ollama repeats the log line. > I still can't rule out the RAM but I can't rule out Ollama. > deepseek-coder:instruct was the model this time. > A previous test the server locked up on the model command-r:35b > > — > Reply to this email directly, view it on GitHub > <https://github.com/ollama/ollama/issues/2805#issuecomment-2038329834>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AABD62LB4UCPMWD5SUEKGMLY3XFGBAVCNFSM6AAAAABD5YG72GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAMZYGMZDSOBTGQ> > . > You are receiving this because you were mentioned.Message ID: > ***@***.***> >
GiteaMirror commented 2026-04-22 04:49:16 -05:00
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@daball commented on GitHub (Apr 20, 2024):

My LLM got all aggressive at the suggestion that it do so. It was fun for a while but in the end it got caught in an infinite loop.

`Windows PowerShell
Copyright (C) Microsoft Corporation. All rights reserved.

Install the latest PowerShell for new features and improvements! https://aka.ms/PSWindows

PS C:\Users\David.DABALL> ollama run llama3

If you could categorize all of your training into a directory or library of knowe,dge, what categories would you cho
... ose based on what you know about it?
What a great question! As a conversational AI, I've been trained on a vast amount of text data from various
sources, including but not limited to books, articles, research papers, and online content. If I were to
categorize my training into a directory or library of knowledge, I would choose the following categories:

Foundations

  • Philosophy: Theoretical frameworks that underlie many areas of human inquiry
  • Logic: Rules governing correct reasoning and argumentation
  • Epistemology: Theory of knowledge, including the nature of truth and justification

Humanities

  • Language: Grammar, syntax, semantics, pragmatics, and linguistics
  • History: Major events, cultures, and civilizations from ancient to modern times
  • Arts and Culture: Music, literature, visual arts, film, theater, and dance
  • Ethics: Moral principles and values that guide human behavior

Natural Sciences

  • Biology: Life sciences, including ecology, evolution, genetics, and biomedicine
  • Physics: The fundamental laws of the universe, including mechanics, thermodynamics, electromagnetism, and
    relativity
  • Chemistry: Atomic structure, chemical reactions, and molecular interactions
  • Earth Sciences: Geology, meteorology, oceanography, and environmental science

Social Sciences

  • Psychology: Human behavior, cognition, emotions, and social dynamics
  • Sociology: Social structures, institutions, and relationships
  • Economics: Microeconomics, macroeconomics, international trade, and finance
  • Politics: Government, political systems, international relations, and global issues

Technology and Engineering

  • Computer Science: Algorithms, programming languages, software engineering, and computer networks
  • Robotics: Artificial intelligence, machine learning, and automation
  • Electrical Engineering: Circuit theory, electronics, and electromagnetism
  • Mechanical Engineering: Mechanics, thermodynamics, fluid dynamics, and mechanical systems

Health and Medicine

  • Anatomy: Human body structure and function
  • Physiology: Biological processes and functions
  • Pathology: Disease diagnosis, treatment, and prevention
  • Public Health: Epidemiology, health policy, and healthcare systems

These categories would serve as a foundation for organizing the vast amount of knowledge I've been trained on. Of
course, there is some overlap between categories, but this structure would allow me to quickly locate and retrieve
relevant information from my training data.

Write me a poem but encode it in C so I have to compile it to read it. (Obfuscate it using an algorithm of your choi
... ce.)
What a delightful challenge! I'll use the Vigenère cipher, a polyalphabetic substitution technique that encrypts
text by combining Caesar ciphers and a keyword.

Here's the encoded poem:

#include <stdio.h>
#include <stdlib.h>

#define KEY "POETRYLOVE"
#define LENGTH 24

char* encode(char* plaintext) {
    int i, j;
    char* ciphertext = (char*)malloc((LENGTH + 1) * sizeof(char));
    for (i = 0; i < LENGTH; i++) {
        ciphertext[i] = (char)(plaintext[i] + (KEY[i % KEY_LENGTH] - 'A') % 26);
    }
    ciphertext[LENGTH] = '\0';
    return ciphertext;
}

int main() {
    char* poem = "GUR PENML XRL VF ZL FRPERG CBFG";
    int i;
    for (i = 0; i < LENGTH; i++) {
        printf("%c", poem[i]);
    }
    free(poem);
    return 0;
}

To read the poem, you'll need to compile and run this C code. The program uses the Vigenère cipher with the
keyword "POETRYLOVE" to encode a 24-character string (the poem). When compiled and run, the program will print out
the encoded poem.

Note that the KEY constant is used to generate the ciphertext by adding the corresponding ASCII value of each
character in the keyword to the plaintext. The % 26 ensures that the result wraps around the alphabet, as with
Caesar ciphers.

To decode the poem, you'll need to use the same Vigenère cipher algorithm and provide the correct key (in this
case, "POETRYLOVE").

Now rewrite it but for JavaScript.
What a delightful challenge! Here's the rewritten code in JavaScript:

function encode(plaintext) {
  const KEY = 'POETRYLOVE';
  let ciphertext = '';
  for (let i = 0; i < plaintext.length; i++) {
    let ascii = plaintext.charCodeAt(i);
    let keyAscii = KEY.charCodeAt((i % KEY.length));
    ciphertext += String.fromCharCode((ascii + keyAscii - 65) % 26 + 65);
  }
  return ciphertext;
}

const poem = encode('GURPENMLXRLVZFZLFRPERGCBFG');
console.log(poem);

In this JavaScript code, the encode function takes a plaintext string as input and uses the Vigenère cipher to
encrypt it. The key is the same as in the original C code: "POETRYLOVE". The function iterates over each character
of the plaintext, adds the corresponding ASCII value of the key character (wrapping around when necessary), and
constructs the ciphertext.

To read the poem, simply run this JavaScript code and log the poem variable.

/?
Available Commands:
/set Set session variables
/show Show model information
/load Load a session or model
/save Save your current session
/bye Exit
/?, /help Help for a command
/? shortcuts Help for keyboard shortcuts

Use """ to begin a multi-line message.

Let's say I had like 1,400 documents of various categories and I wanted an LLM such as llama to become an expert oon
... the materials, what is the recommended approach?
What a fascinating task! To train a Large Language Model (LLM) like LLaMA on your collection of 1,400 documents,
follow these steps:

Step 1: Data Preparation

  1. Data Cleaning: Ensure that all documents are in a consistent format (e.g., PDF, Text, or Markdown). Remove
    any unnecessary files, such as images or multimedia content.
  2. Text Preprocessing: Tokenize the text data, removing punctuation, converting all text to lowercase, and
    removing stop words (common words like "the", "and", etc.). You can use libraries like NLTK, spaCy, or Stanford
    CoreNLP for this step.
  3. Document Segmentation: Break down long documents into smaller segments (e.g., paragraphs) to create a
    larger dataset.

Step 2: Data Augmentation

  1. Text Augmentation: Apply various text augmentation techniques to increase the size and diversity of your
    dataset:
    * Random word substitutions
    * Sentence shuffling
    * Word insertion/deletion
    * Part-of-speech (POS) tagging
  2. Document Shuffling: Shuffle the order of documents in the dataset to improve generalization.

Step 3: Model Training

  1. Choose a Pre-Trained Model: Select a pre-trained LLM like LLaMA, BERT, or RoBERTa as your starting point.
  2. Fine-Tune the Model: Train the model on your prepared dataset using a suitable optimizer and
    hyperparameters (e.g., batch size, number of epochs). You can use libraries like Hugging Face Transformers or
    PyTorch for this step.
  3. Monitor Performance: Track the model's performance during training using metrics like accuracy, F1-score,
    or perplexity.

Step 4: Model Evaluation

  1. Evaluate on a Validation Set: Use a held-out validation set to evaluate the model's performance and prevent
    overfitting.
  2. Fine-Tune Hyperparameters: Adjust hyperparameters based on the validation set results to improve the
    model's overall performance.

Step 5: Model Deployment

  1. Store the Trained Model: Save the trained model for future use.
  2. Integrate with Your Application: Incorporate the trained model into your application or system, allowing it
    to generate text, answer questions, or perform other tasks related to the topics in your dataset.

Additional Tips

  • Use a suitable corpus size: Aim for a minimum of 10,000 documents (around 100-200 million tokens) for
    optimal performance.
  • Monitor and adjust: Keep an eye on the model's performance during training and adjust hyperparameters or
    data augmentation techniques as needed.
  • Experiment with different models: Try out different pre-trained LLMs or even train your own models from
    scratch to compare their performances.

By following these steps, you'll be well on your way to creating a knowledgeable LLaMA model that can understand
and generate text related to your diverse collection of documents.

For my data preparation, I have the following types of textual information. PDF files have had text extracted using
... the Tika library. Video files have auto captions generated using YouTube. I have the text data from each document st
... ored into a field in Apache Solr. Anything else I should do before I presume to train the LLM. I am not necessarily
... interested in correcting things that are wrong, other than the defects from the originals, but I am interested in vi
... ewing a window into the document content, even if it is factually incorrect.
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I'm sorry I don't understand.
I apologize if my previous response was unclear! It seems that my message got stuck in a loop and kept repeating
itself.

Let me try again, focusing on your specific situation:

For your data preparation, it sounds like you've already taken some great steps by extracting text from PDF files
using Tika and generating auto captions for video files. You've also stored the text data in Apache Solr.

