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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/ollama/ollama/issues/5614
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https://api.github.com/repos/ollama/ollama
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| 2,401,766,631
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PR_kwDOJ0Z1Ps51BVsT
| 5,614
|
OpenAI: Support Tools
|
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[] |
closed
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| 2024-07-10T21:21:37
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|
CONTRIBUTOR
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```
curl -s 127.0.0.1:11434/v1/chat/completions -d '{
"model": "mike/mistral",
"messages": [
{
"role": "user",
"content": "What'\''s the weather like today in Paris?"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "89a1e453-0bce-4de3-a456-c54bed09c520",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\"location\": \"Paris, France\", \"format\": \"celsius\"}"
}
}
]
},
{
"role": "tool",
"tool_call_id": "89a1e453-0bce-4de3-a456-c54bed09c520",
"content": "22"
},
{
"role": "assistant",
"content": "The weather in Paris is 22 degrees celsius."
},
{
"role": "user",
"content": "What'\''s the weather like today in San Francisco and Toronto?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"format": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
],
"description": "The temperature unit to use. Infer this from the users location."
}
},
"required": [
"location",
"format"
]
}
}
}
],
"stream": false,
"temperature": 0
}' | jq
```
```
{
"id": "chatcmpl-920",
"object": "chat.completion",
"created": 1721187203,
"model": "mike/mistral",
"system_fingerprint": "fp_ollama",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_q9jtgbw2",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\"format\":\"celsius\",\"location\":\"San Francisco, CA\"}"
}
},
{
"id": "call_9chg85cd",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\"format\":\"celsius\",\"location\":\"Toronto, Canada\"}"
}
}
]
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 198,
"completion_tokens": 65,
"total_tokens": 263
}
}
```
|
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https://github.com/ollama/ollama/pull/700
| 1,926,883,924
|
PR_kwDOJ0Z1Ps5b77h7
| 700
|
rename server subprocess
|
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"gists_url": "https://api.github.com/users/BruceMacD/gists{/gist_id}",
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"received_events_url": "https://api.github.com/users/BruceMacD/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
|
[] |
closed
| false
| null |
[] | null | 0
| 2023-10-04T19:53:55
| 2023-10-06T14:15:43
| 2023-10-06T14:15:42
|
CONTRIBUTOR
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"merged_at": "2023-10-06T14:15:42"
}
|
rename llama.cpp `server.exe` to `ollama-runner`. This makes it easier to see that the subprocess is associated with ollama.
|
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"followers_url": "https://api.github.com/users/BruceMacD/followers",
"following_url": "https://api.github.com/users/BruceMacD/following{/other_user}",
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| 2,817,099,829
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I_kwDOJ0Z1Ps6n6Yg1
| 8,647
|
ollama[2075]: cuda driver library failed to get device context 999time=2025-01-29T03:21:43.859+01:00 level=WARN source=gpu.go:449 msg="error looking up nvidia GPU memory"
|
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"name": "nvidia",
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"default": false,
"description": "Issues relating to Nvidia GPUs and CUDA"
}
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closed
| false
| null |
[] | null | 2
| 2025-01-29T02:34:23
| 2025-01-30T03:09:02
| 2025-01-30T03:09:01
|
NONE
|
{
"total": 0,
"completed": 0,
"percent_completed": 0
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| null | null | null |
### What is the issue?
Hi,
is my GPU not supported or what's the error?
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.120 Driver Version: 550.120 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce GTX 1660 Off | 00000000:01:00.0 On | N/A |
| 0% 48C P0 17W / 130W | 1602MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 2627 G /usr/lib/xorg/Xorg 1032MiB |
| 0 N/A N/A 2853 G /usr/bin/gnome-shell 79MiB |
| 0 N/A N/A 3461 G /usr/libexec/xdg-desktop-portal-gnome 108MiB |
| 0 N/A N/A 4054 G ...irefox/4793/usr/lib/firefox/firefox 203MiB |
| 0 N/A N/A 25159 G /usr/bin/nautilus 40MiB |
| 0 N/A N/A 106416 G ...erProcess --variations-seed-version 82MiB |
+-----------------------------------------------------------------------------------------+
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0
If you still need more context, please ask. I'm new here.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7
|
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I_kwDOJ0Z1Ps6jB38K
| 8,067
|
have a ques
|
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closed
| false
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[] | null | 2
| 2024-12-12T08:17:34
| 2024-12-23T08:12:10
| 2024-12-23T08:12:10
|
NONE
|
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| null | null | null |
### What is the issue?
C:\Users\Administrator> docker exec -it ollama ollama run llama3.2:1b
pulling manifest
pulling 74701a8c35f6... 100% ▕████████████████████████████████████████████████████████▏ 1.3 GB
pulling 966de95ca8a6... 100% ▕████████████████████████████████████████████████████████▏ 1.4 KB
pulling fcc5a6bec9da... 100% ▕████████████████████████████████████████████████████████▏ 7.7 KB
pulling a70ff7e570d9... 100% ▕████████████████████████████████████████████████████████▏ 6.0 KB
pulling 4f659a1e86d7... 100% ▕████████████████████████████████████████████████████████▏ 485 B
verifying sha256 digest
writing manifest
success
Error: error starting the external llama server: fork/exec /usr/lib/ollama/runners/cpu_avx2/ollama_llama_server: exec format error
### OS
Windows
### GPU
Intel
### CPU
Intel
### Ollama version
_No response_
|
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PR_kwDOJ0Z1Ps5V9on7
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|
Update images.go
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6CsJuL
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Cannot create Modelfile on Windows Shell
|
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| 2024-03-18T15:57:03
| 2024-03-19T08:50:15
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### What is the issue?
The following is not working in WIndows Shell. Windows Shell doesn't recognize FROM.
--------------------------------------------------
FROM llama2
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""
### What did you expect to see?
Cannot create Modelfile
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Windows
### Architecture
_No response_
### Platform
_No response_
### Ollama version
_No response_
### GPU
_No response_
### GPU info
_No response_
### CPU
_No response_
### Other software
_No response_
|
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I_kwDOJ0Z1Ps6Am1US
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|
Bug: LLaVA 1.6 34b not respecting initial user prompt
|
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M2 Max MBP 96GB RAM
Ollama 0.1.27
Compared against llama.cpp CLI @b11a93d (same as Ollama version)
Problem:
When using the Ollama CLI or API with an image, the initial text prompt isn't respected. Examples like the one on the [model page](https://ollama.com/library/llava) typically show prompts like "What is in this picture?". However, when changing the initial prompt to something like "Is this image of a llama?" or "How many animals are in this picture?" or even "Ignore the image and tell me the meaning of life", the output is typically a description of the image.
When using the llama.cpp CLI however, these prompts are followed as expected.
When using chat completions in Ollama, a followup question _does_ work properly, so after initially being ignored you can get the expected output on a second attempt.
My suspicion is that this behavior is largely unnoticed, as the default examples are asking for a description and one is being returned. However, this is masking the fact that text prompts used in conjunction with image prompts aren't being properly utilized. As an aside, LM Studio suffers from the same issue.
The below examples use the same image as the one base64 encoded in the above model page CLI example.
Ollama example:
```
❯ ollama run llava:34b-v1.6-q6_K
>>> /set parameter temperature 0.2
Set parameter 'temperature' to '0.2'
>>> How many animals are in this picture? /Users/mike/Downloads/llama.png
Added image '/Users/mike/Downloads/llama.png'
The image you've provided appears to be a cartoon or illustration of an
animal character. It looks like a cute, stylized depiction of a pig with a
happy expression and waving its hand as if saying hello or goodbye. The
art style is simplistic and playful, which is common in many modern
cartoons and emojis.
>>> How many animals are in this picture?
There is only one animal in this picture, which is the cute pig character.
```
llama.cpp example:
```
❯ ./llava-cli -m ../llm-models/cmp-nct/llava-1.6-gguf/ggml-yi-34b-f16-q_5_k.gguf --mmproj ../llm-models/cmp-nct/llava-1.6-gguf/mmproj-llava-34b-f16-q6_k.gguf --image ~/Downloads/llama.png --temp 0.2 -e -p '<|im_start|>system\n<|im_end|><|im_start|>user\n<image>\nHow many animals are in this picture?<|im_end|><|im_start|>assistant\n'
clip_model_load: model name: vit-large336-custom
clip_model_load: description: image encoder for LLaVA
clip_model_load: GGUF version: 3
clip_model_load: alignment: 32
clip_model_load: n_tensors: 378
clip_model_load: n_kv: 26
clip_model_load: ftype: q6_K
...
system_prompt: <|im_start|>system
<|im_end|><|im_start|>user
user_prompt:
How many animals are in this picture?<|im_end|><|im_start|>assistant
There is one animal in this picture, which appears to be a stylized drawing of a pig.
...
