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https://api.github.com/repos/ollama/ollama/issues/5915
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PR_kwDOJ0Z1Ps52WWxZ
| 5,915
|
added tools when `stream=true`
|
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NONE
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fixes #5796
```
curl --location '127.0.0.1:11434/v1/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"messages": [
{
"content": "You are a helpful AI assistant that can use tools.",
"role": "system"
},
{
"content": "What is function_1(10, 11)? use provided tools",
"role": "user"
}
],
"model": "llama3-groq-tool-use:8b",
"logprobs": false,
"n": 1,
"stream": true,
"temperature": 0,
"tools": [
{
"type": "function",
"function": {
"name": "function_1",
"description": "uses function function_1 for arguments a and b.",
"parameters": {
"type": "object",
"properties": {
"a": {
"type": "integer"
},
"b": {
"type": "integer"
}
},
"required": [
"a",
"b"
]
}
}
}
]
}'
```
chunks:
```
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\u003ctool_call\u003e"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\n"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"{\""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"id"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\":"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" "},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"0"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" \""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"name"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\":"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" \""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"function"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"_"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"1"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\","},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" \""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"arguments"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\":"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" {\""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"a"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830176,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\":"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" "},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"10"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" \""},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"b"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\":"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" "},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"11"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"}}\n"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"\u003c/tool_call\u003e"},"finish_reason":null}]}
data: {"id":"chatcmpl-20","object":"chat.completion.chunk","created":1721830177,"model":"llama3-groq-tool-use:8b","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_ejieksiz","type":"function","function":{"name":"function_1","arguments":"{\"a\":10,\"b\":11}"}}]},"finish_reason":"tool_calls"}]}
data: [DONE]
```
last chunk:
```
{
"id": "chatcmpl-20",
"object": "chat.completion.chunk",
"created": 1721830177,
"model": "llama3-groq-tool-use:8b",
"system_fingerprint": "fp_ollama",
"choices": [
{
"index": 0,
"delta": {
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ejieksiz",
"type": "function",
"function": {
"name": "function_1",
"arguments": "{\"a\":10,\"b\":11}"
}
}
]
},
"finish_reason": "tool_calls"
}
]
}
```
also works with `/api/chat`
also added `"finish_reason": "tool_calls"` for completions
|
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"repos_url": "https://api.github.com/users/royjhan/repos",
"events_url": "https://api.github.com/users/royjhan/events{/privacy}",
"received_events_url": "https://api.github.com/users/royjhan/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
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https://api.github.com/repos/ollama/ollama/issues/5915/timeline
| null | null | true
|
https://api.github.com/repos/ollama/ollama/issues/7435
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https://api.github.com/repos/ollama/ollama
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https://github.com/ollama/ollama/issues/7435
| 2,625,700,359
|
I_kwDOJ0Z1Ps6cgQIH
| 7,435
|
加载训练生成的gguf，/v1/chat/completions和/api/generate接口返回的结果不一样
|
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"id": 4754730,
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"avatar_url": "https://avatars.githubusercontent.com/u/4754730?v=4",
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"url": "https://api.github.com/users/czhcc",
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"followers_url": "https://api.github.com/users/czhcc/followers",
"following_url": "https://api.github.com/users/czhcc/following{/other_user}",
"gists_url": "https://api.github.com/users/czhcc/gists{/gist_id}",
"starred_url": "https://api.github.com/users/czhcc/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/czhcc/subscriptions",
"organizations_url": "https://api.github.com/users/czhcc/orgs",
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"events_url": "https://api.github.com/users/czhcc/events{/privacy}",
"received_events_url": "https://api.github.com/users/czhcc/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
|
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closed
| false
| null |
[] | null | 1
| 2024-10-31T01:08:25
| 2024-11-17T14:17:43
| 2024-11-17T14:17:43
|
NONE
|
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| null | null | null |
### What is the issue?
训练的基础模型是qwen2.5-7b。
对于同样的内容，
/v1/chat/completions和/api/generate接口返回不同的结果。
/api/generate返回是正确的训练要求的结果。
### OS
Linux, Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.14
|
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"site_admin": false
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| 2,761,465,899
|
I_kwDOJ0Z1Ps6kmKAr
| 8,260
|
For CPU systems, getting files which are being used for GPU
|
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"received_events_url": "https://api.github.com/users/Abubakkar13/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
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[] | null | 2
| 2024-12-28T04:56:52
| 2024-12-29T03:14:47
| 2024-12-29T03:14:46
|
NONE
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| null | null | null |
### What is the issue?
Hey,
Happy holidays 😍!!!
I just came across with Ollama version update, then saw some files are being downloaded, I noticed that few dll libraries are being download, So i just cross checked the folder there are 7 files(Specifically starting with **cuda*** or **cublas*** ) and in runners folder as well, have downloaded. But these are for GPU acceleration, since i am with CPU only, is their a way to ignore these files or is their a way to do a system check just before installing the Ollama so can ignore while installing itself, so it can reduce 2.8GB of space for CPU users?
Folder is:
1. Taking around 880M: C:\Users\[USER}\AppData\Local\Programs\Ollama\lib\ollama
2. Taking around 2GB: C:\Users\[USER]\AppData\Local\Programs\Ollama\lib\ollama\runners
Thank you
From GPU Poor 😅
### OS
Windows
### GPU
_No response_
### CPU
Intel
### Ollama version
0.5.4
|
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PR_kwDOJ0Z1Ps5YRY9L
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|
Closes #371
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| 2023-08-18T18:43:29
| 2023-08-22T19:21:00
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NONE
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Hello,
ModelPath.ParseModelPath(name) now trims https:// from name if it is present. Keeping in the scope of the issue, no alternative protocols are accepted and a path with no protocol defaults to https as before. My first approach would've allowed garbage text in the protocol, but this lacks that side-effect.
|
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| 2,482,749,930
|
I_kwDOJ0Z1Ps6T-8Hq
| 6,473
|
OpenAI Structured Output Compatability
|
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| 2024-08-23T09:42:33
| 2024-12-05T00:45:49
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NONE
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Hi 👋🏻
Loving ollama always ❤️
I'm eager to use newly released structured output using ollama but it looks like ollama doesn't have compatibility yet so I can just put base_url and I'll get response from my local LLM.
Also, I would like will it support [streaming](https://python.useinstructor.com/concepts/partial/) like instructor?
Thanks.
|
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| 2,288,040,474
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PR_kwDOJ0Z1Ps5vAnA9
| 4,292
|
remove last bits of ParseModelPath
|
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| 2024-08-29T23:26:19
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CONTRIBUTOR
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follow up to #4291
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I_kwDOJ0Z1Ps6OV6dv
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|
ollama does not work on ALL GPU automatically
|
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### What is the issue?
when I use the ollama:0.1.38, I use the following command to start:
`
docker run -d --gpus=all -v /root/ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
`
ollama will automatically use all 4 gpu cards for inference.
when I upgrate to ollama:0.1.48, I use the same command, but it only use 1 gpu for inference:
I remember the running 'Processes' is not '...unners/cuda_v11/ollama_llama_server', should be "ollama/ollama" or something else before.
Why this happens?
Also, when I use
`
docker run -d --gpus=all -v /root/ollama:/root/.ollama -p 11434:11434 -e OLLAMA_SCHED_SPREAD=1 --name ollama ollama/ollama:0.1.48
`
to force it use all 4 gpus for inference, I noticed that the combined utilization of the four GPU cards will not reach 100%, let alone 400%.
Here is the gpu usage I monitor.
[gpu_usage.csv](https://github.com/user-attachments/files/16081349/gpu_usage.csv)
I think ollama does not make full use of GPU resources. Why is this?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.48
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PR_kwDOJ0Z1Ps58jqoL
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|
docs: update llamaindex links
|
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| 2024-09-24T18:44:21
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I_kwDOJ0Z1Ps565BBC
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|
Older CUDA compute capability 3.5 and 3.7 support
|
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I recently put together an (old) physical machine with an Nvidia K80, which is only supported up to CUDA 11.4 and Nvidia driver 470. All my previous experiments with Ollama were with more modern GPU's.
I found that Ollama doesn't use the GPU at all. I cannot find any documentation on the minimum required CUDA version, and if it is possible to run on older CUDA versions (e.g. Nvidia K80, V100 are still present on cloud, e.g. G2 and P2 on AWS) and there's lots of K80's all over ebay.
EDIT: looking through the logs, it appears that the GPU's are being seen:
Jan 1 20:22:43 thinkstation-s30 ollama[911]: 2024/01/01 20:22:43 llama.go:300: 24762 MB VRAM available, loading up to 162 GPU layers
Jan 1 20:22:43 thinkstation-s30 ollama[911]: 2024/01/01 20:22:43 llama.go:436: starting llama runner
Jan 1 20:22:43 thinkstation-s30 ollama[911]: 2024/01/01 20:22:43 llama.go:494: waiting for llama runner to start responding
Jan 1 20:22:43 thinkstation-s30 ollama[911]: ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
Jan 1 20:22:43 thinkstation-s30 ollama[911]: ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
Jan 1 20:22:43 thinkstation-s30 ollama[911]: ggml_init_cublas: found 3 CUDA devices:
Jan 1 20:22:43 thinkstation-s30 ollama[911]: Device 0: Tesla K80, compute capability 3.7
Jan 1 20:22:43 thinkstation-s30 ollama[911]: Device 1: Tesla K80, compute capability 3.7
Jan 1 20:22:43 thinkstation-s30 ollama[911]: Device 2: NVIDIA GeForce GT 730, compute capability 3.5
and
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: ggml ctx size = 0.11 MiB
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: using CUDA for GPU acceleration
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: mem required = 70.46 MiB
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: offloading 32 repeating layers to GPU
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: offloading non-repeating layers to GPU
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: offloaded 33/33 layers to GPU
Jan 1 20:34:20 thinkstation-s30 ollama[911]: llm_load_tensors: VRAM used: 3577.61 MiB
but....
Jan 1 20:34:21 thinkstation-s30 ollama[911]: CUDA error 209 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7801: no kernel image is available for execution on the device
Jan 1 20:34:21 thinkstation-s30 ollama[911]: current device: 0
Jan 1 20:34:21 thinkstation-s30 ollama[911]: GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7801: !"CUDA error"
Jan 1 20:34:22 thinkstation-s30 ollama[911]: 2024/01/01 20:34:22 llama.go:451: 209 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7801: no kernel image is available for execution on the device
Jan 1 20:34:22 thinkstation-s30 ollama[911]: current device: 0
Jan 1 20:34:22 thinkstation-s30 ollama[911]: GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7801: !"CUDA error"
Jan 1 20:34:22 thinkstation-s30 ollama[911]: 2024/01/01 20:34:22 llama.go:459: error starting llama runner: llama runner process has terminated
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I_kwDOJ0Z1Ps6nOzxY
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Ollama does not perform structured output correctly.
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| 2025-01-23T01:19:21
| 2025-01-27T09:10:56
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### What is the issue?
request:
`{
"model":"llama3.2",
"messages":datas+[
{
"role":"user",
"content":input_data,
"images":[screenshot_base64]
}
],
"stream":False,
"format":{
"type":"object",
"reply":{"type":"string"},
"properties":{
"operations":{"type":"array",
"instruct":{
"type":"object",
"functions":{"type":"object",
"function_name":{
"type":"string"},
"parameter":{"type":"array","items":{"type":"string"}}
}
}
}
}
},
"required":["reply"]
}`
reply:
`{"operations": []}`
Ollama did not reply with the required "reply".