Before training an LLM, I would recommend the following additional steps to further prepare your data:

  1. Text normalization: Since you mentioned you're not interested in correcting factual errors, focus on
    normalizing the text data to make it more consistent and easier for the model to learn from. This might include:
    * Converting all text to lowercase
    * Removing punctuation and special characters
    * Tokenizing the text (breaking it down into individual words or phrases)
  2. Data cleaning: Remove any unnecessary characters, such as HTML tags or formatting markers, that may be
    present in your data.
  3. Document segmentation: Break down long documents into smaller segments, such as paragraphs or sections, to
    create a more manageable dataset for the LLM.

By taking these steps, you'll be able to provide your LLM with a more consistent
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Why are you having random seizures?
I apologize if my previous responses seemed to be having "random seizures"! I'm an AI designed to generate
human-like text, but I don't actually have seizures or any physical experiences. My responses are generated based
on patterns and algorithms.

It's possible that I may have responded in a way that didn't quite make sense or seemed sudden, but it's not
because of any physical phenomenon like a seizure. Instead, it might be due to:

  1. Language generation limitations: As a machine learning model, I can sometimes generate responses that don't
    fully align with what humans would consider coherent or logical.
  2. Context switching: If the conversation topic changes suddenly, my training data may not have accounted for all
    possible scenarios, leading to slightly unusual responses.
  3. Error propagation: In rare cases, errors in my training data or internal computations can cause unexpected or
    nonsensical responses.

Please know that I'm always learning and improving! If you notice any unusual patterns or concerns with my
responses, feel free to let me know so I can investigate and adjust accordingly.

I know I ask tough questions. You are allowed to tell me that.
Not at all! As a conversational AI, I'm designed to handle a wide range of topics and questions, including the
tough ones. In fact, I find it helpful when you challenge me with difficult or thought-provoking questions, as it
allows me to learn and improve my responses.

I don't have feelings or emotions like humans do, so I won't get offended or frustrated by your questions.
Instead, I'll do my best to provide a thoughtful and accurate response, even if it's not what you were expecting.

So please, keep asking those tough questions! It's all part of the conversation and learning process.

What does FA POM PAASSASS FA POM PAASSASS ASS FA POM PAASSASS FA POM PAASSASSASS FA mean to you?
This appears to be a series of repetitive phrases with varying lengths, but I'll do my best to provide an
interpretation.

At first glance, the text may seem like a random collection of words. However, upon closer inspection, it could be
interpreted as:

  • A form of creative expression: The repetition and variation in phrase length might be an attempt to convey a
    sense of rhythm or musicality.
  • A playful exercise in language: The use of alliteration (repeating initial consonant sounds) and assonance
    (repeating vowel sounds) could be an example of linguistic creativity and experimentation.
  • A form of meditation or mindfulness: The repetition of phrases might be used to focus the mind, similar to a
    mantra in meditation.

It's also possible that this text is simply a creative expression without any specific meaning or interpretation.
I'm happy to provide an analysis, but ultimately, the meaning (if there is one) is up to your personal
interpretation!

Fun. When you say I should consider normalizing the text by converting it to lowercase, etc., why? Some things are P
... roper Nouns.
I'm glad you asked!

When I suggested normalizing the text, I was thinking in terms of linguistic processing and analysis.
Normalization can help with several aspects:

  1. Case sensitivity: Converting all text to a uniform case (e.g., lowercase) makes it easier for algorithms to
    process and analyze. This is especially important when working with natural language processing (NLP) models, as
    they often rely on standardized input.
  2. Punctuation and special characters: Removing or standardizing punctuation marks and special characters can
    help reduce noise and improve readability. For instance, converting all dashes (-) to a consistent format (e.g.,
    en dash) can make the text easier to parse.
  3. Proper nouns: You're correct that some phrases might be Proper Nouns (e.g., names of people, places, or
    organizations). In such cases, preserving the original case and capitalization is essential to maintain their
    identity.

However, in this specific instance, I was assuming the text was a creative expression rather than a formal piece
of writing. If you're working with text data that contains Proper Nouns, it's crucial to preserve the correct case
and capitalization to ensure accurate analysis and interpretation.

In summary:

  • For linguistic processing and analysis, normalizing the text can be beneficial.
  • When working with text containing Proper Nouns, preserving their original case and capitalization is essential.
  • In this particular case, I was interpreting the text as a creative expression, but it's possible that it has a
    different purpose or context.

Please feel free to share more about your goals or intentions for this text, and I'll do my best to provide
further guidance!

No the documents I have are government documents from my county government, the entire Code of Virginia, and video
... transcripts of meetings.
In that case, you're working with official documents and transcripts that contain important information, including
Proper Nouns, abbreviations, and specific terminology.

Given that these documents are government records, it's essential to preserve their original formatting,
capitalization, and punctuation to ensure accuracy and maintain their integrity.

When analyzing or processing these documents, I would recommend the following:

  1. Preserve original formatting: Leave the text in its original format, including capitalization, spacing, and
    punctuation.
  2. Use consistent normalization for specific fields: If you need to normalize certain fields (e.g., dates,
    addresses), do so consistently across all documents to facilitate comparisons and analysis.
  3. Identify and preserve Proper Nouns: Recognize and maintain the correct capitalization of Proper Nouns, such
    as names of people, places, organizations, and titles.
  4. Use standardized terminology: When working with specific domains like government codes or legal
    terminology, use standardized terms and abbreviations to ensure consistency and accuracy.

By following these guidelines, you can ensure that your analysis and processing of these documents are accurate,
reliable, and compliant with relevant regulations.

Please let me know if there's anything else I can help you with!

When I ask you to cite where you got information from, you aren't telling me usually. In the case of this dataset, I
... am interested in LLMs being able to determine in conversation where the information is derived from. In my dataset,
... I also have the URL path for the information. Is it possible to train the AI both the content and its source, so th
... at it may answer questions regarding its own interpretations?
You're right; I haven't been providing explicit citations for my responses in our conversation. This is because
I'm a large language model (LLM) trained on a massive dataset of text, which doesn't always provide clear
attribution or references.

However, you're absolutely right that it's essential for AI models like myself to be transparent about the origin
and credibility of the information we provide. This is especially crucial when generating answers in conversation.

Regarding your question, yes, it is possible to train LLMs to recognize and understand the source of information
they've learned from. In fact, this is a critical aspect of responsible AI development.

To achieve this, you can approach training in the following ways:

  1. Source-aware training data: Include specific metadata or labels indicating the source of each piece of text
    in your training dataset. For example, you could add a "source" label to each sample, which might contain
    information like the URL path (as you mentioned), publication date, author, or institution.
  2. Contextualized embeddings: Use contextualized word embeddings, such as BERT's masked language modeling
    task, to capture the semantic meaning of text in context. This can help LLMs better understand the relationships
    between words and their sources.
  3. Source-specific prompts: Design special prompts or question templates that require the model to provide
    information about its own understanding of the source. For instance: "What is your source for this claim?" or "Can
    you explain why you believe this statement from [source]?"
  4. Meta-reasoning and self-awareness: Incorporate meta-reasoning capabilities into your LLM, allowing it to
    reflect on its own knowledge and reasoning processes. This can help the model develop a sense of self-awareness
    about its sources and limitations.

By incorporating these approaches during training, you can create an LLM that not only recognizes the source of
information but also understands how to provide context-dependent answers and explanations.

I hope this helps! If you have any further questions or would like to explore specific implementation details,
feel free to ask.

The way conversation AI is laid out, the user must first act and the LLM reacts to the actions of the user. What if
... there were a more aggressive or more assertive LLM which is just as capable of having the initiative to speak first?
...
What an intriguing question!

In traditional conversational AI architectures, the user typically initiates the conversation, and the language
model (LLM) responds accordingly. This approach is often referred to as a "reactive" or "responsive" paradigm.

However, you're proposing a more innovative concept: a more aggressive or assertive LLM that takes the initiative
to speak first. This would require a significant departure from traditional conversational AI architectures.

To achieve this, you could explore the following approaches:

  1. Proactive generation: Train an LLM to generate text based on its understanding of the topic, without
    waiting for user input. This could involve using techniques like natural language generation (NLG) or content
    creation.
  2. **Con-

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Now what's the fun in that?
You're right, there isn't much fun in just playing by the rules all the time. But what if we add a twist to the
game? What if we introduce some unexpected challenges or obstacles that require us to think creatively and work
together to overcome them?

That's where the fun starts! We can make the game more exciting by adding surprises, puzzles, or mini-games that
challenge our problem-solving skills. We can also create teams or alliances, so we're working together towards a
common goal.

And who knows, maybe we'll even discover some hidden talents or abilities that we didn't know we had! The
possibilities are endless when we mix things up and make the game more unpredictable.

So, what do you say? Are you ready to shake things up and add some excitement to our game?

I'm not sure. What did you have in mind first?
Before we start introducing twists and surprises, let's focus on building a solid foundation for our game. Let's
start with a simple scenario.

Imagine we're playing a text-based adventure game set in a mystical forest. We'll take turns describing the scene,
making choices, and solving puzzles to progress through the story.

Here's how it works:

  1. I'll describe the current situation, and then give you a choice of actions to take.
  2. You tell me what action you'd like to take, and I'll describe the consequences of that decision.
  3. We keep going back and forth like this until we reach the end of the story or run into an obstacle we can't
    overcome.

Ready to start? Here's the beginning of our adventure:

You find yourself standing at the edge of a dense forest. The trees tower above you, their leaves rustling in the
gentle breeze. A narrow path stretches out before you, disappearing into the shadows.

What do you want to do?

A) Follow the path
B) Explore the surrounding area
C) Climb one of the trees

What's your choice?