```
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I_kwDOJ0Z1Ps6H-HF4
| 4,205
|
please support deepseek-ai/DeepSeek-V2-Chat
|
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NONE
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please support deepseek-ai/DeepSeek-V2-Chat
https://hf-mirror.com/deepseek-ai/DeepSeek-V2-Chat/tree/main
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I_kwDOJ0Z1Ps58hLOl
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|
Prompt Eval Count is 1 when image is included in multimodal request
|
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When I hit the `/api/generate` endpoint for a multimodal model, such as bakllava, I am seeing that `prompt_eval_count == 1` when an image is included. Is this correct behavior? How can I retrieve the total amount of tokens in the request for a multimodal request?
|
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I_kwDOJ0Z1Ps6Qk1gF
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|
Make Ollama available on Chocolatey
|
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The Windows installers works great, but requires that I manually download/update it from the ollama website. Would it be possible, probably when the Windows version has matured some more, to add ollama to [Chocolatey](https://community.chocolatey.org/)? That will probabably aid in making this an (even) easier to install package on Windows.
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PR_kwDOJ0Z1Ps5mL7sV
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Bump llama.cpp to b2081
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I_kwDOJ0Z1Ps6E3sFY
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Stream: false is ignored in as of c863c6a96d01afda37b65f86eb14a1f04a3c7c47
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### What is the issue?
Stream: false is ignored
### What did you expect to see?
When stream is set to false, the response should contain the full result.
### Steps to reproduce
Pull the latest code, compile, run (pull some model), run /api/generate with stream: false -> the result has no content (just some new lines) and "done: false".
### Are there any recent changes that introduced the issue?
I think this https://github.com/ollama/ollama/commit/c863c6a96d01afda37b65f86eb14a1f04a3c7c47 breaks it (Tested before and after this commit).
### OS
Windows
### Architecture
amd64
### Platform
_No response_
### Ollama version
latest
### GPU
Nvidia
### GPU info
Quadro RTX 5000 with Max-Q Design
### CPU
Intel
### Other software
_No response_
|
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PR_kwDOJ0Z1Ps6FWP5c
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Examples(privategpt): update outdated langchain dependencies and add UnstructuredRSTLoader
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NONE
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This commit updates the langchain dependencies to the latest stable versions and adds the UnstructuredRSTLoader for loading RST files.
- Update langchain dependencies to the latest stable versions.
- Add UnstructuredRSTLoader for loading RST files.
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CUDA error: an illegal memory access was encountered
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| 2024-03-12T09:35:45
| 2024-07-24T23:14:51
| 2024-07-24T23:14:51
|
NONE
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ollama run llama2
When I executed the command: ollama run llama2, an error occurred related to the data information.
I only have a 128G GPU. Do I need more than 2 GPUs? I can run it on my local laptop with 2 GPUs without any errors.
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I_kwDOJ0Z1Ps6e2pAx
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|
On Windows 11 pro, it does work to right click "restart to update"
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| 2024-11-17T00:32:03
| 2024-11-18T22:39:53
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### What is the issue?
It does work to right click "restart to update".
Thanks fo great work.
----------------------
### OS
Windows
### GPU
Intel
### CPU
Intel
### Ollama version
0.4.1
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PR_kwDOJ0Z1Ps6JSssP
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|
Update README with DeepSeek model in the examples
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| 2025-01-28T23:10:31
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NONE
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Updated the README with the an example of DeepSeek model
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PR_kwDOJ0Z1Ps5_Y0rC
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integration: harden embedding test
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| 2024-10-22T00:15:14
| 2024-10-22T22:25:25
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COLLABORATOR
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Use cosine similarity to make the embeddings tests more robust
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Error: connect ECONNREFUSED 127.0.0.1:11434
|
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NONE
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Hello everyone！
My ollama in My docker
docker Start ollama command is docker run -e OLLAMA_HOST=0.0.0.0:11434 -d -v ollama serve -p 11434:11434 --name ollama ollama/ollama
Then I in vscode open chatbot-ollama And then input npm run dev And then Report an error
↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓ Here is the error log ↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓
PS G:\AI\chatbot-ollama> npm run dev
chatbot-ollama@0.1.0 dev
next dev
▲ Next.js 13.5.6
Local: http://localhost:3000/
✓ Ready in 2.9s
○ Compiling / ...
✓ Compiled / in 3.3s (1652 modules)
⚠ Fast Refresh had to perform a full reload. Read more: https://nextjs.org/docs/messages/fast-refresh-reload
✓ Compiled in 1699ms (1652 modules)
✓ Compiled in 519ms (1652 modules)
✓ Compiled /api/models in 245ms (68 modules)
[TypeError: fetch failed] {
cause: [Error: connect ECONNREFUSED 127.0.0.1:11434] {
errno: -4078,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 11434
}
}
✓ Compiled in 620ms (1720 modules)
[TypeError: fetch failed] {
cause: [Error: connect ECONNREFUSED 127.0.0.1:11434] {
errno: -4078,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 11434
}
}
[TypeError: fetch failed] {
cause: [Error: connect ECONNREFUSED 127.0.0.1:11434] {
errno: -4078,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 11434
}
}
[TypeError: fetch failed] {
cause: [Error: connect ECONNREFUSED 127.0.0.1:11434] {
errno: -4078,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 11434
}
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Mistral - Failed To Load Model
|
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I'm running macOS (Ventura 13.0.1) 16in. M1 2021. I am able to run all of the llama2 models just fine, but the following occurs when attempting to run the mistral model:
```
~ % ollama pull mistral:latest
pulling manifest
pulling 6ae280299950... 100% |███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (4.1/4.1 GB, 48 MB/s)
pulling fede2d8d6c1f... 100% |████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (29/29 B, 194 kB/s)
pulling b96850d2e482... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (307/307 B, 1.4 MB/s)
verifying sha256 digest
writing manifest
success
~ % ollama run mistral:latest
>>> Hello
Error: failed to load model
```
If this is still a WIP please close this issue. The only other information I could find is the following log entries:
```
2023/10/11 10:25:47 images.go:1093: redirected to: https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/6a/6ae28029995007a3ee8d0b8556d50f3b59b831074cf19c84de87acf51fb54054/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%2F20231011%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20231011T142546Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=386696851dae4763d830fc88c05381be653dab1e21243686e3180c01011644b6
2023/10/11 10:27:13 images.go:1061: success getting sha256:6ae28029995007a3ee8d0b8556d50f3b59b831074cf19c84de87acf51fb54054
2023/10/11 10:27:14 images.go:1093: redirected to: https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/fe/fede2d8d6c1f404b1db73b1cd26f7d5455ff2deeb737b5e2b339339dce2969d4/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%2F20231011%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20231011T142714Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=4b1d208c4dcb6b20ae9727869c284c8ec0f77ee382ee975d96f50f1c358047e7
2023/10/11 10:27:14 images.go:1061: success getting sha256:fede2d8d6c1f404b1db73b1cd26f7d5455ff2deeb737b5e2b339339dce2969d4
2023/10/11 10:27:15 images.go:1093: redirected to: https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/b9/b96850d2e482b0d1af356eda4ac158af93e9b00e71363a9173d7b5480680bcf3/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%2F20231011%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20231011T142715Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=a5cb6b9a865ca7264746ec67325b71ad80987c800bbeeadd43eef75a6e0363bc
2023/10/11 10:27:15 images.go:1061: success getting sha256:b96850d2e482b0d1af356eda4ac158af93e9b00e71363a9173d7b5480680bcf3
[GIN] 2023/10/11 - 10:27:18 | 200 | 1m33s | 127.0.0.1 | POST "/api/pull"
llama.cpp: loading model from /Users/mattdavenport/.ollama/models/blobs/sha256:6ae28029995007a3ee8d0b8556d50f3b59b831074cf19c84de87acf51fb54054
error loading model: unknown (magic, version) combination: 46554747, 00000002; is this really a GGML file?
llama_load_model_from_file: failed to load model
[GIN] 2023/10/11 - 11:04:20 | 500 | 3.950083ms | 127.0.0.1 | POST "/api/generate"
```
Please let me know if I can provide any additional information here to help debug. Thanks!
|
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PR_kwDOJ0Z1Ps5mFH5O
| 2,363
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Move hub auth out to new package
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| 2,136,760,146
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I_kwDOJ0Z1Ps5_XF9S
| 2,514
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Support for safetensors
|
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| 2024-02-15T15:02:55
| 2024-02-21T05:38:43
| 2024-02-18T06:49:05
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NONE
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Do we already support inferencing safetensors?
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PR_kwDOJ0Z1Ps5lMKh3
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|
Detect lack of AVX and fallback to CPU mode
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| 2024-01-26T19:42:11
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We build the GPU libraries with AVX enabled to ensure that if not all layers fit on the GPU we get better performance in a mixed mode. If the user is using a virtualization/emulation system that lacks AVX this used to result in an illegal instruction error and crash before this fix. Now we will report a warning in the server log, and just use CPU mode to ensure we don't crash.