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7
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PR_kwDOJ0Z1Ps6BeCEy
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|
Allow Compile on older GPUs - still on CUDA 11.3
|
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| 2024-11-11T07:31:03
| 2024-11-22T17:01:26
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NONE
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My Tesla k40s are still able to run this version of CUDA, but are technically on Compute 3.5. These commits will allow them to compile and run the software, and are working well for me.
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PR_kwDOJ0Z1Ps5l2Y_S
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Update README.md
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| 2024-02-02T16:12:29
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NONE
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Adding info on Fusion Quill. Setup info is here https://fusionquill.ai/help-setup-ollama/
Fusion Quill Personal Edition is a Windows app on the Microsoft App Store that connects to multiple AI models with workflows and UX like an Integrated Word processor with AI Chat in a split-pane UI that enables creating documents with ease!
Fusion Quill Personal Edition supports
AI Writing Buddy with Multiple AIs like Ollama, OpenAI, Mistral, Azure AI, Google Gemini, Bedrock, vLLM, etc. Chat with a Debate Coach, Interview Coach and other assistants. More info at https://FusionQuill.AI
|
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PR_kwDOJ0Z1Ps5u72dc
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Convert directly from llama3
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CONTRIBUTOR
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This change allows you to convert directly from a llama3 derived safetensors model into Ollama.
It is currently *missing*:
* pytorch *almost* works however the embeddings layer size is off by the eos/bos tokens
This *will* work with most llama3 derivatives if they are using safetensors including `dolphin-2.9-llama3`, nous research's hermes 2 pro, and nvidia's chatqa.
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I_kwDOJ0Z1Ps56Sk81
| 1,651
|
GPU not in use?
|
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| 2025-01-03T21:54:51
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I am running Ollama which was installed on an arch linux system using "sudo pacman -S ollama" I am using a RTX 4090 with Nvidia's latest drivers. I also installed cuda using "sudo pacman -S cuda"
I run the LLM using the command "ollama run dolphin-mixtral:latest"
it does not appear to use the GPU based on GPU usage provided by GreenWithEnvy (GWE), but I am unsure how to verify that information.
Is there a specific command I need to run to ensure it uses the GPU instead of the CPU?
nvidia-smi returns:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06 Driver Version: 545.29.06 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4090 Off | 00000000:01:00.0 On | Off |
| 0% 42C P8 37W / 450W | 20102MiB / 24564MiB | 11% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 3261 G /usr/lib/Xorg 632MiB |
| 0 N/A N/A 3360 G /usr/bin/gnome-shell 327MiB |
| 0 N/A N/A 3778 G /usr/lib/xdg-desktop-portal-gnome 14MiB |
| 0 N/A N/A 3973 G gjs 99MiB |
| 0 N/A N/A 4337 G /app/lib/librewolf/librewolf 219MiB |
| 0 N/A N/A 5088 G ...,WinRetrieveSuggestionsOnlyOnDemand 167MiB |
| 0 N/A N/A 67846 G /usr/bin/gnome-text-editor 13MiB |
| 0 N/A N/A 69685 C ...p/gguf/build/cuda/bin/ollama-runner 18000MiB |
| 0 N/A N/A 101455 G /usr/bin/nautilus 32MiB |
| 0 N/A N/A 101815 G /usr/bin/kgx 549MiB |
+---------------------------------------------------------------------------------------+
|
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I_kwDOJ0Z1Ps57z-A9
| 1,930
|
Support for CogVLM wanted. CogVLM is an alternative for LLaVA
|
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Currently ollama is supporting LLaVA, which is super great.
I wonder is there a chance to load other similar models like CogVLM?
https://github.com/THUDM/CogVLM
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I_kwDOJ0Z1Ps6OsBHL
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Suggestions
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| 2024-07-07T05:43:01
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Terminal color/bold codes (needs 3 and 3.%)
Custom terminal app (Vulkan for stuff like svgs? Maybe for videos?))
Selinux for English (nouns verbs pronouns)
Voice command and output
Don’t know that much assembly but I think you can daisy chain stuff
I know this is written in go, but a scripting language (if x = chem(h202))
for models haven’t tried to look at them, on the fly compression with etc, some sort of index or master index to cache, instead of printing word by word, you can print the entire thing with a progress bar (or a friendly logo!), and a custom title bar and buttons, and skins
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I_kwDOJ0Z1Ps6oH96Q
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Deepseek-671B: Error: timed out waiting for llama runner to start - progress 0.00 on 8x L40S
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### What is the issue?
Ollama (0.5.7) appears to be correctly calculating how many layers to offload to the GPU with default settings. This is on a g6e.48xlarge which has 1.5TB of RAM.
```
Jan 30 11:56:19 ip-172-31-21-180 ollama[3237]: time=2025-01-30T11:56:19.283Z level=INFO source=memory.go:356 msg="offload to cuda" layers.requested=-1 layers.model=62 layers.offload=51 layers.split=7,7,7,6,6,6,6,6 memory.available="[43.9 GiB 43.9 GiB 43.9 GiB 43.9 GiB 43.9 GiB 43.9 GiB 43.9 GiB 43.9 GiB]" memory.gpu_overhead="0 B" memory.required.full="402.1 GiB" memory.required.partial="330.4 GiB" memory.required.kv="9.5 GiB" memory.required.allocations="[41.4 GiB 41.4 GiB 41.4 GiB 40.9 GiB 41.8 GiB 41.8 GiB 40.9 GiB 40.9 GiB]" memory.weights.total="385.0 GiB" memory.weights.repeating="384.3 GiB" memory.weights.nonrepeating="725.0 MiB" memory.graph.full="1019.5 MiB" memory.graph.partial="1019.5 MiB"
Jan 30 11:56:19 ip-172-31-21-180 ollama[3237]: time=2025-01-30T11:56:19.284Z level=INFO source=server.go:376 msg="starting llama server" cmd="/usr/local/lib/ollama/runners/cuda_v12_avx/ollama_llama_server runner --model /usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9 --ctx-size 2048 --batch-size 512 --n-gpu-layers 51 --threads 96 --parallel 1 --tensor-split 7,7,7,6,6,6,6,6 --port 39933"
...
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA0 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA1 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA2 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA3 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA4 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA5 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA6 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_load_model_from_file: using device CUDA7 (NVIDIA L40S) - 44940 MiB free
Jan 30 11:56:20 ip-172-31-21-180 ollama[3237]: llama_model_loader: loaded meta data with 42 key-value pairs and 1025 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9 (version GGUF V3 (latest))
```
However, I never see the GPU VRAM usage climbing (this normally happens on my 2 x P40 setup as the model loads into VRAM)
it is stuck at this:
```
Thu Jan 30 12:06:42 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.144.03 Driver Version: 550.144.03 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 L40S On | 00000000:9E:00.0 Off | 0 |
| N/A 40C P0 81W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA L40S On | 00000000:A0:00.0 Off | 0 |
| N/A 43C P0 87W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA L40S On | 00000000:A2:00.0 Off | 0 |
| N/A 41C P0 84W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 3 NVIDIA L40S On | 00000000:A4:00.0 Off | 0 |
| N/A 40C P0 81W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 4 NVIDIA L40S On | 00000000:C6:00.0 Off | 0 |
| N/A 40C P0 79W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 5 NVIDIA L40S On | 00000000:C8:00.0 Off | 0 |
| N/A 40C P0 80W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 6 NVIDIA L40S On | 00000000:CA:00.0 Off | 0 |
| N/A 40C P0 81W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 7 NVIDIA L40S On | 00000000:CC:00.0 Off | 0 |
| N/A 39C P0 81W / 350W | 433MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 1 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 2 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 3 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 4 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 5 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 6 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
| 7 N/A N/A 4939 C ...rs/cuda_v12_avx/ollama_llama_server 424MiB |
+-----------------------------------------------------------------------------------------+
```
and at the very end I get this error:
```
Jan 30 12:01:19 ip-172-31-21-180 ollama[3237]: time=2025-01-30T12:01:19.487Z level=ERROR source=sched.go:455 msg="error loading llama server" error="timed out waiting for llama runner to start - progress 0.00 - "
Jan 30 12:01:19 ip-172-31-21-180 ollama[3237]: [GIN] 2025/01/30 - 12:01:19 | 500 | 5m4s | 127.0.0.1 | POST "/api/generate"
Jan 30 12:01:26 ip-172-31-21-180 ollama[3237]: time=2025-01-30T12:01:26.104Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=6.61651503 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
Jan 30 12:01:28 ip-172-31-21-180 ollama[3237]: time=2025-01-30T12:01:28.080Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=8.592545492 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
Jan 30 12:01:30 ip-172-31-21-180 ollama[3237]: time=2025-01-30T12:01:30.058Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=10.570809357 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
```
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.5.7
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PR_kwDOJ0Z1Ps5V1VZH
| 106
|
README typo fix
|
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| 2023-07-18T23:00:59
| 2023-07-18T23:24:58
| 2023-07-18T23:24:58
|
CONTRIBUTOR
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|
- Simple typo fix in README.md
- `13` changed to `13B` (the `B` was missing) on Nous-Hermes model in models table
- Also edited `hous-hermes` to `nous-hermes` (should be n instead of h)
|
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| 2,265,316,091
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PR_kwDOJ0Z1Ps5t0VM1
| 3,936
|
pulled model not found fix - added to FAQ
|
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| 2024-07-03T17:00:00
| 2024-07-03T16:59:59
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NONE
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I've added a fix by [sridvijay](https://github.com/sridvijay) from #3876 to the documentation.
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I_kwDOJ0Z1Ps6g3oUf
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|
Please sync with llama.cpp for the update of bert_base like models.
|
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| 2024-11-27T15:34:28
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NONE
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### What is the issue?
currently [ollama_llama_server](https://github.com/ollama/ollama/blob/main/llm/server.go#L894) can't return properly with google bert_base models, while llama.cpp have already supported, I've varified with `transformers`.
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
| null |
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| 2,238,409,901
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I_kwDOJ0Z1Ps6Fa2yt
| 3,601
|
Docker 0.1.31 the 2nd Ollama cannot use its designated GPU
|
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NONE
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### What is the issue?
I'm on Ubuntu 20.04 with two Ada6000 cards. I use docker compose to host two instance of Ollama, each has its own model because I want to use them for AutoGen. For each Ollama container I want to assign an individual GPU.
In real use, I noticed that ollama1 is working perfectly fine, while ollama2 is working but only use CPU for inference. So I would appreciate the help to get the ollama2 to pick up its designated GPU unit.