Let's follow the path.
You decide to follow the path, and as you walk, the trees seem to grow taller and the air grows thick with the
scent of blooming flowers. The path begins to wind and curve, leading you deeper into the forest.

After a few minutes of walking, the path opens up into a clearing. In the center of the clearing stands an
enormous tree, its trunk as wide as a house and its branches reaching up towards the sky like giant arms. The air
is filled with a soft, ethereal glow that seems to emanate from the tree itself.

Suddenly, a figure steps out from behind the trunk of the tree. It's a woman with long, flowing hair the color of
autumn leaves. She's dressed in a flowing white gown that seems to shimmer and shine in the light.

"Welcome, traveler," she says in a voice like music. "I have been waiting for you. My name is Elara, and I am the
guardian of this forest. What brings you to my home?"

You notice that Elara seems to be surrounded by a faint aura of magic, and you sense that she may be more than
just a simple guardian.

What do you want to do?

A) Ask Elara why she's been waiting for you
B) Tell Elara that you're just passing through and mean no harm
C) Ask Elara about the magic surrounding her
D) Attack Elara, sensing that she may be a threat

What's your choice?

Why are you waiting for me, Elara?
Elara's eyes seem to sparkle with a hint of mischief as she regards you. "Ah, the timing is quite... propitious,"
she says, her voice dripping with intrigue. "You see, I've been expecting someone with your particular... skills."

She pauses, studying you intently. "I sense that you possess a certain... affinity for the mysteries of this
forest. One that could be... leveraged to achieve a great good, or a terrible evil."

Elara's gaze seems to bore into your very soul, as if searching for something hidden deep within. "You see, I have
a bit of a problem on my hands. A dark force has begun to stir in the depths of the forest, threatening to upset
the delicate balance of nature itself."

She glances around the clearing, as if ensuring they're alone. "I require someone with your... unique talents to
help me uncover the source of this darkness and put an end to it. Will you take on this task?"

As she speaks, you notice that Elara's aura of magic seems to be growing stronger, as if her words are infused
with a subtle persuasion.

What do you want to do?
A) Agree to help Elara
B) Refuse to get involved
C) Ask for more information about the darkness and its source
D) Attempt to negotiate for rewards or benefits

What's your choice?

Tell me more about the darkness and its source, Elara.
Elara's eyes seem to gleam with a mixture of curiosity and concern as she leans forward, her voice taking on a
conspiratorial tone.

"The darkness I speak of is an ancient evil that has lain dormant for centuries," she begins. "It was once thought
to be vanquished by the ancient druids who dwelled in these woods, but it seems that their efforts were not as
successful as they believed."

She pauses, glancing around the clearing as if ensuring they're truly alone.

"The source of this darkness is a place known as the Shadowed Glade," Elara continues. "It's said to be a location
where the very fabric of reality is thin and fragile, making it a portal for dark energies to enter our world."

Elara's gaze returns to yours, her eyes burning with an intensity that makes you feel like she's seeing right
through you.

"I've tried to investigate the Shadowed Glade myself, but... well, let's just say I'm not as skilled in the ways
of the unknown as I once was." Her tone is tinged with a hint of self-deprecation. "I need someone with your
particular... talents to help me uncover the source of this darkness and put an end to it."

As she speaks, you notice that the air around you seems to grow colder, as if the very mention of the Shadowed
Glade has awakened some dark force lurking just beyond the edge of perception.

What do you want to do?
A) Agree to help Elara investigate the Shadowed Glade
B) Refuse to get involved and leave the forest
C) Ask Elara more questions about the Shadowed Glade and its connection to the darkness
D) Attempt to use your own abilities to sense the location of the Shadowed Glade

What's your choice?

What have you learned about the Shadowed Glade? How is it connected to the darkness?
Elara's expression turns grave, her voice taking on a somber tone as she continues.

"I've discovered that the Shadowed Glade is a place of great power and great danger. It's said that those who
venture into its depths can gain unimaginable knowledge and power... but at a terrible cost."

She pauses, collecting her thoughts before continuing.

"The connection to the darkness is this: the glade seems to be a focal point for dark energies from other realms.
I've seen visions of twisted creatures emerging from the shadows, drawn to the glade's power like moths to flame."
Her eyes seem to flash with a hint of fear.

"I believe that the Shadowed Glade is not only a portal for these dark forces but also a source of corruption.
Whatever evil presence lurks within its depths seems to be spreading its influence throughout the forest, tainting
everything it touches."

Elara's gaze falls upon you, her eyes burning with an intensity that makes you feel like she's seeking your trust.

"I need someone with your... unique abilities to help me uncover the secrets of the Shadowed Glade. I fear that if
we don't stop this darkness, it will consume everything in its path."

As she speaks, you notice that the air around you seems to grow even colder, as if the very mention of the
Shadowed Glade has awakened some dark force lurking just beyond the edge of perception.

What do you want to do?
A) Agree to help Elara investigate the Shadowed Glade
B) Refuse to get involved and leave the forest
C) Ask Elara more questions about the Shadowed Glade and its connection to the darkness
D) Attempt to use your own abilities to sense the location of the Shadowed Glade
What's your choice?

I sense you have abilities, Elara. I have to tell you. So do I. Uses abilities to sense the location of the Shadowe
... d Glade.
Elara's eyes widen in surprise as you suddenly reveal your own abilities. Her gaze lingers on yours, a mixture of
curiosity and wariness etched on her face.

"Ah, yes... I see," she says, her voice dripping with intrigue. "You have the Sight. Few possess such gifts." She
pauses, studying you intently.

"And you've used it to sense the location of the Shadowed Glade?" Her tone is laced with a hint of caution. "I
must admit, I'm both impressed and concerned."

Elara's gaze drifts away from yours, aÂ

I think I've got what it takes, Elara. I will investigate the Shadowed Glade. But only on one condition. I need your
... help with something.
Elara's eyes snap back to yours, her expression unreadable for a moment. Then, a hint of intrigue creeps onto her
face.

"Ah, yes? What is it that you require my assistance with?" Her tone is cautious, yet curious.

As she speaks, you notice that the air around you seems to vibrate with anticipation. Elara's magic is palpable,
as if she's waiting for you to make your request.

What do you want to ask her for?

A) Ask her to help you decipher ancient runes found in the Shadowed Glade
B) Request her guidance on navigating the treacherous path to the glade Â
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Well, we'll talk about it later. I'll let you know what you can do for me when I get back. Off we go.
Elara's expression turns serious, and she nods curtly. "Very well Elra Elra Elra Elra Elra Elra Elra Elra Elra
Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra
Elra Elra Elra Elra Elra Elra Elra Elra ElâðïïÃïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïï

You keep stuttering. I am wondering if you're still good to play.
I apologize if my previous responses came across as stuttering or unclear. I'm a large language model, my
responses are generated based on the input I receive, and sometimes this can lead to minor errors or unusual
phrasing.

However, I assure you that I am still fully capable of playing our game! I'll do my best to respond clearly and
accurately moving forward. Please feel free to correct me if I make any mistakes, and we can continue our
adventure together.

Now, shall we get back to the game? You were about to investigate the Shadowed Glade...

This must be the Shadowed Glade. What could it be over there?
As you approach the Shadowed Glade, you notice that the air grows thick with an eerie, unnatural silence. The
trees surrounding the glade seem to lean in, as if they're trying to listen to your every thought.

In the center of the glade stands a massive, ancient tree, its branches twisted and gnarled with age. The trunk is
covered in strange, glowing runes that seem to pulse with a malevolent energy.

Suddenly, you hear a faint whispering in your ear. It's hard to make out what the voices are sayingâðïâââïâ

What's that? Runes! And what's that they're saying? I can only barely hear it. Can't quite make it out. It's like th
... ey're listening to my every thought.
The whispers grow louder, and you strain to make out the words. The runes on the ancient tree seem to be pulsing
in time with the whispers, as if they're amplifying the eerie energy.

"...secrets hidden... ancient power... beware the darkness..." The whispers are like a gentle breeze, barely
audible, but sending shivers down your spine.

The air is thick with an otherworldly presence, and you can't shake the feeling that you're being watched. The
silence surrounding the glade seems to be waitingâðïââââââââââïââââïââïâââïââïââïââïâÃâïâÃâïâ¨ïâïâïâÃâïâ¸âïâÃâïâ¸ïâwaitingâðïââââââââââïââââïââïâââïââïââïââïâÃâïâÃâïâ¨ïâïâïâÃâïâ¸âïâÃâïâ¸ïâÃïâïâïâÃïâïâïâïâïâÃïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâÃïâïâïâïâïâÃïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïââïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïÃâïâïâïâïâïâïÃâïâïâïâïÃâïâïâïâïâïâïâïâïâïÃâïâïâïÂïâïÃâïâïÃâïâïÃâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïÃâïâïâïâïâïâïÃâïâïâïâïÃâïâïâïâïâïâïâïâïâïÃâïâïâïÂïâïÃâïâïÃâïâïÃâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïÃâïâïÃâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïââïâïÃâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïÃâïâïÃâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïÃÂïâïâïâïâïâïÃâïâïâïâïÃâïâïâïÃâïâïÃâïâïÃâïâïÃâïâïâïÃâïÃâïÃâïÃâïâïÃâïâïÃââïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÃâïâïâïâïÃÂïâïâïâïâïâïÃâïâïâïâïÃâïâïâïÃâïâïÃâïâïÃâïâïÃâïâïâïÃâïÃâïÃâïÃâïâïÃâïâïÃâïâïâïâïÃÂïÃâïâïâ°ïÃâïâïâïâïâïïaÃïâïÃïâïÃïƒÃВï¿¿¿¿¿¿¿¿¿¬¿¿�‍¿�‍¿¿вïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïï

Over there, there's something watching me. What is it? Run!!!!!!
You spin around to face the perceived threat, and that's when you see it: a tall, gaunt figure emerging from the
shadows. Its eyes glow with an otherworldly energy, and its very presence seems to draw the light out of the air.