This should mitigate #2187.
|
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I_kwDOJ0Z1Ps6mOK7P
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Embedding with bge-large:latest fails with "GGML_ASSERT(i01 >= 0 && i01 < ne01) failed"
|
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| 2025-01-15T03:07:10
| 2025-01-15T06:35:56
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> @rick-github Thanks,I tried the "options":{"num_ctx":512} ,It works well! 💯
> but the logs always shows that
>
> 
_Originally posted by @Justin-12138 in [#7441](https://github.com/ollama/ollama/issues/7441#issuecomment-2591480334)_
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PR_kwDOJ0Z1Ps5fcrpD
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|
FAQ: answer a few faq questions
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PR_kwDOJ0Z1Ps5u0qMP
| 4,247
|
skip hidden files in list models handler
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| 2024-05-08T01:34:13
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Hidden files on MacOS (ex: `.DS_Store`) cause the list command to file when not skipped:
```
❯ ollama ls
Error: unqualified name:
```
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PR_kwDOJ0Z1Ps5uBD92
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Update linux.md, suggest containerized install
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| 2024-04-29T13:43:19
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It's the simplest install process, once you run the one liner, everything just works.
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PR_kwDOJ0Z1Ps52I6Yu
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auth: update auth
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pre cursor to setting up tests for https://github.com/ollama/ollama/pull/5441
moved initialize keypair to auth
use initialize keypair implicitly when reading pubkey
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Support InternLM
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| 2023-09-12T02:09:05
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| 2024-03-11T19:32:53
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NONE
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Dear ollama developer,
Greetings! I am vansinhu, a community developer and volunteer at InternLM. [InternLM](https://github.com/InternLM/InternLM) is a large language model similar to llama2, and we look forward to InternLM being supported in ollama. If there are any challenges or inquiries regarding support for InternLM, please feel free to join our Discord discussion at https://discord.gg/gF9ezcmtM3.
Best regards,
vansinhu
|
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fix: run container as non-root user
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NONE
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**Enable non-root user Container Deployment Support**
**Description**
This PR adds support for running ollama in rootless containers by properly setting up permissions for the `.ollama` directory. This change enables ollama to run in environments that enforce non-root execution such as:
* OpenShift
* Kubernetes with strict security contexts
* Containers with user namespace remapping
**Changes**
* Added directory creation and permission setup in the final stage of Dockerfile
* Set container to run as non-root user (UID 1001)
* Configured group permissions to support random GID assignment
**Testing Done**
* Built image with podman
* Deployed on OpenShift
* Verified container starts successfully with non-root user
* Confirmed `.ollama` directory is properly accessible
**Previous Behavior**
The container would fail to start in rootless environments with the error:
```
Couldn't find '/.ollama/id_ed25519'. Generating new private key.
Error: could not create directory mkdir /.ollama: permission denied
```
**New Behavior**
* Container starts successfully as non-root user
* `.ollama` directory is created with proper permissions
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Do not reinstall the CLI tools if they are already installed on macOS
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Check `installed()` inside of the `install` function before running the cli setup commands. Fix #5305.
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No devices found using AMD gpus
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### What is the issue?
Getting the following error when loading models with AMD gpus (Instinct MI60s):
```
rocBLAS error: Could not initialize Tensile host: No devices found
```
I have 4 devices allocated to the container and it seems to still discover them at startup:
```
time=2024-08-04T22:17:48.256Z level=INFO source=gpu.go:205 msg="looking for compatible GPUs"
time=2024-08-04T22:17:48.264Z level=INFO source=amd_linux.go:345 msg="amdgpu is supported" gpu=0 gpu_type=gfx906
time=2024-08-04T22:17:48.264Z level=WARN source=amd_linux.go:201 msg="amdgpu too old gfx000" gpu=1
time=2024-08-04T22:17:48.264Z level=WARN source=amd_linux.go:201 msg="amdgpu too old gfx000" gpu=2
time=2024-08-04T22:17:48.264Z level=WARN source=amd_linux.go:201 msg="amdgpu too old gfx000" gpu=3
time=2024-08-04T22:17:48.265Z level=WARN source=amd_linux.go:201 msg="amdgpu too old gfx000" gpu=4
time=2024-08-04T22:17:48.265Z level=INFO source=amd_linux.go:345 msg="amdgpu is supported" gpu=5 gpu_type=gfx906
time=2024-08-04T22:17:48.266Z level=INFO source=amd_linux.go:345 msg="amdgpu is supported" gpu=6 gpu_type=gfx906
time=2024-08-04T22:17:48.268Z level=INFO source=amd_linux.go:345 msg="amdgpu is supported" gpu=7 gpu_type=gfx906
```
It is working fine with the `0.3.1-rocm` image, but seeing this behavior in the newest versions.
### OS
Docker
### GPU
AMD
### CPU
Intel
### Ollama version
0.3.2
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CI updates
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Not ready for review yet...
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tighten up the error string for `ollama show` flags
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Add allowed host middleware and remove `workDir` middleware
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PR_kwDOJ0Z1Ps5beytn
| 638
|
add cuda to windows generation
|
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Still a bunch of stuff to do for packaging this for distribution, but I tested building and running this locally and it works along with #637 so may as well get it in.
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I_kwDOJ0Z1Ps6FjigB
| 3,616
|
Richer grammars
|
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NONE
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### What are you trying to do?
Being able to specify grammars is great, but it seems a bit underutilized at the moment. This is mostly a thought dump on how it could be improved from experimentation...
### How should we solve this?
- Using llama.cpp grammar directly would be pretty powerful and nice to have
- Specifying jsonschema for json. Llama.cpp json is usually forced into a specific key order and ollama json isn't schema'd at all
- Changing the format on the fly is useful, but I think it would be nice to have a way to specify a grammar in the Modelfile.
### What is the impact of not solving this?
Not having either of the first two ideas is annoying, since there's no way to guarantee that a model generates a response in a format I want. The third idea allows one to make a "llm api", where a model generates a specific response every time (imagine bundling a "summary llm" that always responds with `{"summary":"..."}` as a Modelfile.)
### Anything else?
_No response_
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I_kwDOJ0Z1Ps6nxHDI
| 8,624
|
Deepseek 80% size reduction
|
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| 2025-01-28T05:27:54
| 2025-01-28T22:28:24
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NONE
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New quants done by unsloth.ai:
| MoE Bits | Disk Size | Type | Quality | Link | Down_proj |
|-----------|-----------|----------|---------|------------------------------------------------------------------------------------------------------------------------------------------------|----------------|
| 1.58-bit | 131GB | IQ1_S | Fair | [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ1_S) | 2.06/1.56bit |
| 1.73-bit | 158GB | IQ1_M | Good | [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ1_M) | 2.06bit |
| 2.22-bit | 183GB | IQ2_XXS | Better | [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ2_XXS) | 2.5/2.06bit |
| 2.51-bit | 212GB | Q2_K_XL | Best | [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-Q2_K_XL) | 3.5/2.5bit |
please consider adding them
https://unsloth.ai/blog/deepseekr1-dynamic
thanks!
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I_kwDOJ0Z1Ps5rABL2
| 59
|
generate pauses after about 50 tokens
|
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| 2023-07-08T23:42:53
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Generation will get paused after about 50 tokens being provided
```
% ollama run orca
>>> Write a review of the restaurant "five guys"
As an AI assistant, I cannot write a biased or subjective review, but I can provide you with some general information about the restaurant "Five Guys". Five Guys is an American fast-food chain that primarily serves hamburgers, fries, <pause here>
```
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I_kwDOJ0Z1Ps6NdVcs
| 5,278
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Is it possible to start llama server through dynamic dependency library?
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| 2024-06-25T17:43:21
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Hi, I'm trying to run Ollama in [Nanos Unikernel](https://github.com/nanovms/nanos), Unikernel a single-process operating system.
I found that in https://github.com/ollama/ollama/commit/58d95cc9bd446a8209e7388a96c70367cbafd653, the running mode of llama server changed from loading dynamic dependency libraries to running through subprocess. Therefore, before version 0.1.32, I could run Ollama normally in Nanos, but it could not run normally in later versions.
I would like to ask whether it is possible to continue to provide the method of loading llama server as a dynamic dependency library and avoid using subprocess. Perhaps the two methods can coexist and users can choose by themselves.
Looking forward to your reply, thank you very much.
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I_kwDOJ0Z1Ps559d6G
| 1,579
|
Error while running ollama locally.
|
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ConnectionError: HTTPConnectionPool(host='localhost', port=11434): Max retries exceeded with url: /api/generate/ (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000002298AE1EF50>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it'))
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I_kwDOJ0Z1Ps6cCAF7
| 7,394
|
The Open WebUI generate unsense text, but the cli terminal can chat normally with the same hosted model
|
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[] | null | 9
| 2024-10-28T09:06:43
| 2024-11-13T22:12:46
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NONE
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### What is the issue?
The results generated by cli terminal:
The results generated by open webui:
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.14
|
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I_kwDOJ0Z1Ps6lHAY-
| 8,316
|
Feature Request: Limit Ollama to use only GPU RAM (VRAM), not system RAM
|
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| 2025-01-06T08:02:41
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### Feature Request
**Description**:
I would like to request a feature that allows Ollama to be configured to use only GPU RAM, without utilizing CPU or system RAM. This would be beneficial for systems with limited RAM resources and high GPU RAM (VRAM) memory availability.