Here is my compose yaml file:
```
services:
ollama1:
image: ollama/ollama:latest
container_name: ollama_model_1
ports:
- '11435:11434'
environment:
- CUDA_VISIBLE_DEVICES=GPU-*************************
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
- /ADATAtmp/docker/ollama_model_1:/root/.ollama
networks:
- autogen_network
restart: always
open-webui1:
image: ghcr.io/open-webui/open-webui:main
container_name: open-webui_1
ports:
- "11436:8080"
volumes:
- /ADATAtmp/docker/open-webui-1:/app/backend/data
environment:
- OLLAMA_BASE_URL=http://ollama_model_1:11434
extra_hosts:
- "host.docker.internal:host-gateway"
networks:
- autogen_network
restart: always
ollama2:
image: ollama/ollama:latest
container_name: ollama_model_2
ports:
- '11437:11434'
environment:
- CUDA_VISIBLE_DEVICES=GPU-*************************
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
- /ADATAtmp/docker/ollama_model_2:/root/.ollama
networks:
- autogen_network
restart: always
open-webui2:
image: ghcr.io/open-webui/open-webui:main
container_name: open-webui_2
ports:
- "11438:8080"
volumes:
- /ADATAtmp/docker/open-webui-2:/app/backend/data
environment:
- OLLAMA_BASE_URL=http://ollama_model_2:11434
extra_hosts:
- "host.docker.internal:host-gateway"
networks:
- autogen_network
restart: always
networks:
autogen_network:
driver: bridge
```
Also I believe this part of docker logs ollama2 is relevant for reviewing:
```
$ docker logs ollama_model_2
time=2024-04-11T19:05:33.375Z level=INFO source=images.go:804 msg="total blobs: 10"
time=2024-04-11T19:05:33.375Z level=INFO source=images.go:811 msg="total unused blobs removed: 0"
time=2024-04-11T19:05:33.375Z level=INFO source=routes.go:1118 msg="Listening on [::]:11434 (version 0.1.31)"
time=2024-04-11T19:05:33.376Z level=INFO source=payload_common.go:113 msg="Extracting dynamic libraries to /tmp/ollama859516905/runners ..."
time=2024-04-11T19:05:35.857Z level=INFO source=payload_common.go:140 msg="Dynamic LLM libraries [rocm_v60000 cpu cpu_avx2 cpu_avx cuda_v11]"
time=2024-04-11T19:05:35.857Z level=INFO source=gpu.go:115 msg="Detecting GPU type"
time=2024-04-11T19:05:35.857Z level=INFO source=gpu.go:265 msg="Searching for GPU management library libcudart.so*"
time=2024-04-11T19:05:35.857Z level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/tmp/ollama859516905/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-11T19:05:35.879Z level=INFO source=gpu.go:340 msg="Unable to load cudart CUDA management library /tmp/ollama859516905/runners/cuda_v11/libcudart.so.11.0: cudart init failure: 100"
time=2024-04-11T19:05:35.879Z level=INFO source=gpu.go:265 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-11T19:05:35.879Z level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.535.154.05]"
time=2024-04-11T19:05:35.885Z level=INFO source=gpu.go:131 msg="Nvidia GPU detected via nvidia-ml"
time=2024-04-11T19:05:35.885Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-11T19:05:35.890Z level=INFO source=gpu.go:169 msg="[nvidia-ml] NVML CUDA Compute Capability detected: 8.9"
[GIN] 2024/04/11 - 19:08:54 | 200 | 630.936µs | 172.20.0.5 | GET "/api/tags"
[GIN] 2024/04/11 - 19:08:54 | 200 | 568.095µs | 172.20.0.5 | GET "/api/tags"
[GIN] 2024/04/11 - 19:08:54 | 200 | 572.393µs | 172.20.0.5 | GET "/api/tags"
[GIN] 2024/04/11 - 19:08:54 | 200 | 36.179µs | 172.20.0.5 | GET "/api/version"
[GIN] 2024/04/11 - 19:08:56 | 200 | 38.734µs | 172.20.0.5 | GET "/api/version"
time=2024-04-11T19:09:04.802Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-11T19:09:04.802Z level=INFO source=gpu.go:169 msg="[nvidia-ml] NVML CUDA Compute Capability detected: 8.9"
time=2024-04-11T19:09:04.802Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-11T19:09:04.802Z level=INFO source=gpu.go:169 msg="[nvidia-ml] NVML CUDA Compute Capability detected: 8.9"
time=2024-04-11T19:09:04.802Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-11T19:09:04.807Z level=INFO source=dyn_ext_server.go:87 msg="Loading Dynamic llm server: /tmp/ollama859516905/runners/cuda_v11/libext_server.so"
time=2024-04-11T19:09:04.807Z level=INFO source=dyn_ext_server.go:147 msg="Initializing llama server"
time=2024-04-11T19:09:04.827Z level=WARN source=llm.go:170 msg="Failed to load dynamic library /tmp/ollama859516905/runners/cuda_v11/libext_server.so Unable to init GPU: no CUDA-capable device is detected"
time=2024-04-11T19:09:04.828Z level=INFO source=dyn_ext_server.go:87 msg="Loading Dynamic llm server: /tmp/ollama859516905/runners/cpu_avx2/libext_server.so"
time=2024-04-11T19:09:04.828Z level=INFO source=dyn_ext_server.go:147 msg="Initializing llama server"
llama_model_loader: loaded meta data with 26 key-value pairs and 995 tensors from /root/.ollama/models/blobs/sha256-d68d6a65178011b746d215273d6a1f607f78be24a53532cf99618a32c2f382a2 (version GGUF V3 (latest))
# ...rest of the log
```
### What did you expect to see?
_No response_
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Linux
### Architecture
x86
### Platform
Docker
### Ollama version
0.1.31
### GPU
Nvidia
### GPU info
_No response_
### CPU
AMD
### Other software
_No response_
|
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PR_kwDOJ0Z1Ps56wsWl
| 6,696
|
Update README.md
|
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added crewai with mesop anew since there was a conflict previously
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I_kwDOJ0Z1Ps6RFPgK
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Prompt evaluation progress indicator
|
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Prompt evaluation can take huge time, especially with long context. it can literary be hours.
Right now Ollama just hangs in that phase. Is it possible to have some messages updating on the progress? It's tough when after an hour of 100% load you still don't know if it's at 15% or at 99%..
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I_kwDOJ0Z1Ps50HCBJ
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failed to verify certificate: x509: certificate signed by unknown authority
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~$ docker exec -it ollama ollama run llama2
pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": tls: failed to verify certificate: x509: certificate signed by unknown authority
please guide to solve this issue
|
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PR_kwDOJ0Z1Ps5_PgOY
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added formatting to ollama ls
|
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This groups output by model on disk, and does not pollute the filesize in the listing when the manifests point to the same model.
```
./ollama ls
NAME ID SIZE MODIFIED
qwen2.5:32b-instruct-q4_K_M 9f13ba1299af 19 GB 9 hours ago
bge-m3:latest 790764642607 1.2 GB 3 weeks ago
llama3.2:3b-instruct-q8_0 e410b836fe61 3.4 GB 3 weeks ago
reader-lm:1.5b-q8_0 39ec9eda0492 1.6 GB 4 weeks ago
└── reader-lm:1.5b-q8_0_defaults 05fe7cfa3bea -- 3 days ago
gemma2:27b-instruct-q6_K 73a0cbed4b12 22 GB 3 months ago
```
Caveats
- it seems the "basemodel" is not distinguished in the data except by modifiedBy -- which perhaps could be updated by the user without deleting or updating manifests they created (these descendent manifests are where you use "/set PARAMETER ..." and "/save ...").
- I had to explicitly turn off AutoWrapText because it was wrapping in the column at the space around the prefixes (like "└── "), and setting min column width just for this model does not effect this wrapping behavior.
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I_kwDOJ0Z1Ps56dsoq
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Ollama mobile app appeared on iOS app store
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NONE
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Accessible here: https://apps.apple.com/us/app/ollama/id6471840114
I'm assuming this is not legitimate?
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Dockerfile: use variables for package version
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Update Dockerfile to use variables instead of hardcoded values
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OpenAI: /v1/embeddings compatibility
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| 2024-06-25T22:47:36
| 2024-07-16T20:36:11
| 2024-07-16T20:36:09
|
CONTRIBUTOR
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In anticipation of #5127
Resolves #2416
```
curl http://localhost:11434/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"input": ["hello", "hi"],
"model": "all-minilm"
}'
```
```
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
...
],
"index": 0
},
{
"object": "embedding",
"embedding": [
...
],
"index": 1
}
],
"model": "all-minilm"
}
```
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I_kwDOJ0Z1Ps6iUzVi
| 7,969
|
Administrative / silent install is borked
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| 2024-12-06T15:50:06
| 2024-12-10T17:30:35
| null |
NONE
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### What is the issue?
For deployment scenarios like for classrooms or to regular managed devices the setup needs to perform an administrative / unattended install. For InnoSetup built installers this can normally be done via the command-line switches /SILENT (or /VERYSILENT) or via a response file via /LOADINF. (See: https://jrsoftware.org/ishelp/index.php?topic=setupcmdline )
If the setup can be performed by both, an unprivileged user and an administrator the switch "/ALLUSERS" helps to select the latter. If the install paths are (correctly) derived from the common environment variables this should automagically move the files to "C:\Program Files" or "C:\Program Files (x86)" instead of the users AppData folder.
Normally this would also lead the installer engine to register the application under the systems registry (HKLM:) and create a system wide start menu.
With Ollamas setup this does not work even though setting the install-path like mentioned in the documentation to 'C:\Program Files\Ollama' makes it look like it should.
Instead it creates a mix of both methods where the files are installed to the Program Files folder, but the registry and start menu entries are limited to the user (context) installing the software. And as most management system use a local system service as context this creates a rather weird state. And even if I could limit the permissions down - for a classroom for example I can never know which user will access the device...
### OS
Windows
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.4.7
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I_kwDOJ0Z1Ps6As1BV
| 2,811
|
Mistral Instruct models prompt does not use <s> or </s>
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| 2024-02-28T15:29:34
| 2024-07-18T22:45:02
| 2024-07-18T22:45:02
|
NONE
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from https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format
> \<s> [INST] Instruction [/INST] Model answer\</s> [INST] Follow-up instruction [/INST]
I didn't see `<s>` as part of the prompt when using `OLLAMA_DEBUG=1 ollama start`. Is that expected?
|
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| 2,503,463,117
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PR_kwDOJ0Z1Ps56S5Sr
| 6,611
|
Make stall duration timeout configurable
|
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| 2024-09-03T18:04:25
| 2024-09-05T21:00:08
| 2024-09-05T21:00:08
|
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With the new very large parameter models, some users are willing to wait for a very long time for models to load.
Fixes #6031
|
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PR_kwDOJ0Z1Ps5XsTL-
| 329
|
Add tutorials for using Langchain with ollama
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I_kwDOJ0Z1Ps6arRlh
| 7,241
|
add module/
|
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I_kwDOJ0Z1Ps5_wffo
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|
Windows preview - please let us set the location where models are stored
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NONE
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My drive C is a bit ancient right now. It's an old 250GB SSD and at any given time seems to have about 5-10gb free, so I'm forever doing cleanups to stop it running out of space. In contrast, I have about 2-3TB of free space on my other drives. I would _much_ prefer it if ollama would store models on one of those drives. How much effort would it be to make that happen?
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I_kwDOJ0Z1Ps6eP3A2
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Sending "hidden" messages for attachments
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| 2024-11-13T10:20:06
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NONE
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I'm building a frontend and trying to send attachment contents and an accompanying prompt (e.g. "analyze this and wait for user prompt before responding") as a "hidden" message so that it's not rendered to the user on the frontend. I'm trying to avoid seeing 10 pages of a doc rendered in the chat window.
Perhaps this is a frontend issue but I'm finding this more difficult to code than I think it should be...
Any ideas? Thanks!
|
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I_kwDOJ0Z1Ps6ldkwj
| 8,349
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can't use gpu after update
|
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### What is the issue?