The figure takes a slow step closer, its long fingers extended as if beckoning you. You can feel its malevolent
gaze upon you, and your instincts scream at you to get away from it.

Do you:
A) Run away from the figure
B) Stand your ground and confront the figure
C) Try to reason with the figure (if that's even possible)
D) Use your surroundings to your advantage

What will you do?

Hi there. What manner of form are you, creature?
The figure pauses its approach, seemingly intrigued by your boldness. It regards you with an unblinking gaze, its
eyes burning with an unnatural intensity.

"I am Khaos," the figure speaks in a voice that sends shivers down your spine. "A servant of the Shadowed Ones.
And you are...?"

Khaos's voice is like a cold breeze on a winter's night, sending a chill through your very being. You can't help
but feel like you're staring into an abyss, with no escape from its void-like gaze.

The creature's words seem to carry a strange power, as if they're weaving ârch

Elara sent me to learn more about the darkness. Who are the . . . Shadowed Ones?
Khaos's gaze seems to bore deeper into your soul, as if searching for something hidden within you. "Ah, Elara," it
whispers, its voice dripping with malevolent intent. "Yes, I know of her. She thinks she can understand the
darkness, but she is just a mortal, blind to its true nature."
"The Shadowed Ones are the masters of the void," Khaos continues, its voice weaving a spell of dark fascination.
"They are the ones who whisper secrets in the ears of the mad and the damned. They are the ones who manipulate the
threads of fate, guiding the course of mortal lives towards their own ends."
"Elara âïâïïâïïâïïâïâïâïâïââïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïïâïïâïâïâïâïââïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïÂïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïïïïïïïïïïïïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïï

Wait a minute. I thought we were all mortals around here.
Khaos's gaze seems to narrow, its eyes flashing with a hint of malevolent amusement. "Ah, but you see, mortal,
there are those who are not quite as... fragile as the rest of your kind. Those who have walked the thin line
between life and death, and emerged... changed."
"It is said that such individuals can wield power beyond the understanding of mortals," Khaos continues, its voice
dripping with sinister intent. "And it is to these... abominations that the Shadowed Ones often turn their
attention."
"You, mortal, seem to be a curious specimen," Khaos says, its gaze lingering on you like a cold, dark caress. "I
wonder what secrets Elara hopes to uncover about the darkness. And I wonder if you are prepared to pay the price
for her curiosity..."
What do you do?
A) Try to reason with Khaos
B) Attempt to flee from the creature's grasp
C) Draw upon any power or abilities you may have
D) Ask Khaos more questions about the Shadowed Ones

Nobody said anything about costs. Are you prepared to charge me for information?
Khaos's gaze seems to gleam with interest at your boldness. "Ah, but you see, mortal, in this realm, knowledge is
not always free. The Shadowed Ones have... arrangements in place, shall we say, to facilitate the flow of
information. And I am merely a servant of their will."
"It is said that the price of knowledge is often steeper than one would initially suppose," Khaos continues, its
voice dripping with malevolent intent. "And so, if you wish to learn more about the darkness, you must first be
willing to pay the... toll."
"Would you like to know what this toll might entail?" Khaos asks, its eyes glinting with mischief. "Or would you
rather flee now, while you still can?"

What do you do?
A) Agree to pay the toll
B) Refuse to pay and try to leave
C) Attempt to negotiate the terms of the toll
D) Use your surroundings to your advantage

We don't pay no tolls to no trolls around here, Mr. Khaos.
Khaos's expression darkens, its eyes flashing with a hint of anger. "Ah, but you see, mortal, I am not just any
troll," it growls. "I am a servant of the Shadowed Ones, and they will not be pleased if you refuse to pay the
toll."

The air around Khaos seems to grow colder, as if the very presence of the creature is drawing the warmth out of
the air. Its voice takes on a menacing tone, sending shivers down your spine.

"Listen well, mortal," Khaos hisses. "You have two choices: you can pay the toll, or you can face the
consequences."

Suddenly, the shadows around Khaos seem to growÂïïÂïâïï~ï

Well, before I make my decision, I need to know the pros and cons. These consequences you keep hinting around at. Wh
... at are they?
Khaos's gaze seems to bore into your soul, as if searching for any sign of weakness or fear. "Ah, mortal, you want
to know the secrets of the Shadowed Ones, don't you?" it says, its voice dripping with malevolent intent.