**Benefits**:
- Optimized performance on GPU-heavy systems
- Reduced CPU load and system RAM usage
- Enhanced efficiency for specific workloads
**Example Use Case**:
In scenarios where the system has multiple GPUs with high memory capacity but limited CPU and system RAM, this feature would enable Ollama to leverage the full potential of the GPUs without being constrained by CPU or system RAM limitations.
**Environment Variables/Configuration**:
- An environment variable or configuration setting to enforce GPU-only memory usage.
- Example: `OLLAMA_USE_GPU_ONLY=1`
**Related Files and Code**:
- Documentation: [docs/gpu.md](https://github.com/ollama/ollama/blob/main/docs/gpu.md)
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I_kwDOJ0Z1Ps6nb0xI
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|
None value being returned from with_structured_output request
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| 2025-01-24T10:26:05
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### What is the issue?
Python versions are:
langchain 0.3.15
langchain-community 0.3.15
langchain-core 0.3.31
langchain-ollama 0.2.2
ollama 0.4.7
Running ollama 0.5.7 (pip install -U ollama did not increase the version beyond 0.4.7)
---
Using **with_structured_output()** seems to work for a very simple example such as the following:
```
from langchain_ollama import ChatOllama
from typing import Optional
from pydantic import BaseModel, Field
class Person(BaseModel):
name: str
age: int
llm = ChatOllama(
model="qwen2.5:1.5b",
temperature=0,
).with_structured_output(Person)
llm.invoke("Erick 27")
```
However, for a more complex requirement, it fails with Ollama returning a value of None.
```
from pydantic import BaseModel, Field
from typing import Optional
from openai import OpenAI
from langchain_ollama import ChatOllama
# Define the output model
class Experience(BaseModel):
company: str = Field(..., description="The name of the company.")
position: str = Field(..., description="The job title held at the company.")
start_date: str = Field(..., description="The date when you started working at the company.")
end_date: str = Field(..., description="The date when you left the company. If still employed, use 'Present'.")
class Education(BaseModel):
institution_name: str = Field(..., description="The name of the educational institution.")
degree: str = Field(..., description="The degree obtained from the institution.")
start_date: str = Field(..., description="The date when you started attending school at the institution.")
end_date: str = Field(..., description="The date when you graduated. If still enrolled, use 'Present'.")
class Resume(BaseModel):
full_name: str = Field(..., description="The full name of the person on the resume.")
contact_email: str = Field(..., description="The email address for contacting the person.")
phone_number: str = Field(..., description="The phone number for contacting the person.")
summary: str = Field(..., description="A brief summary of the person's career highlights.")
experience: Optional[list[Experience]] = Field([], description="List of experiences held by the person.")
education: Optional[list[Education]] = Field([], description="List of educational institutions attended by the person.")
with open('CVs/resume.md', 'r') as file:
resume_data = file.read()
verbose=True
model = "qwen2.5:14b"
prompt = f"""
Analyse the following resume from the content between the triple backticks below: For the resume below, identify the following information:
1) Their personal details, including name, email, phone number and anything else they provide.
2) An overall summary of their experience to provide a general background.
3) A list of the companies they have worked for. This should include the company name, the dates they started and and ended working for the company, and the tasks and activities they carried out.
4) A list of universities or colleges that the person went to. This should include the name of the college the title of the qualification, and the dates they started and ended.
The raw data is here:
```{resume_data}```
"""
# print(prompt)
llm = ChatOllama( model=model,
num_ctx = 32000,
timeout = 600,
temperature=0.0,
verboseness = verbose,
response = "json")
structured_llm = llm.with_structured_output(Resume)
print("Calling LLM")
response = structured_llm.invoke(prompt)
print(response)
```
It also fails without the 'response = "json" included.
I just get a None response.
Oddly, this is not consistent. Sometimes, I get back a response, but it fails satisfying the Resume type. because it won't find education items. Even though Education is an optional type in the Resume class.
For some reason, I cannot upload the small resume file, so here it is in cleartext:
---
```
## **Professional Experience**
### **Senior Software Engineer**
**Tech Innovators Inc.**
_June 2015 – Present_
- Designed and implemented scalable microservices architecture for a SaaS platform, improving performance by 30%.
- Led a team of 12 engineers, mentoring junior developers and conducting regular code reviews.
- Integrated AI/ML capabilities into legacy systems, increasing operational efficiency by 20%.
- Championed DevOps practices, reducing deployment times from days to hours.
### **Software Architect**
**NextGen Solutions**
_March 2010 – May 2015_
- Architected and delivered a real-time analytics platform for financial services, handling millions of transactions daily.
- Migrated a monolithic system to a distributed microservices-based architecture, enabling faster feature delivery.
- Partnered with product managers to define technical requirements and roadmap, aligning business goals with engineering efforts.
### **Lead Developer**
**Alpha Development Corp.**
_January 2005 – February 2010_
- Built a high-availability e-commerce platform that handled over 500,000 daily users.
- Created APIs to integrate third-party payment gateways, enhancing user experience and reducing downtime.
- Conducted performance optimizations that improved application speed by 40%.
### **Software Engineer**
**CodeSphere LLC**
_June 2000 – December 2004_
- Developed enterprise-grade web applications using Java and C++.
- Automated internal processes, saving the company 15% in operational costs annually.
- Collaborated with cross-functional teams to deliver projects on time and within budget.
---
## **Education**
### **Master of Science in Computer Science**
**Massachusetts Institute of Technology**
_August 1998 – May 2000_
### **Bachelor of Science in Computer Science**
**University of California, Berkeley**
_August 1994 – May 1998_
---
## **Skills**
- Programming Languages: Python, Java, C++, JavaScript
- Cloud Platforms: AWS, Azure, Google Cloud
- Architecture: Microservices, Distributed Systems, RESTful APIs
- Tools: Docker, Kubernetes, Terraform
- Agile Development, DevOps, AI/ML Integration
---
## **Certifications**
- AWS Certified Solutions Architect – Professional
- Certified Kubernetes Administrator (CKA)
- Certified ScrumMaster (CSM)
---
## **Contact**
Feel free to reach out via email or phone for opportunities or collaboration.
```
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.5.7
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https://github.com/ollama/ollama/pull/130
| 1,812,847,886
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PR_kwDOJ0Z1Ps5V8NI1
| 130
|
vendor in progress bar and change to bytes instead of bibytes
|
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| 2023-07-19T22:54:28
| 2023-07-20T00:24:03
| 2023-07-20T00:24:03
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6J-fnS
| 4,611
|
User comments on personal model page
|
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| 2024-05-24T09:05:07
| 2024-07-08T17:22:09
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NONE
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I would like to suggest a section for user comments on the personal Ollama model page. This will help with better tuning the parameters for your version of the model.
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PR_kwDOJ0Z1Ps5u_yAg
| 4,287
|
suppress ollama_llama_server.exe blank command window popup
|
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NONE
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I_kwDOJ0Z1Ps57Tlcx
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Massive slowdown on v 0.1.18 vs 0.1.17 with same model on Intel Mac
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| 2024-01-06T17:18:27
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NONE
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<img width="255" alt="SCR-20240106-kfri" src="https://github.com/jmorganca/ollama/assets/327716/c1b5ce5b-acd5-4c81-a59a-6db39ff6a257">
I don’t have exact timings but the same model (in this case, `deepseek-coder:6.7b-instruct-q4_K_S`) generates tokens roughly 5 times faster on 0.1.17 than on 0.1.18 on my Intel Mac.
I upgraded to 0.1.18 and noticed the slowdown in token generation and then downgraded back to 0.1.17 and immediately saw the faster throughput I am accustomed to.
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I_kwDOJ0Z1Ps6jXTQa
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Error: llama runner process has terminated: error:/opt/rocm/lib/libhipblas.so.2: undefined symbol: rocblas_sgbmv_64
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### What is the issue?
When I start llama3.2 with `ollama run llama3.2` this error spawns:
`Error: llama runner process has terminated: error:/opt/rocm/lib/libhipblas.so.2: undefined symbol: rocblas_sgbmv_64`
I am on Cachy OS with ollama and ollama-rocm installed from extra repo and a AMD RX6800 is the GPU
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.5.2
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I_kwDOJ0Z1Ps6U5Btc
| 6,571
|
Impossible to connect to ollama locally from another pc
|
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### What is the issue?
Hello, I'm using ollama on windows but I can't connect to the server from another PC on the local network.
I have already changed the following system environment variables.
`OLLAMA_HOST=0.0.0.0`
`OLLAMA_ORIGINS=*`
Thank you for your assistance.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.8
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I_kwDOJ0Z1Ps6AI-TO
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Ollama windows version not working
|
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NONE
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It was working fine even yesterday, but I got an update notification and it hasn't been working since. I even tried deleting and reinstalling the installer exe, but it seems the app shows up for a few seconds and then disappears again, but powershell still recognizes the command - it just says ollama not running.