Previously, it run on the GPU by default, but after the update, it run on the CPU. What can I do to change it
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.4
|
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PR_kwDOJ0Z1Ps5iI-Kk
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|
add lint and test on pull_request
|
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CONTRIBUTOR
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fixes a bug with generate where `get_flags` errors on ubuntu (and likely windows) when building cuda on a cuda-less system
fixes a bug in windows where `/api/list` does not return models correctly
both lint and test requires go generate results so do it once then propagate the artifacts to the rest of the pipeline
this enables linting with golangci-lint but doesn't go overboard with linters. it enables the default linters and only a few extra linters to catch the most egregious bugs
linting can run locally with [golangci-lint](https://golangci-lint.run/usage/install/)
```
go install github.com/golangci/golangci-lint/cmd/golangci-lint@v1.55.2
golangci-lint run -v
```
```
docker run --rm -v $(pwd):/app -w /app golangci/golangci-lint:v1.55.2 golangci-lint run -v
```
resolves #1539
|
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I_kwDOJ0Z1Ps6Sc2J_
| 6,272
|
Ollama Creat 手动部署 报错 Error: invalid file magic
|
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| 2024-08-09T03:47:19
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| 2024-08-25T20:15:11
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NONE
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### What is the issue?
按飞书文档找的modelfile 文件，ollama creat 后输出报错
Error: invalid file magic ，因此无法部署在ollama
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.34
|
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PR_kwDOJ0Z1Ps51HNIc
| 5,633
|
Update README.md >> Extension & Plugins :Terraform AWS Ollama & Open WebUI
|
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| 2024-11-21T10:28:58
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CONTRIBUTOR
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A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.
|
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I_kwDOJ0Z1Ps6GJvkC
| 3,733
|
I wrote an uninstallation script.
|
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| 2024-04-18T13:26:28
| 2024-04-19T01:59:32
| null |
NONE
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Hi, I wrote an uninstallation script and [this](https://github.com/TikaFlow/tika_grocery/tree/main/delOllama) is my script.
If you think it's not bad, you might consider referencing my script in your [documentation](https://github.com/ollama/ollama/blob/main/docs/linux.md#uninstall).
Thanks！
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| 2,293,631,935
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I_kwDOJ0Z1Ps6Itgu_
| 4,409
|
Windows security: ALERT: Trojan:Win32/Wacatac.H!ml detected
|
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| 2024-05-13T19:17:50
| 2024-05-13T20:03:24
| 2024-05-13T20:03:24
|
CONTRIBUTOR
|
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### What is the issue?
There is an alarm in Windows Defender which sets the following file under quarantine:
file: %USERPROFILE%\AppData\Local\Programs\Ollama\ollama app.exe
Windows Version: Windows 11 Pro, 23H2
You'll get the warning with update and also if you install it new.
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.37
|
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I_kwDOJ0Z1Ps6P1mMy
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process cant't kill on windows 10
|
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| 2024-07-17T10:04:20
| 2024-07-19T03:29:24
| 2024-07-19T03:29:24
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NONE
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### What is the issue?
I use child process in node to start ollama,and when I kill process,The process will be partially retained(`ollama.exe` is killed ollama_llama_server retained)
Ubuntu will correctly kill all processes
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
0.2.5
|
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I_kwDOJ0Z1Ps6eZpWe
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How can I remove specific PARAMETER from model?
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How can I remove specific PARAMETER from model
For example, delete PARAMETER num_gpu 30 from qwen2.5-coder:32b-instruct-q8_0
ollama show qwen2.5-coder:32b-instruct-q8_0
Model
architecture qwen2
parameters 32.8B
context length 32768
embedding length 5120
quantization Q8_0
Parameters
num_ctx 16000
num_gpu 30
System
You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
License
Apache License
Version 2.0, January 2004
|
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Failed to open the file /tmp/tmp.RbBP0lFvPD/ollama:
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### What is the issue?
I am unable to download and install ollama on ubuntu 22.04. I used the following command
```bash
sudo curl -fsSL https://ollama.com/install.sh | sh
```
And i get the following output
```
>>> Downloading ollama...
Warning: Failed to open the file /tmp/tmp.uSBa9PbYEo/ollama: No such file or %##O#-#
Warning: directory
0.0%curl: (23) Failure writing output to destination
```
Here is the output i get by just running `sudo curl -fsSL https://ollama.com/install.sh ` standalone
``` sh
#!/bin/sh
# This script installs Ollama on Linux.
# It detects the current operating system architecture and installs the appropriate version of Ollama.
set -eu
status() { echo ">>> $*" >&2; }
error() { echo "ERROR $*"; exit 1; }
warning() { echo "WARNING: $*"; }
TEMP_DIR=$(mktemp -d)
cleanup() { rm -rf $TEMP_DIR; }
trap cleanup EXIT
available() { command -v $1 >/dev/null; }
require() {
local MISSING=''
for TOOL in $*; do
if ! available $TOOL; then
MISSING="$MISSING $TOOL"
fi
done
echo $MISSING
}
[ "$(uname -s)" = "Linux" ] || error 'This script is intended to run on Linux only.'
ARCH=$(uname -m)
case "$ARCH" in
x86_64) ARCH="amd64" ;;
aarch64|arm64) ARCH="arm64" ;;
*) error "Unsupported architecture: $ARCH" ;;
esac
KERN=$(uname -r)
case "$KERN" in
*icrosoft*WSL2 | *icrosoft*wsl2) ;;
*icrosoft) error "Microsoft WSL1 is not currently supported. Please upgrade to WSL2 with 'wsl --set-version <distro> 2'" ;;
*) ;;
esac
VER_PARAM="${OLLAMA_VERSION:+?version=$OLLAMA_VERSION}"
SUDO=
if [ "$(id -u)" -ne 0 ]; then
# Running as root, no need for sudo
if ! available sudo; then
error "This script requires superuser permissions. Please re-run as root."
fi
SUDO="sudo"
fi
NEEDS=$(require curl awk grep sed tee xargs)
if [ -n "$NEEDS" ]; then
status "ERROR: The following tools are required but missing:"
for NEED in $NEEDS; do
echo " - $NEED"
done
exit 1
fi
status "Downloading ollama..."
curl --fail --show-error --location --progress-bar -o $TEMP_DIR/ollama "https://ollama.com/download/ollama-linux-${ARCH}${VER_PARAM}"
for BINDIR in /usr/local/bin /usr/bin /bin; do
echo $PATH | grep -q $BINDIR && break || continue
done
status "Installing ollama to $BINDIR..."
$SUDO install -o0 -g0 -m755 -d $BINDIR
$SUDO install -o0 -g0 -m755 $TEMP_DIR/ollama $BINDIR/ollama
install_success() {
status 'The Ollama API is now available at 127.0.0.1:11434.'
status 'Install complete. Run "ollama" from the command line.'
}
trap install_success EXIT
# Everything from this point onwards is optional.
configure_systemd() {
if ! id ollama >/dev/null 2>&1; then
status "Creating ollama user..."
$SUDO useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
fi
if getent group render >/dev/null 2>&1; then
status "Adding ollama user to render group..."
$SUDO usermod -a -G render ollama
fi
if getent group video >/dev/null 2>&1; then
status "Adding ollama user to video group..."
$SUDO usermod -a -G video ollama
fi
status "Adding current user to ollama group..."
$SUDO usermod -a -G ollama $(whoami)
status "Creating ollama systemd service..."
cat <<EOF | $SUDO tee /etc/systemd/system/ollama.service >/dev/null
[Unit]
Description=Ollama Service
After=network-online.target
[Service]
ExecStart=$BINDIR/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=$PATH"
[Install]
WantedBy=default.target
EOF
SYSTEMCTL_RUNNING="$(systemctl is-system-running || true)"
case $SYSTEMCTL_RUNNING in
running|degraded)
status "Enabling and starting ollama service..."
$SUDO systemctl daemon-reload
$SUDO systemctl enable ollama
start_service() { $SUDO systemctl restart ollama; }
trap start_service EXIT
;;
esac
}
if available systemctl; then
configure_systemd
fi
if ! available lspci && ! available lshw; then
warning "Unable to detect NVIDIA/AMD GPU. Install lspci or lshw to automatically detect and install GPU dependencies."
exit 0
fi
check_gpu() {
# Look for devices based on vendor ID for NVIDIA and AMD
case $1 in
lspci)
case $2 in
nvidia) available lspci && lspci -d '10de:' | grep -q 'NVIDIA' || return 1 ;;
amdgpu) available lspci && lspci -d '1002:' | grep -q 'AMD' || return 1 ;;
esac ;;
lshw)
case $2 in
nvidia) available lshw && $SUDO lshw -c display -numeric | grep -q 'vendor: .* \[10DE\]' || return 1 ;;
amdgpu) available lshw && $SUDO lshw -c display -numeric | grep -q 'vendor: .* \[1002\]' || return 1 ;;
esac ;;
nvidia-smi) available nvidia-smi || return 1 ;;
esac
}
if check_gpu nvidia-smi; then
status "NVIDIA GPU installed."
exit 0
fi
if ! check_gpu lspci nvidia && ! check_gpu lshw nvidia && ! check_gpu lspci amdgpu && ! check_gpu lshw amdgpu; then
install_success
warning "No NVIDIA/AMD GPU detected. Ollama will run in CPU-only mode."
exit 0
fi
if check_gpu lspci amdgpu || check_gpu lshw amdgpu; then
# Look for pre-existing ROCm v6 before downloading the dependencies
for search in "${HIP_PATH:-''}" "${ROCM_PATH:-''}" "/opt/rocm"; do
if [ -n "${search}" ] && [ -e "${search}/lib/libhipblas.so.2" ]; then
status "Compatible AMD GPU ROCm library detected at ${search}"
install_success
exit 0
fi
done
status "Downloading AMD GPU dependencies..."
$SUDO rm -rf /usr/share/ollama/lib
$SUDO chmod o+x /usr/share/ollama
$SUDO install -o ollama -g ollama -m 755 -d /usr/share/ollama/lib/rocm
curl --fail --show-error --location --progress-bar "https://ollama.com/download/ollama-linux-amd64-rocm.tgz${VER_PARAM}" \
| $SUDO tar zx --owner ollama --group ollama -C /usr/share/ollama/lib/rocm .
install_success
status "AMD GPU dependencies installed."
exit 0
fi
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-7-centos-7
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-8-rocky-8
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-9-rocky-9
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#fedora
install_cuda_driver_yum() {
status 'Installing NVIDIA repository...'
case $PACKAGE_MANAGER in
yum)
$SUDO $PACKAGE_MANAGER -y install yum-utils
$SUDO $PACKAGE_MANAGER-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
;;
dnf)
$SUDO $PACKAGE_MANAGER config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
;;
esac
case $1 in
rhel)
status 'Installing EPEL repository...'
# EPEL is required for third-party dependencies such as dkms and libvdpau
$SUDO $PACKAGE_MANAGER -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-$2.noarch.rpm || true
;;
esac
status 'Installing CUDA driver...'
if [ "$1" = 'centos' ] || [ "$1$2" = 'rhel7' ]; then
$SUDO $PACKAGE_MANAGER -y install nvidia-driver-latest-dkms
fi
$SUDO $PACKAGE_MANAGER -y install cuda-drivers
}
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu
# ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#debian
install_cuda_driver_apt() {
status 'Installing NVIDIA repository...'
curl -fsSL -o $TEMP_DIR/cuda-keyring.deb https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-keyring_1.1-1_all.deb
case $1 in
debian)
status 'Enabling contrib sources...'
$SUDO sed 's/main/contrib/' < /etc/apt/sources.list | $SUDO tee /etc/apt/sources.list.d/contrib.list > /dev/null
if [ -f "/etc/apt/sources.list.d/debian.sources" ]; then
$SUDO sed 's/main/contrib/' < /etc/apt/sources.list.d/debian.sources | $SUDO tee /etc/apt/sources.list.d/contrib.sources > /dev/null
fi
;;
esac
status 'Installing CUDA driver...'