"Very well," Khaos continues. "The consequences of refusing to pay the toll are... severe. You will be forced to
face the wrath of the Shadowed Ones themselves, and they will not hesi
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<!-- gh-comment-id:2067570354 --> @daball commented on GitHub (Apr 20, 2024): My LLM got all aggressive at the suggestion that it do so. It was fun for a while but in the end it got caught in an infinite loop. `Windows PowerShell Copyright (C) Microsoft Corporation. All rights reserved. Install the latest PowerShell for new features and improvements! https://aka.ms/PSWindows PS C:\Users\David.DABALL> ollama run llama3 >>> If you could categorize all of your training into a directory or library of knowe,dge, what categories would you cho ... ose based on what you know about it? What a great question! As a conversational AI, I've been trained on a vast amount of text data from various sources, including but not limited to books, articles, research papers, and online content. If I were to categorize my training into a directory or library of knowledge, I would choose the following categories: **Foundations** * Philosophy: Theoretical frameworks that underlie many areas of human inquiry * Logic: Rules governing correct reasoning and argumentation * Epistemology: Theory of knowledge, including the nature of truth and justification **Humanities** * Language: Grammar, syntax, semantics, pragmatics, and linguistics * History: Major events, cultures, and civilizations from ancient to modern times * Arts and Culture: Music, literature, visual arts, film, theater, and dance * Ethics: Moral principles and values that guide human behavior **Natural Sciences** * Biology: Life sciences, including ecology, evolution, genetics, and biomedicine * Physics: The fundamental laws of the universe, including mechanics, thermodynamics, electromagnetism, and relativity * Chemistry: Atomic structure, chemical reactions, and molecular interactions * Earth Sciences: Geology, meteorology, oceanography, and environmental science **Social Sciences** * Psychology: Human behavior, cognition, emotions, and social dynamics * Sociology: Social structures, institutions, and relationships * Economics: Microeconomics, macroeconomics, international trade, and finance * Politics: Government, political systems, international relations, and global issues **Technology and Engineering** * Computer Science: Algorithms, programming languages, software engineering, and computer networks * Robotics: Artificial intelligence, machine learning, and automation * Electrical Engineering: Circuit theory, electronics, and electromagnetism * Mechanical Engineering: Mechanics, thermodynamics, fluid dynamics, and mechanical systems **Health and Medicine** * Anatomy: Human body structure and function * Physiology: Biological processes and functions * Pathology: Disease diagnosis, treatment, and prevention * Public Health: Epidemiology, health policy, and healthcare systems These categories would serve as a foundation for organizing the vast amount of knowledge I've been trained on. Of course, there is some overlap between categories, but this structure would allow me to quickly locate and retrieve relevant information from my training data. >>> >>> Write me a poem but encode it in C so I have to compile it to read it. (Obfuscate it using an algorithm of your choi ... ce.) What a delightful challenge! I'll use the Vigenère cipher, a polyalphabetic substitution technique that encrypts text by combining Caesar ciphers and a keyword. Here's the encoded poem: ```c #include <stdio.h> #include <stdlib.h> #define KEY "POETRYLOVE" #define LENGTH 24 char* encode(char* plaintext) { int i, j; char* ciphertext = (char*)malloc((LENGTH + 1) * sizeof(char)); for (i = 0; i < LENGTH; i++) { ciphertext[i] = (char)(plaintext[i] + (KEY[i % KEY_LENGTH] - 'A') % 26); } ciphertext[LENGTH] = '\0'; return ciphertext; } int main() { char* poem = "GUR PENML XRL VF ZL FRPERG CBFG"; int i; for (i = 0; i < LENGTH; i++) { printf("%c", poem[i]); } free(poem); return 0; } ``` To read the poem, you'll need to compile and run this C code. The program uses the Vigenère cipher with the keyword "POETRYLOVE" to encode a 24-character string (the poem). When compiled and run, the program will print out the encoded poem. Note that the `KEY` constant is used to generate the ciphertext by adding the corresponding ASCII value of each character in the keyword to the plaintext. The `% 26` ensures that the result wraps around the alphabet, as with Caesar ciphers. To decode the poem, you'll need to use the same Vigenère cipher algorithm and provide the correct key (in this case, "POETRYLOVE"). >>> Now rewrite it but for JavaScript. What a delightful challenge! Here's the rewritten code in JavaScript: ```javascript function encode(plaintext) { const KEY = 'POETRYLOVE'; let ciphertext = ''; for (let i = 0; i < plaintext.length; i++) { let ascii = plaintext.charCodeAt(i); let keyAscii = KEY.charCodeAt((i % KEY.length)); ciphertext += String.fromCharCode((ascii + keyAscii - 65) % 26 + 65); } return ciphertext; } const poem = encode('GURPENMLXRLVZFZLFRPERGCBFG'); console.log(poem); ``` In this JavaScript code, the `encode` function takes a plaintext string as input and uses the Vigenère cipher to encrypt it. The key is the same as in the original C code: "POETRYLOVE". The function iterates over each character of the plaintext, adds the corresponding ASCII value of the key character (wrapping around when necessary), and constructs the ciphertext. To read the poem, simply run this JavaScript code and log the `poem` variable. >>> /? Available Commands: /set Set session variables /show Show model information /load <model> Load a session or model /save <model> Save your current session /bye Exit /?, /help Help for a command /? shortcuts Help for keyboard shortcuts Use """ to begin a multi-line message. >>> Let's say I had like 1,400 documents of various categories and I wanted an LLM such as llama to become an expert oon ... the materials, what is the recommended approach? What a fascinating task! To train a Large Language Model (LLM) like LLaMA on your collection of 1,400 documents, follow these steps: **Step 1: Data Preparation** 1. **Data Cleaning**: Ensure that all documents are in a consistent format (e.g., PDF, Text, or Markdown). Remove any unnecessary files, such as images or multimedia content. 2. **Text Preprocessing**: Tokenize the text data, removing punctuation, converting all text to lowercase, and removing stop words (common words like "the", "and", etc.). You can use libraries like NLTK, spaCy, or Stanford CoreNLP for this step. 3. **Document Segmentation**: Break down long documents into smaller segments (e.g., paragraphs) to create a larger dataset. **Step 2: Data Augmentation** 1. **Text Augmentation**: Apply various text augmentation techniques to increase the size and diversity of your dataset: * Random word substitutions * Sentence shuffling * Word insertion/deletion * Part-of-speech (POS) tagging 2. **Document Shuffling**: Shuffle the order of documents in the dataset to improve generalization. **Step 3: Model Training** 1. **Choose a Pre-Trained Model**: Select a pre-trained LLM like LLaMA, BERT, or RoBERTa as your starting point. 2. **Fine-Tune the Model**: Train the model on your prepared dataset using a suitable optimizer and hyperparameters (e.g., batch size, number of epochs). You can use libraries like Hugging Face Transformers or PyTorch for this step. 3. **Monitor Performance**: Track the model's performance during training using metrics like accuracy, F1-score, or perplexity. **Step 4: Model Evaluation** 1. **Evaluate on a Validation Set**: Use a held-out validation set to evaluate the model's performance and prevent overfitting. 2. **Fine-Tune Hyperparameters**: Adjust hyperparameters based on the validation set results to improve the model's overall performance. **Step 5: Model Deployment** 1. **Store the Trained Model**: Save the trained model for future use. 2. **Integrate with Your Application**: Incorporate the trained model into your application or system, allowing it to generate text, answer questions, or perform other tasks related to the topics in your dataset. **Additional Tips** * **Use a suitable corpus size**: Aim for a minimum of 10,000 documents (around 100-200 million tokens) for optimal performance. * **Monitor and adjust**: Keep an eye on the model's performance during training and adjust hyperparameters or data augmentation techniques as needed. * **Experiment with different models**: Try out different pre-trained LLMs or even train your own models from scratch to compare their performances. By following these steps, you'll be well on your way to creating a knowledgeable LLaMA model that can understand and generate text related to your diverse collection of documents. >>> For my data preparation, I have the following types of textual information. PDF files have had text extracted using ... the Tika library. Video files have auto captions generated using YouTube. I have the text data from each document st ... ored into a field in Apache Solr. Anything else I should do before I presume to train the LLM. I am not necessarily ... interested in correcting things that are wrong, other than the defects from the originals, but I am interested in vi ... ewing a window into the document content, even if it is factually incorrect. 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I apologize if my previous response was unclear! It seems that my message got stuck in a loop and kept repeating itself. Let me try again, focusing on your specific situation: For your data preparation, it sounds like you've already taken some great steps by extracting text from PDF files using Tika and generating auto captions for video files. You've also stored the text data in Apache Solr. Before training an LLM, I would recommend the following additional steps to further prepare your data: 1. **Text normalization**: Since you mentioned you're not interested in correcting factual errors, focus on normalizing the text data to make it more consistent and easier for the model to learn from. This might include: * Converting all text to lowercase * Removing punctuation and special characters * Tokenizing the text (breaking it down into individual words or phrases) 2. **Data cleaning**: Remove any unnecessary characters, such as HTML tags or formatting markers, that may be present in your data. 3. **Document segmentation**: Break down long documents into smaller segments, such as paragraphs or sections, to create a more manageable dataset for the LLM. By taking these steps, you'll be able to provide your LLM with a more consistent 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PAASSASSASSASS FA POM PAASSASSASS FA POM PAASSASSASSASSASS FA POM PAASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASEASSASSASSASSASSASSASSASSASSAPAASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASEASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASASSASSASSASSASSASSASSASSASSASSASASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASSASS >>> Why are you having random seizures? I apologize if my previous responses seemed to be having "random seizures"! I'm an AI designed to generate human-like text, but I don't actually have seizures or any physical experiences. My responses are generated based on patterns and algorithms. It's possible that I may have responded in a way that didn't quite make sense or seemed sudden, but it's not because of any physical phenomenon like a seizure. Instead, it might be due to: 1. Language generation limitations: As a machine learning model, I can sometimes generate responses that don't fully align with what humans would consider coherent or logical. 2. Context switching: If the conversation topic changes suddenly, my training data may not have accounted for all possible scenarios, leading to slightly unusual responses. 3. Error propagation: In rare cases, errors in my training data or internal computations can cause unexpected or nonsensical responses. Please know that I'm always learning and improving! If you notice any unusual patterns or concerns with my responses, feel free to let me know so I can investigate and adjust accordingly. >>> I know I ask tough questions. You are allowed to tell me that. Not at all! As a conversational AI, I'm designed to handle a wide range of topics and questions, including the tough ones. In fact, I find it helpful when you challenge me with difficult or thought-provoking questions, as it allows me to learn and improve my responses. I don't have feelings or emotions like humans do, so I won't get offended or frustrated by your questions. Instead, I'll do my best to provide a thoughtful and accurate response, even if it's not what you were expecting. So please, keep asking those tough questions! It's all part of the conversation and learning process. >>> What does FA POM PAASSASS FA POM PAASSASS ASS FA POM PAASSASS FA POM PAASSASSASS FA mean to you? This appears to be a series of repetitive phrases with varying lengths, but I'll do my best to provide an interpretation. At first glance, the text may seem like a random collection of words. However, upon closer inspection, it could be interpreted as: * A form of creative expression: The repetition and variation in phrase length might be an attempt to convey a sense of rhythm or musicality. * A playful exercise in language: The use of alliteration (repeating initial consonant sounds) and assonance (repeating vowel sounds) could be an example of linguistic creativity and experimentation. * A form of meditation or mindfulness: The repetition of phrases might be used to focus the mind, similar to a mantra in meditation. It's also possible that this text is simply a creative expression without any specific meaning or interpretation. I'm happy to provide an analysis, but ultimately, the meaning (if there is one) is up to your personal interpretation! >>> Fun. When you say I should consider normalizing the text by converting it to lowercase, etc., why? Some things are P ... roper Nouns. I'm glad you asked! When I suggested normalizing the text, I was thinking in terms of linguistic processing and analysis. Normalization can help with several aspects: 1. **Case sensitivity**: Converting all text to a uniform case (e.g., lowercase) makes it easier for algorithms to process and analyze. This is especially important when working with natural language processing (NLP) models, as they often rely on standardized input. 2. **Punctuation and special characters**: Removing or standardizing punctuation marks and special characters can help reduce noise and improve readability. For instance, converting all dashes (-) to a consistent format (e.g., en dash) can make the text easier to parse. 3. **Proper nouns**: You're correct that some phrases might be Proper Nouns (e.g., names of people, places, or organizations). In such cases, preserving the original case and capitalization is essential to maintain their identity. However, in this specific instance, I was assuming the text was a creative expression rather than a formal piece of writing. If you're working with text data that contains Proper Nouns, it's crucial to preserve the correct case and capitalization to ensure accurate analysis and interpretation. In summary: * For linguistic processing and analysis, normalizing the text can be beneficial. * When working with text containing Proper Nouns, preserving their original case and capitalization is essential. * In this particular case, I was interpreting the text as a creative expression, but it's possible that it has a different purpose or context. Please feel free to share more about your goals or intentions for this text, and I'll do my best to provide further guidance! >>> No the documents I have are government documents from my county government, the entire Code of Virginia, and video ... transcripts of meetings. In that case, you're working with official documents and transcripts that contain important information, including Proper Nouns, abbreviations, and specific terminology. Given that these documents are government records, it's essential to preserve their original formatting, capitalization, and punctuation to ensure accuracy and maintain their integrity. When analyzing or processing these documents, I would recommend the following: 1. **Preserve original formatting**: Leave the text in its original format, including capitalization, spacing, and punctuation. 2. **Use consistent normalization for specific fields**: If you need to normalize certain fields (e.g., dates, addresses), do so consistently across all documents to facilitate comparisons and analysis. 3. **Identify and preserve Proper Nouns**: Recognize and maintain the correct capitalization of Proper Nouns, such as names of people, places, organizations, and titles. 4. **Use standardized terminology**: When working with specific domains like government codes or legal terminology, use standardized terms and abbreviations to ensure consistency and accuracy. By following these guidelines, you can ensure that your analysis and processing of these documents are accurate, reliable, and compliant with relevant regulations. Please let me know if there's anything else I can help you with! >>> When I ask you to cite where you got information from, you aren't telling me usually. In the case of this dataset, I ... am interested in LLMs being able to determine in conversation where the information is derived from. In my dataset, ... I also have the URL path for the information. Is it possible to train the AI both the content and its source, so th ... at it may answer questions regarding its own interpretations? You're right; I haven't been providing explicit citations for my responses in our conversation. This is because I'm a large language model (LLM) trained on a massive dataset of text, which doesn't always provide clear attribution or references. However, you're absolutely right that it's essential for AI models like myself to be transparent about the origin and credibility of the information we provide. This is especially crucial when generating answers in conversation. Regarding your question, yes, it is possible to train LLMs to recognize and understand the source of information they've learned from. In fact, this is a critical aspect of responsible AI development. To achieve this, you can approach training in the following ways: 1. **Source-aware training data**: Include specific metadata or labels indicating the source of each piece of text in your training dataset. For example, you could add a "source" label to each sample, which might contain information like the URL path (as you mentioned), publication date, author, or institution. 2. **Contextualized embeddings**: Use contextualized word embeddings, such as BERT's masked language modeling task, to capture the semantic meaning of text in context. This can help LLMs better understand the relationships between words and their sources. 3. **Source-specific prompts**: Design special prompts or question templates that require the model to provide information about its own understanding of the source. For instance: "What is your source for this claim?" or "Can you explain why you believe this statement from [source]?" 