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PR_kwDOJ0Z1Ps51sq18
| 5,758
|
marshal json automatically for some template values
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PR_kwDOJ0Z1Ps5s1fe1
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|
darwin: no partial offloading if required memory greater than system
|
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I_kwDOJ0Z1Ps6IlVSs
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|
Yi 1.5 was released
|
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https://github.com/01-ai/Yi-1.5
https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8
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PR_kwDOJ0Z1Ps5cxVPh
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fix: offloading on low end GPUs
|
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| 2023-10-13T20:08:46
| 2023-10-13T21:36:45
| 2023-10-13T21:36:44
|
CONTRIBUTOR
|
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Fixes two issues when using low end GPUs:
GPUs with low VRAM are disproportionately affected by overhead when offloading so any device that has less than 2GB VRAM will be exclusively CPU unless overwritten by num_gpu.
A CUDA-enabled runner will still offload to GPU even if num_gpu is 0. This is problematic when the GPU doesn't support a compatible version of CUDA. In this case, select the CPU runner instead.
Caveat: for MacOS (darwin) `go generate` only builds Metal on ARM so it shouldn't be marked as `Accelerated` since there's no fallback
|
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https://github.com/ollama/ollama/pull/861
| 1,954,903,919
|
PR_kwDOJ0Z1Ps5dakP4
| 861
|
Added /clear command to clear the buffer
|
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| 2023-10-20T19:25:06
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| 2023-10-20T20:05:19
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Hey,
I thought a /clear command could enhance user experience.
It should work for both windows and linux/mac.
|
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I_kwDOJ0Z1Ps6LMce-
| 4,830
|
model import to Ollama
|
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| 2024-06-05T08:54:55
| 2024-09-04T13:31:21
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NONE
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Is it possible to import model to Ollama from HuggingFace which is not listed in the script convert-hf-to-gguf.py?
In my case model: https://huggingface.co/ai-forever/ruGPT-3.5-13B
When I try import got error:
```
Traceback (most recent call last):
File "llm/llama.cpp/convert-hf-to-gguf.py", line 2865, in <module>
main()
File "llm/llama.cpp/convert-hf-to-gguf.py", line 2850, in main
model_instance.set_vocab()
File "llm/llama.cpp/convert-hf-to-gguf.py", line 114, in set_vocab
self._set_vocab_gpt2()
File "llm/llama.cpp/convert-hf-to-gguf.py", line 500, in _set_vocab_gpt2
tokens, toktypes, tokpre = self.get_vocab_base()
File "llm/llama.cpp/convert-hf-to-gguf.py", line 379, in get_vocab_base
tokpre = self.get_vocab_base_pre(tokenizer)
File "llm/llama.cpp/convert-hf-to-gguf.py", line 491, in get_vocab_base_pre
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
NotImplementedError: BPE pre-tokenizer was not recognized - update get_vocab_base_pre()
```
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I_kwDOJ0Z1Ps6FMQJY
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Could anyone tell me the purpose of "ollama-linux-amd64-rocm.tgz" in the released files?
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NONE
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### What are you trying to do?
I'm trying to run Ollama on Linux (Ubuntu 22.04) with ROCm, but I'm unable to use it even after following the installation instructions in the documentation. Do I need to use the "ollama-linux-amd64-rocm.tgz" file? However, I'm not sure how to use this file, and there are no instructions provided. Any help would be greatly appreciated!
### How should we solve this?
_No response_
### What is the impact of not solving this?
_No response_
### Anything else?
_No response_
|
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Ollama in private EC2
|
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### What is the issue?
How to run ollama on private EC2
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
_No response_
|
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| 2,288,039,651
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PR_kwDOJ0Z1Ps5vAm1e
| 4,290
|
fix(server): prune files
|
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follow up to #4269
|
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I_kwDOJ0Z1Ps6PM011
| 5,627
|
unknown model architecture: 'gemma2'
|
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### What is the issue?
I'm trying to run models on my internal Intel GPU.
Here's the software env:
1. activate a virtual env in Anaconda
2. already installed ipex-llm[cpp], ipex-llm[xpu]
3. start the Ollama serve
4. start gemma2 model
Then the log said: unknown model architecture: 'gemma2'
Here's the sreenshot of the log:
### OS
Windows, Docker
### GPU
Intel
### CPU
Intel
### Ollama version
0.2.1
|
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I_kwDOJ0Z1Ps6HLb_9
| 4,011
|
When my ollama has both the gemma and llama large models, how can I enable them at the same time?Thank you.
|
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NONE
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When my ollama has both the gemma and llama large models, how can I enable them at the same time? Does pressing CTRL+D mean to exit the large model? Thank you.
|
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I_kwDOJ0Z1Ps6mb6t9
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model wanted in ollama please:Qwen2.5-Math-PRM-7B
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model wanted in ollama please:Qwen2.5-Math-PRM-7B
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| 2,492,824,185
|
I_kwDOJ0Z1Ps6UlXp5
| 6,544
|
Specifying options via openai client extra_body are not handled by ollama
|
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| 2024-08-28T19:19:11
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CONTRIBUTOR
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### What is the issue?
So I've been trying to set the num_ctx for the mistral-nemo through the openai api client, however it does not seem to have an effect.
```python
# Ollama client
client = OllamaAsyncClient(host=base_url, **kwargs)
chat_completion = await client.chat(
messages=messages,
model=model,
stream=stream,
options={
"num_ctx": 64_000,
"temperature": temperature,
"stop": stop_tokens,
},
**kwargs,
)
message = chat_completion["message"]["content"].strip()
# OpenAI client
client = AsyncOpenAI(api_key=api_key, base_url=base_url, **kwargs)
chat_completion = await client.chat.completions.create(
messages=messages,
model=model,
temperature=temperature,
stream=stream,
stop=stop_tokens,
extra_body={"options": {"num_ctx": 64_000}},
**kwargs,
)
message = chat_completion.choices[0].message.content.strip()
```
Doing it with ollama works, but the extra_body argument from openai seems to be handled as an extra field rather than merged with the rest of the request.
```
'model': 'mistral-nemo:12b-instruct-2407-q8_0', 'stop': None, 'stream': False, 'temperature': 0.7}, 'extra_json': {'options': {'num_ctx': 64000}}}
```
I guess the solution would be to unpack the extra_json field on the server end?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.8
|
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I_kwDOJ0Z1Ps6ZGGtf
| 7,106
|
Update /api/show endpoint to return a boolean describing if the model supports FIM
|
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Hi,
We support Ollama as a [model provider in Continue.dev](https://docs.continue.dev/customize/model-providers/ollama) and have recently added FIM (Fill-in-the-Middle) support. Currently, there is no API we are aware of that can be used to determine if a model supports FIM, so we guess its capabilities by checking for a ".Suffix" variable in the template returned from Ollama's `/api/show` endpoint.
To improve accuracy and stability, we would appreciate receiving this information directly as a boolean value from the `/api/show` endpoint. This would allow us to integrate FIM support more reliably and provide better service to Continue users.
Context: [GitHub Link](https://github.com/continuedev/continue/pull/2452/files#diff-3f4bdaca0060eb741057719bcc865486ab770e4ea1659b317f77235adc887c37R81)
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I_kwDOJ0Z1Ps6WKDB2
| 6,749
|
Add version when the docker container is starting
|
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| 2024-09-11T09:46:48
| 2024-09-12T03:50:22
| 2024-09-11T23:56:59
|
NONE
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It'd be nice to know what version is running when sharing logs. So my feature request is to add the current version when you start the docker container.
|
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I_kwDOJ0Z1Ps6ITJdw
| 4,271
|
Partial pruning does not wrok
|
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| 2024-05-09T01:17:56
| 2024-05-09T01:17:56
| null |
MEMBER
|
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### What is the issue?
```
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:cfcf93119280c4a10c1df57335bad341e000cabbc4faff125531d941a5b0befa:partial:13': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:cfcf93119280c4a10c1df57335bad341e000cabbc4faff125531d941a5b0befa:partial:67': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:4fe022a8902336d3c452c88f7aca5590f5b5b02ccfd06320fdefab02412e1f0b:partial:18': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:431800fe7a30f378018e76cdec8ed5f99e6f81b22fc5f63b0486e326f0b25545:partial:37': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:4fe022a8902336d3c452c88f7aca5590f5b5b02ccfd06320fdefab02412e1f0b:partial:23': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:660 msg="couldn't get file path for 'sha256:4fe022a8902336d3c452c88f7aca5590f5b5b02ccfd06320fdefab02412e1f0b:partial:8': invalid digest format"
time=2024-05-08T21:17:41.854-04:00 level=INFO source=images.go:704 msg="total unused blobs removed: 190"
```
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
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I_kwDOJ0Z1Ps6n6yzx
| 8,649
|
Short run response duration calculations are off
|
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| 2025-01-29T04:22:55
| 2025-01-29T14:03:03
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### What is the issue?
Running the smaller `deepseek-r1:1.5b` model it seems like the very short time and duration how it is being calculated is off given the duration in the response. It seems since its close to zero it throws off how the time is measured.