$SUDO dpkg -i $TEMP_DIR/cuda-keyring.deb
$SUDO apt-get update
[ -n "$SUDO" ] && SUDO_E="$SUDO -E" || SUDO_E=
DEBIAN_FRONTEND=noninteractive $SUDO_E apt-get -y install cuda-drivers -q
}
if [ ! -f "/etc/os-release" ]; then
error "Unknown distribution. Skipping CUDA installation."
fi
. /etc/os-release
OS_NAME=$ID
OS_VERSION=$VERSION_ID
PACKAGE_MANAGER=
for PACKAGE_MANAGER in dnf yum apt-get; do
if available $PACKAGE_MANAGER; then
break
fi
done
if [ -z "$PACKAGE_MANAGER" ]; then
error "Unknown package manager. Skipping CUDA installation."
fi
if ! check_gpu nvidia-smi || [ -z "$(nvidia-smi | grep -o "CUDA Version: [0-9]*\.[0-9]*")" ]; then
case $OS_NAME in
centos|rhel) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -d '.' -f 1) ;;
rocky) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -c1) ;;
fedora) [ $OS_VERSION -lt '37' ] && install_cuda_driver_yum $OS_NAME $OS_VERSION || install_cuda_driver_yum $OS_NAME '37';;
amzn) install_cuda_driver_yum 'fedora' '37' ;;
debian) install_cuda_driver_apt $OS_NAME $OS_VERSION ;;
ubuntu) install_cuda_driver_apt $OS_NAME $(echo $OS_VERSION | sed 's/\.//') ;;
*) exit ;;
esac
fi
if ! lsmod | grep -q nvidia; then
KERNEL_RELEASE="$(uname -r)"
case $OS_NAME in
rocky) $SUDO $PACKAGE_MANAGER -y install kernel-devel kernel-headers ;;
centos|rhel|amzn) $SUDO $PACKAGE_MANAGER -y install kernel-devel-$KERNEL_RELEASE kernel-headers-$KERNEL_RELEASE ;;
fedora) $SUDO $PACKAGE_MANAGER -y install kernel-devel-$KERNEL_RELEASE ;;
debian|ubuntu) $SUDO apt-get -y install linux-headers-$KERNEL_RELEASE ;;
*) exit ;;
esac
NVIDIA_CUDA_VERSION=$($SUDO dkms status | awk -F: '/added/ { print $1 }')
if [ -n "$NVIDIA_CUDA_VERSION" ]; then
$SUDO dkms install $NVIDIA_CUDA_VERSION
fi
if lsmod | grep -q nouveau; then
status 'Reboot to complete NVIDIA CUDA driver install.'
exit 0
fi
$SUDO modprobe nvidia
fi
status "NVIDIA CUDA drivers installed."
```
### What did you expect to see?
I expected the ollama to be installed
### Steps to reproduce
Run `sudo curl -fsSL https://ollama.com/install.sh | sh`
### Are there any recent changes that introduced the issue?
I would not say so
### OS
Linux
### Architecture
amd64
### Platform
_No response_
### Ollama version
_No response_
### GPU
Nvidia, AMD
### GPU info
GTX 1080 ti
AMD R5 7600 integrated graphics
nvidia-smi
```
nvidia-smi
Sun Mar 24 23:09:06 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.239.06 Driver Version: 470.239.06 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| 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 ... Off | 00000000:01:00.0 On | N/A |
| 28% 32C P2 52W / 250W | 109MiB / 11178MiB | 16% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1189 G /usr/lib/xorg/Xorg 107MiB |
+-----------------------------------------------------------------------------+
```
### CPU
AMD R5 7600
### Other software
|
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I_kwDOJ0Z1Ps6kvueD
| 8,271
|
llama runner process terminated: CUDA error
|
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[] | null | 14
| 2024-12-31T05:34:54
| 2025-01-01T07:33:02
| 2025-01-01T07:33:02
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### What is the issue?
I'm making a little embedding example. The text is chunked fine, and embedded. But on retrieval, the I get this weird message.
Then if I go to the cli and try running the same model, I get the same message.
I'm pretty sure it has to do with having the embedded model in memory at the same time as llama3.2
**Also at the time only the embedded model was in memory, llama3.2 was supposed to be (and had just been used) but is no longer there, which I think is a big clue**
```
(venv-game) chris@FORGE:~/game$ ollama run llama3.2:latest
Error: llama runner process has terminated: CUDA error
(venv-game) chris@FORGE:~/game$ ollama ps
NAME ID SIZE PROCESSOR UNTIL
nomic-embed-text:latest 0a109f422b47 849 MB 100% GPU About a minute from now
(venv-game) chris@FORGE:~/game$ ollama run llama3.2:latest
Error: llama runner process has terminated: CUDA error
(venv-game) chris@FORGE:~/game$ nvidia-smi
Tue Dec 31 00:33:00 2024
+-----------------------------------------------------------------------------------------+
| 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 RTX 4090 On | 00000000:01:00.0 On | Off |
| 0% 42C P8 23W / 450W | 2269MiB / 24564MiB | 16% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 2454 G /usr/lib/xorg/Xorg 653MiB |
| 0 N/A N/A 3508 G cinnamon 71MiB |
| 0 N/A N/A 7665 G ...seed-version=20241225-174432.450000 341MiB |
| 0 N/A N/A 592670 G ...erProcess --variations-seed-version 76MiB |
| 0 N/A N/A 1334610 C ...rs/cuda_v12_avx/ollama_llama_server 930MiB |
| 0 N/A N/A 1663080 G ...yOnDemand --variations-seed-version 105MiB |
| 0 N/A N/A 3924325 G ...erProcess --variations-seed-version 61MiB |
+-----------------------------------------------------------------------------------------+
```
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama version is 0.5.4
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PR_kwDOJ0Z1Ps6DI6lo
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fix(docs): Replace hard coded num of make threads w/ CPU cores
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Replace the seemingly random number of make threads with the number CPU cores / 2.
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PR_kwDOJ0Z1Ps5twARO
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|
Improve mac parallel performance
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| 2024-04-25T16:40:14
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Carries #3900
Testing underway...
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I_kwDOJ0Z1Ps6HYSk2
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|
Windows installation is behaving like malware
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NONE
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### What is the issue?
This is beyond bad.
1. It installs stuff all over the place, without informing the user.
```
C:\Users\xxxx\AppData\Roaming\Microsoft\Windows\Start Menu\Programs
C:\Users\xxxx\AppData\Local\Temp\ollamaNNNNNN
C:\Users\xxxx\.ollama\id_ed25519
C:\Users\xxxx\AppData\Local\Programs\Ollama
C:\Users\xxxx\AppData\Local\Ollama
```
2. It doesn't ask the user where they want it to be installed!
3. It opens ports and starts a server under different application name and on unspecified sever ports that can only be found in a temp directory.
4. Once the original (cmd) windows is closed, the server is still running!
5. There are no instructions on how to shut down the server
In the current state this application is acting more like **malware** than something serious.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
_No response_
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PR_kwDOJ0Z1Ps5pJu-X
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|
Rename ROCm deps file to avoid confusion
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I_kwDOJ0Z1Ps6ZKBzd
| 7,111
|
Any plans to add nvidia/NVLM-D-72B new model to ollama?
|
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Nvidia just released a state of the art model that would be a good addition to ollama. Any plans to add it? [https://huggingface.co/nvidia/NVLM-D-72B/tree/main](https://huggingface.co/nvidia/NVLM-D-72B/tree/main)
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I_kwDOJ0Z1Ps6d3_qp
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llama3.2-vision on multi gpu error
|
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| 2024-11-11T09:26:43
| 2024-11-12T20:59:09
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NONE
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### What is the issue?
multi gpu
ollama run llama3.2-vision
>>> The image is a book cover. Output should be in this format - <Name of the Book>: <Name of the Author>. Do not output anything else /media/root/ssd2t/data/pro/tmp/o
... l/new/FastChat/image.png
Added image '/media/root/ssd2t/data/pro/tmp/ol/new/FastChat/image.png'
Error: POST predict: Post "http://127.0.0.1:41121/completion": EOF
one gpu
run ok
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.4.1
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I_kwDOJ0Z1Ps5zXOGc
| 748
|
Enabling langchain
|
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I found this video about implementing langchain with ollama. If available it should be easy to use it via ollama-ui
https://www.youtube.com/watch?v=CPgp8MhmGVY
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I_kwDOJ0Z1Ps6A334d
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OpenAI package compatibility
|
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| 2024-02-29T21:26:43
| 2024-05-02T01:23:23
| 2024-03-01T01:08:58
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NONE
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I read the example in https://ollama.com/blog/openai-compatibility
The example works, but it doesn't when I add "response_format={ "type": "json_object" },"
https://platform.openai.com/docs/guides/text-generation/json-mode
|
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PR_kwDOJ0Z1Ps5XOXrT
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Update README.md
|
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| 2023-08-04T19:43:08
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|
CONTRIBUTOR
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Ollama now supports Intel Macs
|
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I_kwDOJ0Z1Ps5-k25I
| 2,392
|
unable to initialize llm library Radeon card detected
|
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[] | null | 6
| 2024-02-07T18:04:01
| 2024-02-29T00:50:07
| 2024-02-07T22:47:19
|
NONE
|
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Hello,
I am trying to run as user and manually, i get this error:
time=2024-02-07T19:00:18.967+01:00 level=INFO source=payload_common.go:106 msg="Extracting dynamic libraries..."
Error: unable to initialize llm library Radeon card detected, but permissions not set up properly. Either run ollama as root, or add you user account to the render group.
I had a firepro w7100 but some days ago , i removed it and now i am using an nvidia 3060, I am on ubuntu 20 and i have no idea how to tell ollama that the gpu is nvidia.
|
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| 2,391,347,345
|
I_kwDOJ0Z1Ps6OiRCR
| 5,488
|
falcon2 fails to genarate code in almalinux9
|
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| 2024-07-04T18:55:46
| 2024-09-26T12:39:47
| 2024-09-26T12:39:47
|
CONTRIBUTOR
|
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### What is the issue?
I got a error that it could not able to genarate any code but it failed and just skipped and jumped 5lines of free space and ask for input again.
### OS
Linux
### GPU
_No response_
### CPU
Intel
### Ollama version
1.48
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I_kwDOJ0Z1Ps6DKkcn
| 3,287
|
Allow system message to be on its own
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| 2024-03-21T16:05:18
| 2024-09-04T04:42:37
| 2024-09-04T04:42:37
|
CONTRIBUTOR
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### What are you trying to do?
Current prompt rendering assumes there is only one system message. With the [Command-R model from Cohere AI](https://huggingface.co/CohereForAI/c4ai-command-r-v01), there can be multiple system message: https://docs.cohere.com/docs/prompting-command-r#structured-prompt-templates. There is no way to get around this.