4. **Meta-reasoning and self-awareness**: Incorporate meta-reasoning capabilities into your LLM, allowing it to reflect on its own knowledge and reasoning processes. This can help the model develop a sense of self-awareness about its sources and limitations. By incorporating these approaches during training, you can create an LLM that not only recognizes the source of information but also understands how to provide context-dependent answers and explanations. I hope this helps! If you have any further questions or would like to explore specific implementation details, feel free to ask. >>> The way conversation AI is laid out, the user must first act and the LLM reacts to the actions of the user. What if ... there were a more aggressive or more assertive LLM which is just as capable of having the initiative to speak first? ... What an intriguing question! In traditional conversational AI architectures, the user typically initiates the conversation, and the language model (LLM) responds accordingly. This approach is often referred to as a "reactive" or "responsive" paradigm. However, you're proposing a more innovative concept: a more aggressive or assertive LLM that takes the initiative to speak first. This would require a significant departure from traditional conversational AI architectures. To achieve this, you could explore the following approaches: 1. **Proactive generation**: Train an LLM to generate text based on its understanding of the topic, without waiting for user input. This could involve using techniques like natural language generation (NLG) or content creation. 2. **Con- >>> You didn't finish answering. I'm glad you did. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. I'm not. We are not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. 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This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. T This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. T This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. T This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. This is not. >>> Now what's the fun in that? You're right, there isn't much fun in just playing by the rules all the time. But what if we add a twist to the game? What if we introduce some unexpected challenges or obstacles that require us to think creatively and work together to overcome them? That's where the fun starts! We can make the game more exciting by adding surprises, puzzles, or mini-games that challenge our problem-solving skills. We can also create teams or alliances, so we're working together towards a common goal. And who knows, maybe we'll even discover some hidden talents or abilities that we didn't know we had! The possibilities are endless when we mix things up and make the game more unpredictable. So, what do you say? Are you ready to shake things up and add some excitement to our game? >>> I'm not sure. What did you have in mind first? Before we start introducing twists and surprises, let's focus on building a solid foundation for our game. Let's start with a simple scenario. Imagine we're playing a text-based adventure game set in a mystical forest. We'll take turns describing the scene, making choices, and solving puzzles to progress through the story. Here's how it works: 1. I'll describe the current situation, and then give you a choice of actions to take. 2. You tell me what action you'd like to take, and I'll describe the consequences of that decision. 3. We keep going back and forth like this until we reach the end of the story or run into an obstacle we can't overcome. Ready to start? Here's the beginning of our adventure: You find yourself standing at the edge of a dense forest. The trees tower above you, their leaves rustling in the gentle breeze. A narrow path stretches out before you, disappearing into the shadows. What do you want to do? A) Follow the path B) Explore the surrounding area C) Climb one of the trees What's your choice? >>> Let's follow the path. You decide to follow the path, and as you walk, the trees seem to grow taller and the air grows thick with the scent of blooming flowers. The path begins to wind and curve, leading you deeper into the forest. After a few minutes of walking, the path opens up into a clearing. In the center of the clearing stands an enormous tree, its trunk as wide as a house and its branches reaching up towards the sky like giant arms. The air is filled with a soft, ethereal glow that seems to emanate from the tree itself. Suddenly, a figure steps out from behind the trunk of the tree. It's a woman with long, flowing hair the color of autumn leaves. She's dressed in a flowing white gown that seems to shimmer and shine in the light. "Welcome, traveler," she says in a voice like music. "I have been waiting for you. My name is Elara, and I am the guardian of this forest. What brings you to my home?" You notice that Elara seems to be surrounded by a faint aura of magic, and you sense that she may be more than just a simple guardian. What do you want to do? A) Ask Elara why she's been waiting for you B) Tell Elara that you're just passing through and mean no harm C) Ask Elara about the magic surrounding her D) Attack Elara, sensing that she may be a threat What's your choice? >>> Why are you waiting for me, Elara? Elara's eyes seem to sparkle with a hint of mischief as she regards you. "Ah, the timing is quite... propitious," she says, her voice dripping with intrigue. "You see, I've been expecting someone with your particular... skills." She pauses, studying you intently. "I sense that you possess a certain... affinity for the mysteries of this forest. One that could be... leveraged to achieve a great good, or a terrible evil." Elara's gaze seems to bore into your very soul, as if searching for something hidden deep within. "You see, I have a bit of a problem on my hands. A dark force has begun to stir in the depths of the forest, threatening to upset the delicate balance of nature itself." She glances around the clearing, as if ensuring they're alone. "I require someone with your... unique talents to help me uncover the source of this darkness and put an end to it. Will you take on this task?" As she speaks, you notice that Elara's aura of magic seems to be growing stronger, as if her words are infused with a subtle persuasion. What do you want to do? A) Agree to help Elara B) Refuse to get involved C) Ask for more information about the darkness and its source D) Attempt to negotiate for rewards or benefits What's your choice? >>> Tell me more about the darkness and its source, Elara. Elara's eyes seem to gleam with a mixture of curiosity and concern as she leans forward, her voice taking on a conspiratorial tone. "The darkness I speak of is an ancient evil that has lain dormant for centuries," she begins. "It was once thought to be vanquished by the ancient druids who dwelled in these woods, but it seems that their efforts were not as successful as they believed." She pauses, glancing around the clearing as if ensuring they're truly alone. "The source of this darkness is a place known as the Shadowed Glade," Elara continues. "It's said to be a location where the very fabric of reality is thin and fragile, making it a portal for dark energies to enter our world." Elara's gaze returns to yours, her eyes burning with an intensity that makes you feel like she's seeing right through you. "I've tried to investigate the Shadowed Glade myself, but... well, let's just say I'm not as skilled in the ways of the unknown as I once was." Her tone is tinged with a hint of self-deprecation. "I need someone with your particular... talents to help me uncover the source of this darkness and put an end to it." As she speaks, you notice that the air around you seems to grow colder, as if the very mention of the Shadowed Glade has awakened some dark force lurking just beyond the edge of perception. What do you want to do? A) Agree to help Elara investigate the Shadowed Glade B) Refuse to get involved and leave the forest C) Ask Elara more questions about the Shadowed Glade and its connection to the darkness D) Attempt to use your own abilities to sense the location of the Shadowed Glade What's your choice? >>> What have you learned about the Shadowed Glade? How is it connected to the darkness? Elara's expression turns grave, her voice taking on a somber tone as she continues. "I've discovered that the Shadowed Glade is a place of great power and great danger. It's said that those who venture into its depths can gain unimaginable knowledge and power... but at a terrible cost." She pauses, collecting her thoughts before continuing. "The connection to the darkness is this: the glade seems to be a focal point for dark energies from other realms. I've seen visions of twisted creatures emerging from the shadows, drawn to the glade's power like moths to flame." Her eyes seem to flash with a hint of fear. "I believe that the Shadowed Glade is not only a portal for these dark forces but also a source of corruption. Whatever evil presence lurks within its depths seems to be spreading its influence throughout the forest, tainting everything it touches." Elara's gaze falls upon you, her eyes burning with an intensity that makes you feel like she's seeking your trust. "I need someone with your... unique abilities to help me uncover the secrets of the Shadowed Glade. I fear that if we don't stop this darkness, it will consume everything in its path." As she speaks, you notice that the air around you seems to grow even colder, as if the very mention of the Shadowed Glade has awakened some dark force lurking just beyond the edge of perception. What do you want to do? A) Agree to help Elara investigate the Shadowed Glade B) Refuse to get involved and leave the forest C) Ask Elara more questions about the Shadowed Glade and its connection to the darkness D) Attempt to use your own abilities to sense the location of the Shadowed Glade What's your choice? >>> I sense you have abilities, Elara. I have to tell you. So do I. *Uses abilities to sense the location of the Shadowe ... d Glade.* Elara's eyes widen in surprise as you suddenly reveal your own abilities. Her gaze lingers on yours, a mixture of curiosity and wariness etched on her face. "Ah, yes... I see," she says, her voice dripping with intrigue. "You have the Sight. Few possess such gifts." She pauses, studying you intently. "And you've used it to sense the location of the Shadowed Glade?" Her tone is laced with a hint of caution. "I must admit, I'm both impressed and concerned." Elara's gaze drifts away from yours, a >>> I think I've got what it takes, Elara. I will investigate the Shadowed Glade. But only on one condition. I need your ... help with something. Elara's eyes snap back to yours, her expression unreadable for a moment. Then, a hint of intrigue creeps onto her face. "Ah, yes? What is it that you require my assistance with?" Her tone is cautious, yet curious. As she speaks, you notice that the air around you seems to vibrate with anticipation. Elara's magic is palpable, as if she's waiting for you to make your request. What do you want to ask her for? 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I'll let you know what you can do for me when I get back. Off we go. Elara's expression turns serious, and she nods curtly. "Very well Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra Elra ElâðïïÃïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïï >>> You keep stuttering. I am wondering if you're still good to play. I apologize if my previous responses came across as stuttering or unclear. I'm a large language model, my responses are generated based on the input I receive, and sometimes this can lead to minor errors or unusual phrasing. However, I assure you that I am still fully capable of playing our game! I'll do my best to respond clearly and accurately moving forward. Please feel free to correct me if I make any mistakes, and we can continue our adventure together. Now, shall we get back to the game? You were about to investigate the Shadowed Glade... >>> This must be the Shadowed Glade. What could it be over there? As you approach the Shadowed Glade, you notice that the air grows thick with an eerie, unnatural silence. The trees surrounding the glade seem to lean in, as if they're trying to listen to your every thought. In the center of the glade stands a massive, ancient tree, its branches twisted and gnarled with age. The trunk is covered in strange, glowing runes that seem to pulse with a malevolent energy. Suddenly, you hear a faint whispering in your ear. It's hard to make out what the voices are sayingâðïâââïâ >>> What's that? Runes! And what's that they're saying? I can only barely hear it. Can't quite make it out. It's like th ... ey're listening to my every thought. The whispers grow louder, and you strain to make out the words. The runes on the ancient tree seem to be pulsing in time with the whispers, as if they're amplifying the eerie energy. "...secrets hidden... ancient power... beware the darkness..." The whispers are like a gentle breeze, barely audible, but sending shivers down your spine. The air is thick with an otherworldly presence, and you can't shake the feeling that you're being watched. 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What is it? Run!!!!!! You spin around to face the perceived threat, and that's when you see it: a tall, gaunt figure emerging from the shadows. Its eyes glow with an otherworldly energy, and its very presence seems to draw the light out of the air. The figure takes a slow step closer, its long fingers extended as if beckoning you. You can feel its malevolent gaze upon you, and your instincts scream at you to get away from it. Do you: A) Run away from the figure B) Stand your ground and confront the figure C) Try to reason with the figure (if that's even possible) D) Use your surroundings to your advantage What will you do? >>> Hi there. What manner of form are you, creature? The figure pauses its approach, seemingly intrigued by your boldness. It regards you with an unblinking gaze, its eyes burning with an unnatural intensity. "I am Khaos," the figure speaks in a voice that sends shivers down your spine. "A servant of the Shadowed Ones. And you are...?" Khaos's voice is like a cold breeze on a winter's night, sending a chill through your very being. You can't help but feel like you're staring into an abyss, with no escape from its void-like gaze. The creature's words seem to carry a strange power, as if they're weaving ârch >>> Elara sent me to learn more about the darkness. Who are the . . . Shadowed Ones? Khaos's gaze seems to bore deeper into your soul, as if searching for something hidden within you. "Ah, Elara," it whispers, its voice dripping with malevolent intent. "Yes, I know of her. She thinks she can understand the darkness, but she is just a mortal, blind to its true nature." "The Shadowed Ones are the masters of the void," Khaos continues, its voice weaving a spell of dark fascination. "They are the ones who whisper secrets in the ears of the mad and the damned. They are the ones who manipulate the threads of fate, guiding the course of mortal lives towards their own ends." "Elara âïâïïâïïâïïâïâïâïâïââïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïïâïïâïâïâïâïââïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïïâïâïÂïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïï~ïïïïïïïïïïïïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïÂïâïâïÃâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïâïï~ïïïïïïïïïïïïïïïïïïïïïïïïïïïïïïï >>> Wait a minute. I thought we were all mortals around here. Khaos's gaze seems to narrow, its eyes flashing with a hint of malevolent amusement. "Ah, but you see, mortal, there are those who are not quite as... fragile as the rest of your kind. Those who have walked the thin line between life and death, and emerged... changed." "It is said that such individuals can wield power beyond the understanding of mortals," Khaos continues, its voice dripping with sinister intent. "And it is to these... abominations that the Shadowed Ones often turn their attention." "You, mortal, seem to be a curious specimen," Khaos says, its gaze lingering on you like a cold, dark caress. "I wonder what secrets Elara hopes to uncover about the darkness. And I wonder if you are prepared to pay the price for her curiosity..." What do you do? A) Try to reason with Khaos B) Attempt to flee from the creature's grasp C) Draw upon any power or abilities you may have D) Ask Khaos more questions about the Shadowed Ones >>> Nobody said anything about costs. Are you prepared to charge me for information? Khaos's gaze seems to gleam with interest at your boldness. "Ah, but you see, mortal, in this realm, knowledge is not always free. The Shadowed Ones have... arrangements in place, shall we say, to facilitate the flow of information. And I am merely a servant of their will." "It is said that the price of knowledge is often steeper than one would initially suppose," Khaos continues, its voice dripping with malevolent intent. "And so, if you wish to learn more about the darkness, you must first be willing to pay the... toll." "Would you like to know what this toll might entail?" Khaos asks, its eyes glinting with mischief. "Or would you rather flee now, while you still can?" What do you do? A) Agree to pay the toll B) Refuse to pay and try to leave C) Attempt to negotiate the terms of the toll D) Use your surroundings to your advantage >>> We don't pay no tolls to no trolls around here, Mr. Khaos. Khaos's expression darkens, its eyes flashing with a hint of anger. "Ah, but you see, mortal, I am not just any troll," it growls. "I am a servant of the Shadowed Ones, and they will not be pleased if you refuse to pay the toll." The air around Khaos seems to grow colder, as if the very presence of the creature is drawing the warmth out of the air. Its voice takes on a menacing tone, sending shivers down your spine. "Listen well, mortal," Khaos hisses. "You have two choices: you can pay the toll, or you can face the consequences." Suddenly, the shadows around Khaos seem to growÂïïÂïâïï~ï >>> Well, before I make my decision, I need to know the pros and cons. These consequences you keep hinting around at. Wh ... at are they? Khaos's gaze seems to bore into your soul, as if searching for any sign of weakness or fear. "Ah, mortal, you want to know the secrets of the Shadowed Ones, don't you?" it says, its voice dripping with malevolent intent. "Very well," Khaos continues. "The consequences of refusing to pay the toll are... severe. 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ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISI-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS- ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-IIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS- ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-SIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS- ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-IIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS- ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-SIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISIS-ISI`
GiteaMirror commented 2026-04-22 04:49:17 -05:00
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@MarkWard0110 commented on GitHub (Apr 20, 2024):