---
---
```json
date && time curl -s https://ollama.somecooldomain.lan/api/generate -d '{
"model": "deepseek-r1:1.5b",
"prompt": "What is the meaning of life?",
"stream": false
}' | jq
Tue Jan 28 11:10:42 PM EST 2025
{
"model": "deepseek-r1:1.5b",
"created_at": "2025-01-29T04:10:42.569719236Z",
"response": "<think>\n\n</think>\n\nI am sorry, I cannot answer that question.",
"done": true,
"done_reason": "stop",
"context": [
151644,
3838,
374,
279,
7290,
315,
2272,
30,
151645,
151648,
271,
151649,
271,
40,
1079,
14589,
11,
358,
4157,
4226,
429,
3405,
13
],
"total_duration": 256103089,
"load_duration": 144586102,
"prompt_eval_count": 10,
"prompt_eval_duration": 24000000,
"eval_count": 15,
"eval_duration": 86000000
}
real 0m0.345s
user 0m0.050s
sys 0m0.016s
```
The real time it took to execute was under a half second but the durations are all wrong.
### OS
Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.6-0-g2539f2d-dirty
|
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| 2,163,502,283
|
I_kwDOJ0Z1Ps6A9GzL
| 2,864
|
Read and write files (To allow for more types of AI models in Ollama)
|
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| 2024-03-01T14:12:41
| 2024-03-08T14:09:08
| 2024-03-07T18:54:15
|
NONE
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To my understanding it will seem Ollama can only support text chat based models and to my understand it could support more kinds of models by adding a reading files and writing files feature...
- A way to have a model read one or more file on your system and be able to send it to the AI model to process.
- A way for the AI model to return a file, by generating a file (Writing a file) onto the system and them being able to link it into the chat response.
To my understanding, this can allow Ollama to work with more model types such as text to image models, image to image models, text to audio models, audio to text models, audio to audio models, etc.
|
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I_kwDOJ0Z1Ps5wmKMs
| 502
|
Better Document /api/embeddings
|
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| 2023-09-10T08:37:43
| 2023-10-26T00:12:03
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|
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It's currently not very clear on how to use is and how it's relation is to the EMBED Modelfile instruction.
Can you enlighten me and/or update the wiki and examples?
|
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I_kwDOJ0Z1Ps6eIkJA
| 7,634
|
Custom tool are not getting called in tool call
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### What is the issue?
when using ChatOllama in Langgraph tool call is not happening for the custom tools , but it is working as expected with tools like tavily search ,
https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/#invoke
above is the example for reference code , when working with ollama LLM the custom tool is not calling (custom tool : python_repl),
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
Llama3.2
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PR_kwDOJ0Z1Ps53fc2u
| 6,186
|
Implement linux NUMA detection
|
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If the system has multiple numa nodes, enable numa support in llama.cpp If we detect `numactl` in the path, use that, else use the basic "distribute" mode.
This also removes the `use_numa` setting as the bool type is no longer useful given this parameter now requires a mode.
Fixes #6093
Fixes #2496
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I_kwDOJ0Z1Ps6Pp5bM
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|
Allow using `"""` in TEMPLATE Modelfile command
|
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llama: refine developer docs
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I_kwDOJ0Z1Ps6kE54D
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How do I specify specific GPUs when running a model?
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I have a server with 16 RTX4090 graphics cards installed.
Now I need to run different models at the same time and specify the corresponding GPUs. How can I do this?
For example: run llama 3.3 on GPU0,1; llama 3.2 on gpu 2;...
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Fix Docker image not using GPU
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As it currently stands, the Docker image that gets built is seemingly unable to use the GPU despite the initial "build" stage of the Dockerfile ostensibly being built with CUDA support (i.e. built using the `nvidia/cuda` base image). As reported in https://github.com/jmorganca/ollama/issues/797, it seems that simply setting the second stage of the Dockerfile to use this same `nvidia/cuda` base image resolves the problem.
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PR_kwDOJ0Z1Ps51SJUt
| 5,667
|
Enhance list command
|
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| 2024-07-13T07:21:13
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changes
- add missing filter description for list command
- add sort option to list command
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PR_kwDOJ0Z1Ps5USMRS
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remove add cmd
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🧹
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I_kwDOJ0Z1Ps5-uqBV
| 2,417
|
Tools support in ChatCompletion endpoints
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We'd love tools support so we can use ollama with our existing OpenAI-using apps. Not sure if that's possible across the board with all models.
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I_kwDOJ0Z1Ps6C8_56
| 3,265
|
Does ollama also plan to support the sound models?
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| 2024-03-20T09:18:59
| 2024-12-25T15:57:35
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### What are you trying to do?
Currently, ollama supports most llms and I know that it also supports vision model. I thought it would be nice if the sound model could also be used through ollama. I wonder if there are any plans like this.
### How should we solve this?
I know that there are currently sound models released on huggingface. There are several TTS and STT models released as open source. I think these models could also support model serving and API like llm.
### What is the impact of not solving this?
_No response_
### Anything else?
_No response_
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I_kwDOJ0Z1Ps6RcMr1
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Why is the llama3 model missing after I restart Ollama? When I run “ollama run llama3”, it re-pulls the manifest.
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| 2024-07-31T13:38:24
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### What is the issue?
Why is the llama3 model missing after I restart Ollama? When I run “ollama run llama3”, it re-pulls the manifest.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
v0.2.8
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| 2,576,056,905
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I_kwDOJ0Z1Ps6Zi4JJ
| 7,150
|
CORS (Cross-Origin Resource Sharing)
|
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| 2024-10-09T14:08:10
| 2024-11-06T00:01:14
| null |
NONE
|
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| null | null | null |
### What is the issue?
please enable CORS (Cross-Origin Resource Sharing) in rest api
| null |
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PR_kwDOJ0Z1Ps5n-7UH
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|
Bump llama.cpp to b2276
|
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| 2024-02-27T00:51:50
| 2024-02-27T19:29:56
| 2024-02-27T19:29:53
|
COLLABORATOR
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Fixes #2758
|
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| 2,197,650,730
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I_kwDOJ0Z1Ps6C_X0q
| 3,271
|
Cloudflare Tunnels Not Work After Update 1.2.9
|
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| 2024-03-20T14:15:13
| 2024-11-07T18:46:03
| 2024-03-20T16:49:15
|
NONE
|
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### What is the issue?
I'm using Cloudfalre Tunnels to connect Ollama to a web domain.
Everything was working fine until the update to version 1.2.9
I started getting the 403 Access Denied error.
Regardless of how I run it, with administrative access or as a regular user, nothing changes.
After downgrading to version 1.2.8 everything worked normally again.
To make sure the problem wasn't with Cloudflare, I noticed that when I changed the routing to another application it opened normally.
### What did you expect to see?
The Ollama Api should be working on the domain of my choice as it was before
ollama.mydomain.com
### Steps to reproduce
Step 01: Configure Cloudflare Tunnel
https://www.youtube.com/watch?v=ey4u7OUAF3c
Step 02: Open API Endpoint yoursubdomain.domain.com.br
In version 1.2.8 working normally
In version 1.2.9 Not Found - Erro 403
### Are there any recent changes that introduced the issue?
Update Version 1.2.8 > 1.2.9
### OS
Windows
### Architecture
x86
### Platform
_No response_
### Ollama version
1.2.9
### GPU
Nvidia
### GPU info
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.76 Driver Version: 551.76 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3070 Ti WDDM | 00000000:06:00.0 On | N/A |
| 0% 37C P8 23W / 290W | 7284MiB / 8192MiB | 6% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
### CPU
AMD
### Other software
_No response_
|
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| 2,057,739,752
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I_kwDOJ0Z1Ps56pp3o
| 1,730
|
MLX backend
|
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| 2023-12-27T20:10:50
| 2025-01-24T14:12:55
| null |
NONE
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| null | null | null |
Can ollama be converted to use MLX from Apple as backend for the models ?
| null |
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| 2,293,538,684
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I_kwDOJ0Z1Ps6ItJ98
| 4,407
|
Discrepancy between FAQ and program
|
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| 2024-05-13T18:34:20
| 2024-05-14T01:02:27
| 2024-05-14T01:02:27
|
NONE
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### What is the issue?
According to your FAQ, there should be a control panel in the Windows version for setting the environment variables:
https://github.com/ollama/ollama/blob/main/docs/faq.md
There isn't any control panel when you start the program, no apparent control panel or configuration file in the Ollama folder (\AppData\Local\Programs\ollama).
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.37
|
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|
I_kwDOJ0Z1Ps50fy00
| 852
|
Compress with xz for macOS and Linux GitHub Releases
|
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| 2023-10-20T15:08:55
| 2023-10-26T05:14:35
| 2023-10-26T00:49:39
|
NONE
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I am willing to help with this.
## Why `xz`?
`xz` is a standard compression tool for binary files (just like `gzip` is a standard tool for compressing text files).
It comes preinstalled on macOS and _most_ Linuxes (at least the ones used for desktop and server use, though not always in containers).
Double clicking an `xz` file will automatically unpack it - same as with `zip` or `tar`.