Consider the following messages:
```go
{Role: "system", Content: "Preambles"},
{Role: "user", Content: "i'm user"},
{Role: "system", Content: "Tool output"},
{Role: "system", Content: "---Instructions---"},
```
And the template:
```
<BOS_TOKEN>
{{ if .System }}<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ .System }}<|END_OF_TURN_TOKEN|>{{ end }}
{{ if .Prompt }}<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ .Prompt }}<|END_OF_TURN_TOKEN|>{{ end }}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{{ .Response }}
```
We get
```
<BOS_TOKEN>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Preambles<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>i'm user<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><BOS_TOKEN>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Tool output<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><BOS_TOKEN>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>---Instructions---<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
```
However, the desired output is:
```
<BOS_TOKEN>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Preambles<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>i'm user<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><BOS_TOKEN>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Tool output<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>---Instructions---<|END_OF_TURN_TOKEN|>
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
```
### How should we solve this?
In order to make this backward compatible, I suggest we introduce a new version of the template that formats each message as is. It'll be much easier to control the desired output
The code to render the prompt should detect is the new template is present and use it instead. Preferably, gradually deprecate old template.
### What is the impact of not solving this?
There doesn't seems to be a backward compatible way to fix this. Introducing a new template add complexity, especially for new users. But there doesn't seems to be a better way.
### Anything else?
_No response_
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I_kwDOJ0Z1Ps6GEBsd
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|
CUDA malloc fails on newly supported models in 0.1.32 (dual-GPU setup with 72GB VRAM and 128GB RAM)
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| 2024-04-17T20:18:35
| 2024-06-22T00:10:31
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NONE
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### What is the issue?
I am getting cuda malloc errors with v0.1.32 (as well as with the current head of main branch) when trying any of the new big models: wizardlm2, mixtral:8x22b, dbrx (command-r+ does work) with my dual GPU setup (A6000 + RTX 3090, i.e. combined 72GB VRAM) with a 24-core 13th gen Intel CPU (128GB of DDR5 on the system).
The symptoms are similar, with llama-cpp dying
```
ollama run mixtral:8x22b
Error: llama runner process no longer running: 1 error:failed to create context with model '/media/data/ollama/blobs/sha256-b5fc1eb35edf792b07d6163cf7ac162fdd9f9024903e6b33a3a870f2f973b8ca'
```
From the service logs I see:
```
Apr 17 23:09:42 athena ollama.listener[2840147]: time=2024-04-17T23:09:42.621+03:00 level=INFO source=server.go:136 msg="offload to gpu" layers.real=39 layers.estimate=39 memory.available="70186.6 MiB" memory.required.full="72169.5 MiB" memory.required.partial="69599.9 MiB" memory.required.kv="320.0 MiB" memory.weights.total="70752.5 MiB" memory.weights.repeating="69939.4 MiB" memory.weights.nonrepeating="813.1 MiB" memory.graph.full="640.0 MiB" memory.graph.partial="640.0 MiB"
Apr 17 23:09:42 athena ollama.listener[2840147]: time=2024-04-17T23:09:42.621+03:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
Apr 17 23:09:42 athena ollama.listener[2840147]: time=2024-04-17T23:09:42.622+03:00 level=INFO source=server.go:302 msg="starting llama server" cmd="/tmp/ollama3763278260/runners/cuda_v12/ollama_llama_server --model /media/data/ollama/blobs/sha256-1d12441f19436dbb0bcc4067e9d47921b944ef4a87b35873aa430e85e91a93c8 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 39 --port 38939"
Apr 17 23:09:42 athena ollama.listener[2840147]: time=2024-04-17T23:09:42.622+03:00 level=INFO source=server.go:427 msg="waiting for llama runner to start responding"
Apr 17 23:09:42 athena ollama.listener[2841851]: {"function":"server_params_parse","level":"INFO","line":2603,"msg":"logging to file is disabled.","tid":"123385345642496","timestamp":1713384582}
Apr 17 23:09:42 athena ollama.listener[2841851]: {"build":1,"commit":"7593639","function":"main","level":"INFO","line":2819,"msg":"build info","tid":"123385345642496","timestamp":1713384582}
Apr 17 23:09:42 athena ollama.listener[2841851]: {"function":"main","level":"INFO","line":2822,"msg":"system info","n_threads":16,"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 | ","tid":"123385345642496","timestamp":1713384582,"total_threads":32}
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: loaded meta data with 24 key-value pairs and 323 tensors from /media/data/ollama/blobs/sha256-1d12441f19436dbb0bcc4067e9d47921b944ef4a87b35873aa430e85e91a93c8 (version GGUF V3 (latest))
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 0: general.architecture str = dbrx
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 1: general.name str = dbrx
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 2: dbrx.block_count u32 = 40
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 3: dbrx.context_length u32 = 32768
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 4: dbrx.embedding_length u32 = 6144
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 5: dbrx.feed_forward_length u32 = 10752
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 6: dbrx.attention.head_count u32 = 48
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 7: dbrx.attention.head_count_kv u32 = 8
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 8: dbrx.rope.freq_base f32 = 500000.000000
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 9: dbrx.attention.clamp_kqv f32 = 8.000000
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 10: general.file_type u32 = 2
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 11: dbrx.expert_count u32 = 16
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 12: dbrx.expert_used_count u32 = 4
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 13: dbrx.attention.layer_norm_epsilon f32 = 0.000010
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 14: tokenizer.ggml.model str = gpt2
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,100352] = ["!", "\"", "#", "$", "%", "&", "'", ...
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,100352] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,100000] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 100257
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 100257
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 100257
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 100277
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 22: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'...
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - kv 23: general.quantization_version u32 = 2
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - type f32: 81 tensors
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - type f16: 40 tensors
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - type q4_0: 201 tensors
Apr 17 23:09:42 athena ollama.listener[2840147]: llama_model_loader: - type q6_K: 1 tensors
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_vocab: special tokens definition check successful ( 96/100352 ).
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: format = GGUF V3 (latest)
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: arch = dbrx
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: vocab type = BPE
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_vocab = 100352
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_merges = 100000
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_ctx_train = 32768
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_embd = 6144
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_head = 48
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_head_kv = 8
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_layer = 40
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_rot = 128
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_embd_head_k = 128
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_embd_head_v = 128
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_gqa = 6
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_embd_k_gqa = 1024
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_embd_v_gqa = 1024
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: f_norm_eps = 1.0e-05
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: f_norm_rms_eps = 0.0e+00
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: f_clamp_kqv = 8.0e+00
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: f_logit_scale = 0.0e+00
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_ff = 10752
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_expert = 16
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_expert_used = 4
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: causal attn = 1
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: pooling type = 0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: rope type = 2
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: rope scaling = linear
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: freq_base_train = 500000.0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: freq_scale_train = 1
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: n_yarn_orig_ctx = 32768
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: rope_finetuned = unknown
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: ssm_d_conv = 0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: ssm_d_inner = 0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: ssm_d_state = 0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: ssm_dt_rank = 0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: model type = 16x12B
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: model ftype = Q4_0
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: model params = 131.60 B
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: model size = 69.09 GiB (4.51 BPW)
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: general.name = dbrx
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: BOS token = 100257 '<|endoftext|>'
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: EOS token = 100257 '<|endoftext|>'
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: UNK token = 100257 '<|endoftext|>'
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: PAD token = 100277 '<|pad|>'
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_print_meta: LF token = 128 'Ä'
Apr 17 23:09:42 athena ollama.listener[2840147]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes
Apr 17 23:09:42 athena ollama.listener[2840147]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
Apr 17 23:09:42 athena ollama.listener[2840147]: ggml_cuda_init: found 2 CUDA devices:
Apr 17 23:09:42 athena ollama.listener[2840147]: Device 0: NVIDIA RTX A6000, compute capability 8.6, VMM: yes
Apr 17 23:09:42 athena ollama.listener[2840147]: Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Apr 17 23:09:42 athena ollama.listener[2840147]: llm_load_tensors: ggml ctx size = 1.10 MiB
Apr 17 23:09:46 athena ollama.listener[2840147]: llm_load_tensors: offloading 39 repeating layers to GPU
Apr 17 23:09:46 athena ollama.listener[2840147]: llm_load_tensors: offloaded 39/41 layers to GPU
Apr 17 23:09:46 athena ollama.listener[2840147]: llm_load_tensors: CPU buffer size = 70752.49 MiB
Apr 17 23:09:46 athena ollama.listener[2840147]: llm_load_tensors: CUDA0 buffer size = 45460.59 MiB
Apr 17 23:09:46 athena ollama.listener[2840147]: llm_load_tensors: CUDA1 buffer size = 22730.30 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: ....................................................................................................
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: n_ctx = 2048
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: n_batch = 512
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: n_ubatch = 512
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: freq_base = 500000.0
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: freq_scale = 1
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_kv_cache_init: CUDA_Host KV buffer size = 8.00 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_kv_cache_init: CUDA0 KV buffer size = 208.00 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_kv_cache_init: CUDA1 KV buffer size = 104.00 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: KV self size = 320.00 MiB, K (f16): 160.00 MiB, V (f16): 160.00 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: CUDA_Host output buffer size = 0.41 MiB
Apr 17 23:09:53 athena ollama.listener[2840147]: ggml_backend_cuda_buffer_type_alloc_buffer: allocating 1794.00 MiB on device 0: cudaMalloc failed: out of memory
Apr 17 23:09:53 athena ollama.listener[2840147]: ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 1881147392
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_new_context_with_model: failed to allocate compute buffers
Apr 17 23:09:53 athena ollama.listener[2840147]: llama_init_from_gpt_params: error: failed to create context with model '/media/data/ollama/blobs/sha256-1d12441f19436dbb0bcc4067e9d47921b944ef4a87b35873aa430e85e91a93c8'
Apr 17 23:09:54 athena ollama.listener[2841851]: {"function":"load_model","level":"ERR","line":410,"model":"/media/data/ollama/blobs/sha256-1d12441f19436dbb0bcc4067e9d47921b944ef4a87b35873aa430e85e91a93c8","msg":"unable to load model","tid":"123385345642496","timestamp":1713384594}
```
When watching nvidia-smi (`watch nvidia-smi`) I see that the GPU 0 (the A6000) gets its memory nearly fully allocated, before the malloc failure begins.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.32 (or current head of main branch)
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I_kwDOJ0Z1Ps6GYbJI
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How to check memory utilization rate
|
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| 2024-04-20T13:19:09
| 2024-05-02T10:55:11
| 2024-05-02T10:55:11
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NONE
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after loading a model,How to check v-memory utilization rate
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UTF-8 characters
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| 2023-08-27T12:56:34
| 2023-08-30T21:11:50
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|
NONE
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How to render properly such characters , is this per-model issue ... or font related ?
|
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I_kwDOJ0Z1Ps551kcn
| 1,546
|
Is running behind a proxy fully supported?
|
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| 2023-12-15T17:16:41
| 2023-12-28T12:17:29
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|
NONE
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Hi,
when I run with a configured proxy, I get the error `lookup registry.ollama.ai: no such host`:
```
> HTTPS_PROXY=http://REDACTED.XXX:10000 ollama run llama2
pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": dial tcp: lookup registry.ollama.ai: no such host
```
I then added a manual dns entry to /etc/hosts and ran the command again, but this time after trying to fetch the manifest, I get
```
> HTTPS_PROXY=http://REDACTED.XXX:10000 ollama run llama2
pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": dial tcp 34.120.132.20:443: connect: network is unreachable
```
Going to those urls in Safari works just fine so it seems that the proxy support isn't fully working.
|
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PR_kwDOJ0Z1Ps5fmfF0
| 1,151
|
install: fix enable contrib on debian 12
|
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| 2023-11-16T08:23:44
| 2023-11-16T20:53:07
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|
CONTRIBUTOR
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On debian 12, sources definitions have moved from
/etc/apt/sources.list to /etc/apt/sources.list.d/debian.sources
|
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I_kwDOJ0Z1Ps6djPuF
| 7,571
|
Please add Qwen-VL Qwen2.5-VL Qwen2-VL multimodal large model
|
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| 2024-11-12T00:40:52
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NONE
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I_kwDOJ0Z1Ps6Iit4T
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|
Unable to determine if Ollama can be run on GPU after running Linux commands
|
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| 2024-05-11T09:18:56
| 2024-05-21T23:21:51
| 2024-05-21T23:21:48
|
NONE
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Hello, I have a question. If I use the command curl - fsSL https://ollama.com/install.sh |Can Ollama run on GPU after installing the Nvidia driver and cuda toolkit in SH?
|
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I_kwDOJ0Z1Ps6NhdCp
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|
请上架InternVL
|
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| null | null | null | ERROR: type should be string, got "\r\nhttps://github.com/OpenGVLab/InternVL\r\n\r\nthanks 谢谢\r\n\r\n\r\n\r\nollama0.1.43\r\nerror format not yet support!\r\n错误格式尚不支持！\r\n错误格式尚不支持！\r\n搞了多次 均报错"
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PR_kwDOJ0Z1Ps5YHWrd
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fix nous-hermes model file size listing in readme
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fix nous-hermes model file size listing in readme
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I_kwDOJ0Z1Ps6laaTJ
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llama3.2-vision does not support tools
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### What is the issue?