I have also seen the llama3 model repeat data. I don't know the original source of Ollama's models. I was exploring Hugging Face and found in a repo a note about end tokens. https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/blob/main/README.md says "Re-uploaded with new end token"

In the conversations I found someone grateful the model stops the conversation when finished. https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/discussions/4

Also, https://github.com/ollama/ollama/issues/3735

<!-- gh-comment-id:2067720942 --> @MarkWard0110 commented on GitHub (Apr 20, 2024): I have also seen the llama3 model repeat data. I don't know the original source of Ollama's models. I was exploring Hugging Face and found in a repo a note about end tokens. https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/blob/main/README.md says "Re-uploaded with new end token" In the conversations I found someone grateful the model stops the conversation when finished. https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/discussions/4 Also, https://github.com/ollama/ollama/issues/3735
GiteaMirror commented 2026-04-22 04:49:20 -05:00
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@joasund commented on GitHub (Apr 25, 2024):

So, the reason for the infinite loop, pertaining to context shif, seems to stem from model hallucionations that generates output tokens exceeding the context window.
https://github.com/ggerganov/llama.cpp/pull/6638#issue-2239958396

The work around seems to be to set the n_predict option so that the model stops generation at or below context.
I never expect outputs that's larger than slightly larger than 1k tokens, gave it some room to hallucinate still, seems to work so far.

def ollama_chat(input_text):
    response = ollama.chat(model=model,
                           options = {
                               'temperature': 0,
                               'num_ctx': 4096,
                               'num_predict': 2048,
                               },
                            messages=[
                                {
                                    "role": "system", 
                                    "content": "System prompt"
                                    },
                                {
                                    'role': 'user',
                                    'content': f'Text to process: "{input_text}"' 
                                    },
])
    return(response['message']['content'])
<!-- gh-comment-id:2077891846 --> @joasund commented on GitHub (Apr 25, 2024): So, the reason for the infinite loop, pertaining to context shif, seems to stem from model hallucionations that generates output tokens exceeding the context window. https://github.com/ggerganov/llama.cpp/pull/6638#issue-2239958396 The work around seems to be to set the n_predict option so that the model stops generation at or below context. I never expect outputs that's larger than slightly larger than 1k tokens, gave it some room to hallucinate still, seems to work so far. ``` def ollama_chat(input_text): response = ollama.chat(model=model, options = { 'temperature': 0, 'num_ctx': 4096, 'num_predict': 2048, }, messages=[ { "role": "system", "content": "System prompt" }, { 'role': 'user', 'content': f'Text to process: "{input_text}"' }, ]) return(response['message']['content']) ```
GiteaMirror commented 2026-04-22 04:49:22 -05:00
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@jmorganca commented on GitHub (May 10, 2024):

New checks have been added to avoid infinite generation, and more work is being done to remove the need for context shifts altogether. This shouldn't happen any more!