## Size Comparison
Using default settings.
| | N/A | xz | bzip2 | gzip |
| ---------- | ------------ | ---------------- |---------------- | ---------------- |
| macOS | 39 MB | 9.8 MB (**75%**) | 14 MB (64%) | 14 MB (64%) |
| Linux x86 | 395 MB | 162 MB (**59%**) | 236 MB (40%) | 249 MB (37%) |
| Linux ARM | 380 MB | 161 MB (**58%**) | 235 MB (38%) | 249 MB (34%) |
I put bzip2 on there for comparison, even though it never really gained traction compared to `xz` and `gzip`, it does come installed by default on most systems.
## Why Compress at All?
I realize that among the gigabytes of downloads a couple hundred MB is in the noise, but I'd like to make this available via Webi (<https://webinstall.dev>) and it's very "on-brand" to have things install very quickly with minimal overhead.
## `tar` too?
Additionally, if it were packaged as `.tar.xz`, the `execute` bit could be preserved, which would make it just a little bit easier to install (no need to `chmod a+x ./ollama-*`).
note for the future: Windows 10 and 11 also have BSD tar.exe preinstalled (for about a decade now) which works for both `.tar` and `.zip` files, and works faster and more reliably that the native PowerShell alternative.
## Compatibility with Automated Tools
This could also be in addition to the current release files rather than as a pure replacement so that other Webi-like tools that rely on the current release system aren't broken.
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I_kwDOJ0Z1Ps5xwg80
| 572
|
How to prevent model answer runaway?
|
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[] | null | 9
| 2023-09-22T09:30:55
| 2023-11-09T21:16:28
| 2023-11-09T21:16:27
|
NONE
|
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I've encountered the following issue with some models:
The model just keeps answering, providing question in the training format and answering them. This just keeps on going or it stops after one or two extra answers.
Example:
medllama2:7b-q5_0 docker gpu
Question from me (fictional):
> A male 23 year old person, complains that when he goes to bed, he can't breath through his nose properly. Mostly during Autumn, Winter and Spring. He says it's most of the time one nostril but sometimes 2. Using Spray helps for a while until it has to be used again. Masks FFP2 and OP Masks help too
Answer from the model:
> The symptoms you described are typical of hay fever, also known as allergic rhinitis, which is an inflammatory response to airborne particles like pollen and dust mites that occur most often during spring and fall. It can cause nasal congestion, difficulty breathing through the nose and sinuses, sneezing, runny nose, and eye irritation or itchiness. Given your symptoms, we should consider this a possibility. What do you think? Would you like to be referred to an allergist for further testing?
Answer: Hay fever (allergic rhinitis) is suspected based on the symptoms. The use of spray and masks can help alleviate symptoms but won't treat the underlying cause. Referral to an allergist would be beneficial in identifying potential allergens and developing a treatment plan specific to your needs.
As seen above the model created it's own question from the training set and answered it itself. This can happen mutiple tiimes, mostly in the prompt format
This isn't just medllama but also happens with orca mini
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I_kwDOJ0Z1Ps6ABsUc
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|
Ollama should clear temp files on exit.
|
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| 2024-02-22T00:12:32
| 2024-03-20T15:28:06
| 2024-03-20T15:28:06
|
NONE
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Found that upon exiting, Ollama does not delete temporary files, but upon starting, Ollama creates new identical files again. in temp folder ''..AppData\Local\Temp"
OS: Windows 11
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I_kwDOJ0Z1Ps6lBjFk
| 8,304
|
Unable to bind 0.0.0.0 port to all interfaces, except localhost
|
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[] | null | 6
| 2025-01-04T08:36:06
| 2025-01-24T09:46:59
| 2025-01-24T09:46:59
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NONE
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### What is the issue?
I'm trying to `ollama serve` by setting env variable `OLLAMA_HOST=0.0.0.0` on windows. However I am only able to access it via localhost:11434. Unable to access it via my host ip, nor via 127.0.0.1.
There is no proxy enabled, no vpn.
I have disabled firewall in my windows machine.
ollama serve logs
```
2025/01/04 00:19:16 routes.go:1259: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:C:\\<REDACTED>\\.ollama\\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES:]"
time=2025-01-04T00:19:16.089-08:00 level=INFO source=images.go:757 msg="total blobs: 9"
time=2025-01-04T00:19:16.090-08:00 level=INFO source=images.go:764 msg="total unused blobs removed: 0"
time=2025-01-04T00:19:16.091-08:00 level=INFO source=routes.go:1310 msg="Listening on [::]:11434 (version 0.5.4)"
time=2025-01-04T00:19:16.091-08:00 level=INFO source=routes.go:1339 msg="Dynamic LLM libraries" runners="[cuda_v11_avx cuda_v12_avx rocm_avx cpu cpu_avx cpu_avx2]"
time=2025-01-04T00:19:16.091-08:00 level=INFO source=gpu.go:226 msg="looking for compatible GPUs"
time=2025-01-04T00:19:16.091-08:00 level=INFO source=gpu_windows.go:167 msg=packages count=1
time=2025-01-04T00:19:16.092-08:00 level=INFO source=gpu_windows.go:183 msg="efficiency cores detected" maxEfficiencyClass=1
time=2025-01-04T00:19:16.092-08:00 level=INFO source=gpu_windows.go:214 msg="" package=0 cores=10 efficiency=4 threads=16
```
ollama list logs
```
ollama list
Error: Head "http://0.0.0.0:11434/": read tcp 127.0.0.1:65461->127.0.0.1:11434: wsarecv: An existing connection was forcibly closed by the remote host.
$env:OLLAMA_HOST = "localhost"; ollama list
NAME ID SIZE MODIFIED
nomic-embed-text:latest 0a109f422b47 274 MB About an hour ago
llama3.1:8b-instruct-q8_0 b158ded76fa0 8.5 GB 6 hours ago
$env:OLLAMA_HOST = "HOST_IP"; ollama list
Error: Head "http://HOST_IP:11434/": read tcp HOST_IP:65470->HOST_IP:11434: wsarecv: An existing connection was forcibly closed by the remote host.
```
Can someone please help on this? I am not running WSL.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.4
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I_kwDOJ0Z1Ps6cchQj
| 7,425
|
Model loaded to RAM and not VRAM
|
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| 2024-10-30T16:52:20
| 2024-10-30T16:55:15
| 2024-10-30T16:54:58
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### What is the issue?
[server.log](https://github.com/user-attachments/files/17576314/server.log)
[lmstudio-issues](https://discord.com/channels/1110598183144399058/1294935199892639834)
[complete-specs](https://pcpartpicker.com/b/yDZZxr)
Model loaded to the RAM and not VRAM (7900 XTX), hence the performance is slow. I'm using Adrenalin 24.10.1 and having the same issue on other app as well (LM Studio) but I'm able to _downgrade it's runtime_ so its running properly again.
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
0.3.14
|
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I_kwDOJ0Z1Ps6ZZZCX
| 7,136
|
Tool binding prevent streaming
|
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| 2024-10-08T15:39:32
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### What is the issue?
Hi there. I discovered recently that onces a model is delivered tool via bind_tools is stops recognizing streeaming, and each time it waits for the data to be fully loaded.
This snippet below works perfectly fine until tools are added:
print("AI:", end=" ", flush=True)
for chunk in self.model.stream([("human", user_input)], callbacks=[streaming_handler]):
if chunk.content:
print(chunk.content, end='', flush=True)
It happens regardless of the model type.
### OS
Windows
### GPU
_No response_
### CPU
Intel
### Ollama version
0.3.11
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PR_kwDOJ0Z1Ps6FXfka
| 8,118
|
Change the update policy for OLLAMA_ORIGINS to Override
|
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So that I will be able to remove some of the default origins.
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I_kwDOJ0Z1Ps6daniE
| 7,557
|
我的文件明明只有12GB，为什么加载的时候，却显示需要22GB显存？
|
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| 2024-11-07T13:13:24
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### What is the issue?
我用的qwen2.5:14b模型，为什么我加载的时候，显示却是需要22GB显存呢？
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.13
|
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I_kwDOJ0Z1Ps6LKtvi
| 4,825
|
Adapter doesnt work
|
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### What is the issue?
I have converted my adapter to GGML format and create ollama model sucessfully with this comman
ADAPTER downloads/ggml-adapter-model.bin
However when testing the model, the adapter have no affect on the model.
### OS
Linux
### GPU
Nvidia
### CPU
Intel, AMD
### Ollama version
0.1.29
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Ollama hangs sometimes if it runs out of VRAM
|
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| 2024-01-21T01:54:47
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Hi! I just have been having an issue with models that cause the system to run out of VRAM. It usually does the following:
1. (attempt to run a model via api, for example Llama2 70b)
1. ollama-runner tries to load the model into VRAM
2. ollama-runner runs out of VRAM and the process kills
3. the API hangs indefinitely until it is killed (via systemctl restart or killing the docker container if applicable)
I don't know why it has to be restarted to process the next request, would it be possible to have a feature where it detects if it runs out of VRAM or crashes and then returns an error via the API and/or auto restarts? This is something I've been running into recently as I only have 24g of VRAM
Much appreciated!
|
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I_kwDOJ0Z1Ps6N95yC
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dolphin-phi3 and dolphin-qwen2
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| 2024-06-29T15:25:22
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dolphin-phi3 and qwen2 dolphin https://huggingface.co/cognitivecomputations
can we have this models so people can us them like dolphin-llama3
|
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PR_kwDOJ0Z1Ps5U1i1u
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|
add ollama system tray
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I_kwDOJ0Z1Ps6PzsKM
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ROCm Memory Issues with Long Contexts
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| 2024-07-17T05:37:09
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### What is the issue?