I get above error when I use `llama3.2-vision:11b` with tool calling.
I think `llama3.2-vision:11b` should support tool calls: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/vision_prompt_format.md#builtin-and-zero-shot-tool-calling
- [llama3.2:3b template](https://ollama.com/library/llama3.2/blobs/966de95ca8a6)
- [llama3.2-vision:11b template](https://ollama.com/library/llama3.2-vision/blobs/715415638c9c)
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.0
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PR_kwDOJ0Z1Ps5hd4Rw
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fix redundant newline
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| null | null | null |
### What is the issue?
It finds:
* [CVE-2024-8063](https://ossindex.sonatype.org/vulnerability/CVE-2024-8063?component-type=golang&component-name=github.com%2Follama%2Follama&utm_source=nancy-client&utm_medium=integration&utm_content=1.0.46) CWE-369: Divide By Zero
* [CVE-2024-39719](https://ossindex.sonatype.org/vulnerability/CVE-2024-39719?component-type=golang&component-name=github.com%2Follama%2Follama&utm_source=nancy-client&utm_medium=integration&utm_content=1.0.46) CWE-209: Information Exposure Through an Error Message
The latest stable Ollama version does not fix them, so I am opening an issue to track that.
### OS
Linux
### GPU
Intel
### CPU
Intel
### Ollama version
0.5.1
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PR_kwDOJ0Z1Ps5mqQ3_
| 2,467
|
Check image filetype in api handlers
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Fixes: https://github.com/ollama/ollama/issues/2456
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PR_kwDOJ0Z1Ps5fF3bO
| 1,067
|
add custom ollama-runner
|
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| 2023-12-06T23:54:38
| 2023-11-21T20:14:50
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CONTRIBUTOR
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- update llama.cpp examples with custom ollama-runner
- update llama.cpp gguf version to latest
This change adds a custom inference server to llama.cpp based on the server we use in the current version, but with excess features removed. This allows us to have a more stable interface to build on when llama.cpp updates.
To review this please pull down the changes run `go generate ./...` and review the contents of the `llm/llama.cpp/gguf/examples/ollama-runner`
This change may be superseded by packaging in llama.cpp directly in the near future.
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PR_kwDOJ0Z1Ps5hdZct
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|
os specific ctrl-z
|
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| 2023-12-07T20:03:08
| 2023-12-14T06:46:26
| 2023-12-11T15:48:15
|
CONTRIBUTOR
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Add OS specific readline functions. Windows does not support these suspend system calls, so make ctrl-z a no-op on windows. This fixes development windows native builds.
resolves #1414
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I_kwDOJ0Z1Ps6gQH29
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Tool calling parsing for llama3.2
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MEMBER
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### What is the issue?
Llama 3.2 tool call outputs [are not in JSON](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/text_prompt_format.md) and so Ollama's tool parsing needs to be updated
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
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|
Question on model updating
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NONE
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I know I could download the latest model when a model is not installed. Say:
```swift
ollama run llama2
```
After some days, say one month, the model will be updated. Can ollama updates to the latest model automatically? I didn't find a way to do that.
Please help.
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PR_kwDOJ0Z1Ps5lRYk5
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Update import instructions to use convert and quantize tooling from llama.cpp submodule
|
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PR_kwDOJ0Z1Ps52nmhq
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server: fix race conditions during download
|
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This fixes various data races scattered throughout the download/pull client where the client was accessing the download state concurrently.
This commit is mostly a hot-fix and will be replaced by a new client one day soon.
Also, remove the unnecessary opts argument from downloadChunk.
|
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I_kwDOJ0Z1Ps6GmX1C
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|
Feature Request: Detect Truncation Due to Exceeding Context Size
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Understanding whether model output has been truncated due to exceeding context size is crucial for trusting the model to provide complete and accurate information. Here are some specific examples that illustrate why it's necessary to know if the output has been truncated:
1. **Question-Answering Systems**: When building a question-answering system, if the answer to a user's query is truncated due to exceeding context size, the user may receive incomplete or inaccurate answers, which can degrade user experience and system reliability.
2. **Text Summarization**: When using a model to generate summaries of articles or reports, if the summary is truncated due to exceeding context size, it may omit key information, leading to incomplete understanding of the original content.
3. **Chatbots**: In chatbot applications, if a response in a conversation is truncated due to exceeding context size, it may cause the dialogue flow to be interrupted, affecting the user's interaction experience with the bot.
4. **Content Generation**: When using a model to generate articles, stories, or other creative content, if the generated text is truncated due to exceeding context size, it may disrupt the coherence and completeness of the content.
5. **Machine Translation**: In machine translation scenarios, if the translated text is truncated due to exceeding context size, it may result in the loss of the latter part of the translation, affecting translation accuracy.
6. **Natural Language Processing Tasks**: When dealing with long texts in any natural language processing task, such as sentiment analysis or topic classification, if the model's output is truncated due to exceeding context size, it may lead to incorrect processing results.
7. **Legal and Compliance**: In applications with high legal or compliance requirements, the completeness and accuracy of information are crucial. If model output is truncated due to exceeding context size, it may violate these requirements and lead to serious consequences.
8. **Academic and Research**: When using large language models in academic research, ensuring the completeness and accuracy of results is vital for validating research hypotheses and conclusions. If model output is truncated, it may negatively impact the effectiveness and reliability of the research.
Please consider implementing a mechanism within the API that clearly indicates whether the output has been truncated. This could be a boolean flag in the response payload or an error message that provides insight into the truncation.
Thank you for your attention to this matter. I believe this feature would greatly enhance the usability and trustworthiness of ollama for all users.
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I_kwDOJ0Z1Ps6RJAMe
| 6,044
|
Illegal istruction in ollama_llama_server runner
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### What is the issue?
I tried to run llama3 model with ollama.
Reproduction (with my CPU: `AMD Ryzen 7 7735HS`)
1. Start server: `ollama serve`
2. Try to run llama3: `ollama run llama3
(The same error occurs with llama3.1)
The server will execute a new runner with the following arguments:
```/tmp/ollama996131774/runners/cpu/ollama_llama_server --model /home/snowy/.ollama/models/blobs/ sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 42857```
And the model will fail with the following errors:
```
llama runner process has terminated: signal: illegal instruction (core dumped)
```
When opening the runner with GDB, the program crashes when trying to run the following instruction: `vmovupd %zmm0,0x13(%rax)` (ggml_init+775) that requires AVX512F feature flag.
Complete log:
```
❯ ollama serve
2024/07/29 12:09:49 routes.go:1099: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/home/snowy/.ollama/models 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://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-07-29T12:09:49.854+02:00 level=INFO source=images.go:784 msg="total blobs: 10"
time=2024-07-29T12:09:49.855+02:00 level=INFO source=images.go:791 msg="total unused blobs removed: 0"
time=2024-07-29T12:09:49.855+02:00 level=INFO source=routes.go:1146 msg="Listening on 127.0.0.1:11434 (version 0.3.0)"
time=2024-07-29T12:09:49.855+02:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama996131774/runners
time=2024-07-29T12:09:57.575+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu rocm]"
time=2024-07-29T12:09:57.575+02:00 level=INFO source=gpu.go:205 msg="looking for compatible GPUs"
time=2024-07-29T12:09:57.657+02:00 level=WARN source=amd_linux.go:58 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-07-29T12:09:57.662+02:00 level=WARN source=amd_linux.go:325 msg="amdgpu is not supported" gpu=0 gpu_type=gfx1035 library=/opt/rocm/lib supported_types="[gfx1030 gfx1100 gfx1101 gfx1102 gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942]"
time=2024-07-29T12:09:57.662+02:00 level=WARN source=amd_linux.go:327 msg="See https://github.com/ollama/ollama/blob/main/docs/gpu.md#overrides for HSA_OVERRIDE_GFX_VERSION usage"
time=2024-07-29T12:09:57.662+02:00 level=INFO source=amd_linux.go:345 msg="no compatible amdgpu devices detected"
time=2024-07-29T12:09:57.662+02:00 level=INFO source=gpu.go:346 msg="no compatible GPUs were discovered"
time=2024-07-29T12:09:57.662+02:00 level=INFO source=types.go:105 msg="inference compute" id=0 library=cpu compute="" driver=0.0 name="" total="13.3 GiB" available="9.1 GiB"
[GIN] 2024/07/29 - 12:09:57 | 200 | 66.764µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/07/29 - 12:09:57 | 200 | 41.795654ms | 127.0.0.1 | POST "/api/show"
time=2024-07-29T12:09:57.790+02:00 level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=33 layers.offload=0 layers.split="" memory.available="[9.1 GiB]" memory.required.full="5.8 GiB" memory.required.partial="0 B" memory.required.kv="1.0 GiB" memory.required.allocations="[5.8 GiB]" memory.weights.total="4.7 GiB" memory.weights.repeating="4.3 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-07-29T12:09:57.792+02:00 level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama996131774/runners/cpu/ollama_llama_server --model /home/snowy/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 42857"
time=2024-07-29T12:09:57.793+02:00 level=INFO source=sched.go:437 msg="loaded runners" count=1
time=2024-07-29T12:09:57.793+02:00 level=INFO source=server.go:583 msg="waiting for llama runner to start responding"
time=2024-07-29T12:09:57.794+02:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server error"
time=2024-07-29T12:09:58.044+02:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: signal: illegal instruction (core dumped)"
[GIN] 2024/07/29 - 12:09:58 | 500 | 338.214098ms | 127.0.0.1 | POST "/api/chat"
[GIN] 2024/07/29 - 12:18:18 | 200 | 63.63µs | 127.0.0.1 | GET "/api/version"
```
[coredump.zip](https://github.com/user-attachments/files/16411350/coredump.zip)
[cpuinfo.txt](https://github.com/user-attachments/files/16411356/cpuinfo.txt)
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.3.0
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PR_kwDOJ0Z1Ps5mGd7d
| 2,368
|
Replace `reflect` usage in option parsing
|
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I_kwDOJ0Z1Ps6Xqae7
| 6,929
|
support qwen2.5:72b-instruct function call
|
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| 2024-09-24T06:36:16
| 2024-11-06T00:18:09
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qwen2.5:72b-instruct now support function call
Returned its own unique data format, but currently ollama 0.3.11 does not correctly return formatted data for function calls
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I_kwDOJ0Z1Ps6J19Dz
| 4,588
|
Tool calling feature support
|
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| 2024-05-23T11:06:39
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since there are more and more models that support function/tool calling. I think it would be a great feature for that support. It can be hard because the models could need Special tokens. My thought is that you can be able to set these spetial tokens or customizations in the Modelfile, something like:
```text
PARAMETER tool_calls_start ""
PARAMETER tool_calls_end ""
PARAMETER tool_results_start ""
PARAMETER tool_results_end ""
...