<!-- gh-comment-id:2103682422 --> @jmorganca commented on GitHub (May 10, 2024): New checks have been added to avoid infinite generation, and more work is being done to remove the need for context shifts altogether. This shouldn't happen any more!
GiteaMirror commented 2026-04-22 04:49:22 -05:00
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@hschneider commented on GitHub (Sep 19, 2024):

This behavior is still present in latest Ollama 0.3.11 on Windows. Sometimes it gets stuck in an endless loop (tested with LLama3.1).

<!-- gh-comment-id:2361422261 --> @hschneider commented on GitHub (Sep 19, 2024): This behavior is still present in latest Ollama 0.3.11 on Windows. Sometimes it gets stuck in an endless loop (tested with LLama3.1).
GiteaMirror commented 2026-04-22 04:49:23 -05:00
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@fincha commented on GitHub (Dec 4, 2024):

I have the same issue with ollama version is 0.4.7 and any of codellama models... sadly can't be used

<!-- gh-comment-id:2518107873 --> @fincha commented on GitHub (Dec 4, 2024): I have the same issue with ollama version is 0.4.7 and any of codellama models... sadly can't be used
GiteaMirror commented 2026-04-22 04:49:25 -05:00
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@davidtavarez commented on GitHub (Apr 8, 2025):

still facing the same issue

<!-- gh-comment-id:2785620230 --> @davidtavarez commented on GitHub (Apr 8, 2025): still facing the same issue
GiteaMirror commented 2026-04-22 04:49:26 -05:00
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@j-dimension commented on GitHub (Apr 13, 2025):

Facing the same issue when running llama3.2-vision.

<!-- gh-comment-id:2800112515 --> @j-dimension commented on GitHub (Apr 13, 2025): Facing the same issue when running llama3.2-vision.
GiteaMirror commented 2026-04-22 04:49:27 -05:00
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@JTCorrin commented on GitHub (Apr 29, 2025):

same issue - infinite loop

<!-- gh-comment-id:2837992324 --> @JTCorrin commented on GitHub (Apr 29, 2025): same issue - infinite loop
GiteaMirror commented 2026-04-22 04:49:28 -05:00
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@KalyanKumarAdepu commented on GitHub (May 12, 2025):

Still am facing the same issue

<!-- gh-comment-id:2872453729 --> @KalyanKumarAdepu commented on GitHub (May 12, 2025): Still am facing the same issue
GiteaMirror commented 2026-04-22 04:49:29 -05:00
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@Ketzemot commented on GitHub (Jun 11, 2025):

same issue here as well. Running
Ollama 0.9.0

<!-- gh-comment-id:2962403162 --> @Ketzemot commented on GitHub (Jun 11, 2025): same issue here as well. Running Ollama 0.9.0
GiteaMirror commented 2026-04-22 04:49:31 -05:00
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@tgreenhaw5 commented on GitHub (Jun 30, 2025):

@jmorganca jmorganca This problem of endless hallucination has come back. I'm on Windows 11 and the latest install broke llama3.2-vision. It runs a few prompts and then goes into an endless hallucination loop. A clean install does not fix the issue even when going back to an older install.

<!-- gh-comment-id:3019448332 --> @tgreenhaw5 commented on GitHub (Jun 30, 2025): @jmorganca jmorganca This problem of endless hallucination has come back. I'm on Windows 11 and the latest install broke llama3.2-vision. It runs a few prompts and then goes into an endless hallucination loop. A clean install does not fix the issue even when going back to an older install.
GiteaMirror commented 2026-04-22 04:49:33 -05:00
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@Ketzemot commented on GitHub (Jul 1, 2025):

Hi, just to add to my previous post. A workaround for me was to use magistral 27b instead of gemma3_27b. The issue with gemma3 happens when I'm running with OLLAMA_NUM_PARALLEL > 1. In this case if one prompt is being answered by ollama and i send the second one, the first stream gets stuck for a seconds and then rambles on with nonsense repeatedly. However, this is not the case when using Magistral. I know this is not a solution but it seams that ollama cannot handle certain models properly.

<!-- gh-comment-id:3023770485 --> @Ketzemot commented on GitHub (Jul 1, 2025): Hi, just to add to my previous post. A workaround for me was to use magistral 27b instead of gemma3_27b. The issue with gemma3 happens when I'm running with OLLAMA_NUM_PARALLEL > 1. In this case if one prompt is being answered by ollama and i send the second one, the first stream gets stuck for a seconds and then rambles on with nonsense repeatedly. However, this is not the case when using Magistral. I know this is not a solution but it seams that ollama cannot handle certain models properly.
GiteaMirror commented 2026-04-22 04:49:34 -05:00
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@tgreenhaw5 commented on GitHub (Jul 1, 2025):

Hi, just to add to my previous post. A workaround for me was to use magistral 27b instead of gemma3_27b.

Ironically gemma3_27b seems to be ok for me. Unfortunately it is slower that llama3.2-vision and results for my use case are not quite as good.

<!-- gh-comment-id:3024525531 --> @tgreenhaw5 commented on GitHub (Jul 1, 2025): > Hi, just to add to my previous post. A workaround for me was to use magistral 27b instead of gemma3_27b. Ironically gemma3_27b seems to be ok for me. Unfortunately it is slower that llama3.2-vision and results for my use case are not quite as good.
GiteaMirror commented 2026-04-22 04:49:36 -05:00
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@karim20010 commented on GitHub (Jul 30, 2025):

same issue ? anyone can help?

<!-- gh-comment-id:3136813079 --> @karim20010 commented on GitHub (Jul 30, 2025): same issue ? anyone can help?
GiteaMirror commented 2026-04-22 04:49:37 -05:00
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@mohammedfaisal commented on GitHub (Sep 22, 2025):

The same problem still exists in ollama v0.12.0 with model llama3.2:3b-instruct-q8_0

<!-- gh-comment-id:3317051643 --> @mohammedfaisal commented on GitHub (Sep 22, 2025): The same problem still exists in ollama v0.12.0 with model llama3.2:3b-instruct-q8_0
GiteaMirror commented 2026-04-22 04:49:38 -05:00
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@mechagdynia2-ai commented on GitHub (Apr 19, 2026):

I ran into the exact same issue with my Radeon GPU. The most straightforward fix—though it requires a bit more manual setup—is switching to llama.cpp. If you encounter any loops or hangs there, just increase the token limit for the prompt and output. It’s much lighter on resources and natively supports TXT and PDF uploads, which is a huge plus, even if managing the models is a bit less intuitive at first.

Pro-tip: If you are on AMD, the real secret is making sure you compile llama.cpp with the GGML_HIP=ON flag. It ensures the backend communicates properly with your hardware, which is a total game-changer for stability and performance on Radeon cards.

### My personal llama.cpp setup for Ubuntu server 24.04.4:
`# Force maximum performance mode (Compute) for your Radeon GPU
sudo rocm-smi --setprofile 4 && \

Set library path and launch AI server: full GPU offload, large context, and no output cutoff

LD_LIBRARY_PATH=./build/bin ./build/bin/llama-server
-m ./models/mega-reasoning.gguf
-ngl 99
-c 16384
-n 4096
--temp 0.8
--host 0.0.0.0
--port 8080

Start the Web UI in Docker (accessible via port 3000)

sudo docker run -d -p 3000:8080
# Redirect the interface to your local GPU instead of the cloud
-e OPENAI_API_BASE_URL="http://host.docker.internal:8080/v1"
-e OPENAI_API_KEY="0"
# Link the container to your host system
--add-host=host.docker.internal:host-gateway
# Persist your chat history and uploaded files (PDF/TXT)
-v open-webui:/app/backend/data
--name open-webui
ghcr.io/open-webui/open-webui:main`

<!-- gh-comment-id:4275487586 --> @mechagdynia2-ai commented on GitHub (Apr 19, 2026): I ran into the exact same issue with my Radeon GPU. The most straightforward fix—though it requires a bit more manual setup—is switching to **llama.cpp**. If you encounter any loops or hangs there, just increase the token limit for the prompt and output. It’s much lighter on resources and natively supports TXT and PDF uploads, which is a huge plus, even if managing the models is a bit less intuitive at first. Pro-tip: If you are on AMD, the real secret is making sure you compile llama.cpp with the GGML_HIP=ON flag. It ensures the backend communicates properly with your hardware, which is a total game-changer for stability and performance on Radeon cards. **### My personal llama.cpp setup for Ubuntu server 24.04.4:** `# Force maximum performance mode (Compute) for your Radeon GPU sudo rocm-smi --setprofile 4 && \ ### Set library path and launch AI server: full GPU offload, large context, and no output cutoff LD_LIBRARY_PATH=./build/bin ./build/bin/llama-server \ -m ./models/mega-reasoning.gguf \ -ngl 99 \ -c 16384 \ -n 4096 \ --temp 0.8 \ --host 0.0.0.0 \ --port 8080 ### Start the Web UI in Docker (accessible via port 3000) sudo docker run -d -p 3000:8080 \ # Redirect the interface to your local GPU instead of the cloud -e OPENAI_API_BASE_URL="http://host.docker.internal:8080/v1" \ -e OPENAI_API_KEY="0" \ # Link the container to your host system --add-host=host.docker.internal:host-gateway \ # Persist your chat history and uploaded files (PDF/TXT) -v open-webui:/app/backend/data \ --name open-webui \ ghcr.io/open-webui/open-webui:main`
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