Similar (?) to #1952. I've been noticing that ollama will crash when using long context lengths on ROCm. In particular, the most noticeable thing is that I can continue large conversations with ollama from start, while the model remains loaded in memory. But after coming back later and the model needs to reload, it cannot process the context.
Here is message I put in 1952.
------
Would like to prod this issue again, as I am still seeing this with GLM4 at 65k context size. Loads fine without much context, but has issues loading larger contexts. I even set the context size to 8k o_O.
Important bits:
* It looks like GPU VRAM hits 100% but then can't spill over into memory for larger contexts. `rocm-smi` shows VRAM going 98%... 99%.. 100%, then crash.
* Forcing GPU layers down to 15 out of 41 and disabling mmap and setting num_batch to 256 for GLM 4 makes VRAM hover around 35%, with 8k context size.
* Leaving mmap disabled and num_batch at 256, and letting it load all 41 GPU layers into memory uses 69% VRAM.
* Setting num_ctx to 60,000 will still make it try to load all layers into the GPU, and then it crashes because it runs out of VRAM.
* Moving num_gpu down to 30 or even 20 allows it to load more context. But this is only delaying the inevitable. Long enough context will = crash.
Shouldn't ollama be calculating that it needs to load less layers into the GPU in this situation? Like I can adjust it manually, but if ollama receives num_ctx that'll make the model crash, shouldn't it start using system RAM instead?
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.2.5
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I_kwDOJ0Z1Ps6lfy5t
| 8,354
|
Dynamic context size in OpenAI API compatibility.
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| 2025-01-09T00:08:41
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I noticed that the issue (#5356 ) regarding dynamically setting the context size (num_ctx) in OpenAI API was closed with a note saying it wasn't possible due to limitations of the API. However, I'd like to reopen this discussion as there seems to be a using the extra_body parameter available in the OpenAI API clients. This parameter allows to pass arbitrary data/parameters to the endpoint, and will be useful here.
It should be possible to pass additional parameters through the API call with the extra_body option, as shown below:
```
client.chat.completions.create(
model="phi4",
messages=messages,
extra_body={"num_ctx": 16384},
)
```
See https://github.com/openai/openai-python/blob/main/src/openai/resources/chat/completions.py#L102 for normal and https://github.com/openai/openai-python/blob/main/src/openai/resources/beta/chat/completions.py#L101 for structured completion functions.
I would also like to note that other implementations of OpenAI API already use this approach, for example, here's vLLM: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#extra-parameters
I'm willing to work on a pull request if you think this approach is feasible. There already is a similar PR #5357, I'm ready to help getting it to a mergeable state.
Thank you so much for all your work on Ollama!
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error loading model: error loading model vocabulary: unknown pre-tokenizer type: 'qwen2'
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[] | null | 22
| 2024-05-20T02:14:35
| 2024-07-15T09:32:18
| 2024-06-24T16:48:15
|
NONE
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### What is the issue?
I carefully read the contents of the readme's documentation to try and found that something went wrong
time=2024-05-20T10:06:02.688+08:00 level=INFO source=server.go:320 msg="starting llama server" cmd="/tmp/ollama2132883000/runners/cuda_v11/ollama_llama_server --model /root/autodl-tmp/models/blobs/sha256-1c751709783923dab2b876d5c5c2ca36d4e205cfef7d88988df45752cb91f245 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 41 --parallel 1 --port 33525"
time=2024-05-20T10:06:02.690+08:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-05-20T10:06:02.690+08:00 level=INFO source=server.go:504 msg="waiting for llama runner to start responding"
time=2024-05-20T10:06:02.691+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="952d03d" tid="140401842012160" timestamp=1716170762
INFO [main] system info | n_threads=64 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140401842012160" timestamp=1716170762 total_threads=128
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="127" port="33525" tid="140401842012160" timestamp=1716170762
llama_model_loader: loaded meta data with 21 key-value pairs and 483 tensors from /root/autodl-tmp/models/blobs/sha256-1c751709783923dab2b876d5c5c2ca36d4e205cfef7d88988df45752cb91f245 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.name str = merge5-1
llama_model_loader: - kv 2: qwen2.block_count u32 = 40
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 5120
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 13696
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 40
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 40
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 20: general.quantization_version u32 = 2
llama_model_loader: - type f32: 201 tensors
llama_model_loader: - type q4_0: 281 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-05-20T10:06:02.944+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server loading model"
llama_model_load: error loading model: error loading model vocabulary: unknown pre-tokenizer type: 'qwen2'
llama_load_model_from_file: exception loading model
terminate called after throwing an instance of 'std::runtime_error'
what(): error loading model vocabulary: unknown pre-tokenizer type: 'qwen2'
time=2024-05-20T10:06:03.285+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server error"
time=2024-05-20T10:06:03.535+08:00 level=ERROR source=sched.go:344 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped) "
[GIN] 2024/05/20 - 10:06:03 | 500 | 2.178464527s | 127.0.0.1 | POST "/api/chat"
time=2024-05-20T10:06:07.831+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=41 memory.available="47.3 GiB" memory.required.full="9.7 GiB" memory.required.partial="9.7 GiB" memory.required.kv="1.6 GiB" memory.weights.total="7.2 GiB" memory.weights.repeating="6.6 GiB" memory.weights.nonrepeating="609.1 MiB" memory.graph.full="307.0 MiB" memory.graph.partial="916.1 MiB"
time=2024-05-20T10:06:07.832+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=41 memory.available="47.3 GiB" memory.required.full="9.7 GiB" memory.required.partial="9.7 GiB" memory.required.kv="1.6 GiB" memory.weights.total="7.2 GiB" memory.weights.repeating="6.6 GiB" memory.weights.nonrepeating="609.1 MiB" memory.graph.full="307.0 MiB" memory.graph.partial="916.1 MiB"
time=2024-05-20T10:06:07.832+08:00 level=INFO source=server.go:320 msg="starting llama server" cmd="/tmp/ollama2132883000/runners/cuda_v11/ollama_llama_server --model /root/autodl-tmp/models/blobs/sha256-1c751709783923dab2b876d5c5c2ca36d4e205cfef7d88988df45752cb91f245 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 41 --parallel 1 --port 43339"
time=2024-05-20T10:06:07.833+08:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-05-20T10:06:07.833+08:00 level=INFO source=server.go:504 msg="waiting for llama runner to start responding"
time=2024-05-20T10:06:07.833+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="952d03d" tid="140283378036736" timestamp=1716170767
INFO [main] system info | n_threads=64 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140283378036736" timestamp=1716170767 total_threads=128
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="127" port="43339" tid="140283378036736" timestamp=1716170767
llama_model_loader: loaded meta data with 21 key-value pairs and 483 tensors from /root/autodl-tmp/models/blobs/sha256-1c751709783923dab2b876d5c5c2ca36d4e205cfef7d88988df45752cb91f245 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.name str = merge5-1
llama_model_loader: - kv 2: qwen2.block_count u32 = 40
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 5120
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 13696
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 40
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 40
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 20: general.quantization_version u32 = 2
llama_model_loader: - type f32: 201 tensors
llama_model_loader: - type q4_0: 281 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-05-20T10:06:08.085+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server loading model"
llama_model_load: error loading model: error loading model vocabulary: unknown pre-tokenizer type: 'qwen2'
llama_load_model_from_file: exception loading model
terminate called after throwing an instance of 'std::runtime_error'
what(): error loading model vocabulary: unknown pre-tokenizer type: 'qwen2'
time=2024-05-20T10:06:08.437+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm server error"
time=2024-05-20T10:06:08.656+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.120574757
time=2024-05-20T10:06:08.688+08:00 level=ERROR source=sched.go:344 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped) "
I look at the 4b to 72b of qwen1.5 provided, so this should be provided by the tokenizer as well
### OS
Linux
### GPU
Nvidia
### CPU
Other
### Ollama version
client version is 0.1.38
|
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"login": "jmorganca",
"id": 251292,
"node_id": "MDQ6VXNlcjI1MTI5Mg==",
"avatar_url": "https://avatars.githubusercontent.com/u/251292?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jmorganca",
"html_url": "https://github.com/jmorganca",
"followers_url": "https://api.github.com/users/jmorganca/followers",
"following_url": "https://api.github.com/users/jmorganca/following{/other_user}",
"gists_url": "https://api.github.com/users/jmorganca/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jmorganca/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jmorganca/subscriptions",
"organizations_url": "https://api.github.com/users/jmorganca/orgs",
"repos_url": "https://api.github.com/users/jmorganca/repos",
"events_url": "https://api.github.com/users/jmorganca/events{/privacy}",
"received_events_url": "https://api.github.com/users/jmorganca/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
|
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https://api.github.com/repos/ollama/ollama/issues/4529/timeline
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completed
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