```
or (my preferred one):
```text
TOOL_CALLS """"[TOOL_CALLS] {response}"""
AVAILABLE_TOOLS """[AVAILABLE_TOOLS] {available tools list} [/AVAILABLE_TOOLS]"""
TOOL_RESULTS """[TOOL_RESULTS] {tool response} [/TOOL_RESULTS]""""
```
The API could be the same with OpenAI's or something different.
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I_kwDOJ0Z1Ps5-wa_j
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Always getting a timeout error while querying using mistral using Ollama
|
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NONE
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```
Traceback (most recent call last):
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_exceptions.py", line 10, in map_exceptions
yield
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_backends/sync.py", line 126, in read
return self._sock.recv(max_bytes)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TimeoutError: timed out
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_transports/default.py", line 67, in map_httpcore_exceptions
yield
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_transports/default.py", line 231, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/connection_pool.py", line 268, in handle_request
raise exc
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/connection_pool.py", line 251, in handle_request
response = connection.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/connection.py", line 103, in handle_request
return self._connection.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/http11.py", line 133, in handle_request
raise exc
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/http11.py", line 111, in handle_request
) = self._receive_response_headers(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/http11.py", line 176, in _receive_response_headers
event = self._receive_event(timeout=timeout)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_sync/http11.py", line 212, in _receive_event
data = self._network_stream.read(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_backends/sync.py", line 124, in read
with map_exceptions(exc_map):
File "/opt/homebrew/Cellar/python@3.11/3.11.7_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/contextlib.py", line 158, in __exit__
self.gen.throw(typ, value, traceback)
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpcore/_exceptions.py", line 14, in map_exceptions
raise to_exc(exc) from exc
httpcore.ReadTimeout: timed out
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/chakitrocks/Desktop/llm/index.py", line 57, in <module>
response = query_engine.query("What does the author think about Star Trek? Give details.")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/core/base_query_engine.py", line 40, in query
return self._query(str_or_query_bundle)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/query_engine/retriever_query_engine.py", line 172, in _query
response = self._response_synthesizer.synthesize(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/response_synthesizers/base.py", line 168, in synthesize
response_str = self.get_response(
^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/response_synthesizers/compact_and_refine.py", line 38, in get_response
return super().get_response(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/response_synthesizers/refine.py", line 146, in get_response
response = self._give_response_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/response_synthesizers/refine.py", line 202, in _give_response_single
program(
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/response_synthesizers/refine.py", line 64, in __call__
answer = self._llm.predict(
^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/llms/llm.py", line 239, in predict
chat_response = self.chat(messages)
^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/llms/base.py", line 100, in wrapped_llm_chat
f_return_val = f(_self, messages, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/llama_index/llms/ollama.py", line 102, in chat
response = client.post(
^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 1146, in post
return self.request(
^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 828, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 915, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 943, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 980, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_client.py", line 1016, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_transports/default.py", line 230, in handle_request
with map_httpcore_exceptions():
File "/opt/homebrew/Cellar/python@3.11/3.11.7_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/contextlib.py", line 158, in __exit__
self.gen.throw(typ, value, traceback)
File "/Users/chakitrocks/Desktop/llm/env/lib/python3.11/site-packages/httpx/_transports/default.py", line 84, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ReadTimeout: timed out
```
I seem to get this error while I am trying to fetch the top 20 values using VectorStoreIndex.
Here's the link to the blog I was trying to implement: https://blog.llamaindex.ai/running-mixtral-8x7-locally-with-llamaindex-e6cebeabe0ab
I am getting a timeout while I am querying with `similarity_top_k=20`.
What is the workaround this?
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I_kwDOJ0Z1Ps52CpLz
| 1,025
|
How to stop a model aftering it's served?
|
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NONE
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When the modelling is already being served, what is the command to stop it so that I can serve it again with different variables? I want to run `OLLAMA_ORIGINS=https://XXXXX ollama serve` but I'm getting an error that the address is already in use.
Running `ollama --help` doesn't show a command to unserve...?
tldr; how can I unserve?
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| 2,306,532,844
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PR_kwDOJ0Z1Ps5v_RLM
| 4,544
|
update langchain-python-rag-document example to use latest langchain apis
|
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NONE
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|
Hello!
Langchain library went through a lot changes since this helpful example was first added and users will find that this will not work out of box anymore.
I have updated this to work with all the latest changes and also removed any deprecated functions.
Hope others find this useful!
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PR_kwDOJ0Z1Ps5pzx1y
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llama: remove server static assets
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I_kwDOJ0Z1Ps55MAnp
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|
Serious bug
|
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NONE
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(base) mcfrank@interface:~$ orca show
The following are not valid: show
Warning: Could not load keyboard geometry for :0
BadName (named color or font does not exist)
Resulting keymap file will not describe geometry
^C(base) mcfrank@interface:~$
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PR_kwDOJ0Z1Ps6ATg6p
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add more tests for getting the optimal tiled canvas
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CONTRIBUTOR
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I_kwDOJ0Z1Ps5thpuJ
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add `ollama serve` to the quickstart doc
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i downloaded using `brew` and attempted to follow the quick start doc
it is missing `ollama serve`
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I_kwDOJ0Z1Ps6Qk9s0
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Is llama 3.1 already supported (on 2.8) or should we wait another update ?
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### What is the issue?
The model page seems to already exists in ollama website, but the model is clearly behaving erratically, which makes me wonder if we should wait for an update before using llama 3.1.
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
2.8
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PR_kwDOJ0Z1Ps5hkiJ6
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fix: parallel queueing race condition caused silent failure
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CONTRIBUTOR
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As of the most recent llama.cpp update concurrent requests had a race condition that would result in an empty response.
This was not easy to observe since the response from the llm runner subprocess was a 200 with the error {"content":"slot unavailable"} in the response stream, which just silently closed the channel.
This change resolves this by allowing multiple slots in the llm runner subprocess. We manage the queueing ourselves so this should be ok. @dhiltgen this may be a case we need to account for in the cgo changes.
|
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I_kwDOJ0Z1Ps6El5Db
| 3,483
|
Ollama hangs on CUDA devices when running multi-modal models
|
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| 2024-04-04T05:24:22
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MEMBER
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### What is the issue?
```
Apr 04 05:15:04 gpu.us-central1-a.c.ollama.internal ollama[5042]: {"function":"launch_slot_with_data","level":"INFO","line":804,"msg":"slot is processing task","slot_id":0,"task_id":29930,"tid":"140079034640064","timestamp":1712207704}
Apr 04 05:15:04 gpu.us-central1-a.c.ollama.internal ollama[5042]: {"function":"update_slots","level":"INFO","line":1808,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":29930,"tid":"140079034640064","timestamp":1712207704}
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 256
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 128
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 64
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 32
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 16
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 8
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 4
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 2
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 1
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to decode the batch, n_batch = 1, ret = 1
Apr 04 05:15:44 gpu.us-central1-a.c.ollama.internal ollama[5042]: [1712207744] update_slots : failed to find free space in the KV cache, retrying with smaller n_batch = 256
```
### What did you expect to see?
_No response_
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
_No response_
### 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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|
partial decode ggml bin for more info
|
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I_kwDOJ0Z1Ps5qQgNl
| 27
|
show a loading bar for model loading
|
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| 2023-06-30T15:17:59
| 2023-09-07T11:11:10
| 2023-09-07T11:11:10
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MEMBER
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currently we show a spinner – even better would be to show a loader similar to the download one
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I_kwDOJ0Z1Ps6KuvSv
| 4,749
|
OLLAMA_MODELS not applied on initial start or on restart after upgrade on macOS
|
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| 2024-05-31T10:32:39
| 2024-07-26T03:15:28
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NONE
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The addition of OLLAMA_MODELS is much appreciated, since it allows specifying a different location, such as an external disk, where more space might be available.
One issue, however, is if we put specify OLLAMA_MODELS in our .zshrc, for example, the .zshrc file isn't read when Ollama starts initially or when Ollama restarts after an update.
Perhaps I'm missing something?
Otherwise, it would be great to make this something that can be configured/set directly in Ollama instead of needing to quit Ollama, open a Terminal (so that .zshrc is read) and start Ollama from the Terminal so that the OLLAMA_MODELS directory is set instead of using the default.
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I_kwDOJ0Z1Ps6SeQiF
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Need qwen2:math !!
|
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https://github.com/QwenLM/Qwen2-Math
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I_kwDOJ0Z1Ps6lBfmQ
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|
Warning: could not connect to a running Ollama instance
|
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NONE
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### What is the issue?
I manually installed Ollama and successfully ran the command 'ollama serve' in a terminal,but when i try to ran the command 'ollama -v',it showed 'Warning: could not connect to a running Ollama instance
Warning: client version is 0.5.4'
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
_No response_
|
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PR_kwDOJ0Z1Ps5b1LE3
| 692
|
Increase client/server streaming buffer size to prevent `token too long` error
|
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| 2023-10-03T20:28:42
| 2023-10-04T18:09:01
| 2023-10-04T18:09:00
|
CONTRIBUTOR
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In the case of a large input the response from `/generate` would be very long due to the encoded context length. Increase the buffer size to prevent this error.
resolves #687
|
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PR_kwDOJ0Z1Ps5gDPzg
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|
update llama.cpp
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| 2,361,138,517
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I_kwDOJ0Z1Ps6MvB1V
| 5,129
|
gpu destroys TMPDIR
|
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[] | null | 2
| 2024-06-19T02:53:24
| 2024-06-25T07:16:32
| 2024-06-20T17:41:00
|
NONE
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### What is the issue?
When building ollama package for ALT Linux I noticed that %buildroot (directory where new binaries are installed) is disappeared after `go test github.com/ollama/ollama/gpu`. Our `%buildroot` (`/usr/src/tmp/ollama-buildroot`) is inside of `TMPDIR` (`/usr/src/tmp`).
I am not completely investigated the issue but this is extremely dangerous practice to delete directories you did not create. If it deletes %buildroot it may delete anything else such as homedir?
I see so suspicious code fragment in `gpu/assets.go`:
```go
func Cleanup() {
lock.Lock()
defer lock.Unlock()
runnersDir := envconfig.RunnersDir
if payloadsDir != "" && runnersDir == "" && runtime.GOOS != "windows" {
// We want to fully clean up the tmpdir parent of the payloads dir
tmpDir := filepath.Clean(filepath.Join(payloadsDir, ".."))
slog.Debug("cleaning up", "dir", tmpDir)
err := os.RemoveAll(tmpDir)
if err != nil {
// On windows, if we remove too quickly the llama.dll may still be in-use and fail to remove
time.Sleep(1000 * time.Millisecond)
err = os.RemoveAll(tmpDir)
if err != nil {
slog.Warn("failed to clean up", "dir", tmpDir, "err", err)
}
}
}
}
```
So it tries to delete parent of TMPDIR? What if HOME or other important directories are there?
Please make it not delete directories or files it did not create
### OS
Linux
### GPU
Other
### CPU
Intel
### Ollama version
0.1.44
|
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