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PR_kwDOJ0Z1Ps5aEmqk
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support for packaging in multiple cuda runners
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DeepSeek-V2-Lite-Chat - ERROR [validate_model_chat_template] The chat template comes with this model is not yet supported
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NONE
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### What is the issue?
When I load DeepSeek-V2 I see this message:
`ERROR [validate_model_chat_template] The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses | tid="139815279423488" timestamp=1718280244`
I do not know if it's related, but on inference the model sometimes, randomly (?), starts generating exclusively in Chinese, even if I ask it to generate the response in English.
### OS
WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
git HEAD from June 11, 2024
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exit status 2
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| 2024-12-07T03:25:46
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NONE
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### What is the issue?
exit status 2
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama version is 0.5.1
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PR_kwDOJ0Z1Ps5crty9
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add how to quantize doc
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feat: K/V cache quantisation (massive vRAM improvement!)
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This PR introduces optional K/V (context) cache quantisation.
(PR recreated after Github broke https://github.com/ollama/ollama/pull/5894 🤦)
In addition the deprecated `F16KV` parameter has been removed, if a user wishes for some reason to run the KV at f32, they can provide that as an option.
## Impact
- With defaults (f16) - none, behaviour is the same as the current defaults.
- With q8_0
- **The K/V context cache will consume 1/2 the vRAM** (!)
- A _very_ small loss in quality within the cache
- With q4_0
- **the K/V context cache will consume 1/4 the vRAM** (!!)
- A small/medium loss in quality within the cache
- For example, loading llama3.1 8b with a 32K context drops vRAM usage by cache from 4GB to 1.1GB
- The and q4_1 -> q5_1 in between.
Additional quantisations supported by llama.cpp and this PR that may depend on the quantisation of the model you're running:
`q5_1`, `q5_0`, `q4_1`, `iq4_nl`
- Fixes https://github.com/ollama/ollama/issues/5091
- Related discussion in llama.cpp - https://github.com/ggerganov/llama.cpp/discussions/5932
- (Note that ExllamaV2 has a similar feature - https://github.com/turboderp/exllamav2/blob/master/doc/qcache_eval.md)
## Screenshots
Example of estimated (v)RAM savings - f16 (q8_0,q4_0)
<img width="1211" alt="image" src="https://github.com/user-attachments/assets/a3520770-7b31-40c7-b45b-4aad6db9b117">
### f16
### q4_0
### q8_0
## Performance
llama.cpp did some perplexity measurements (although looking at the commits things have likely improved even further since May when they were done, and CUDA graphs were later fixed etc....): https://github.com/ggerganov/llama.cpp/pull/7412#issuecomment-2120427347
As far as I can tell (at least with q6_k quants) there isn't much of a noticeable hit to performance.
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I_kwDOJ0Z1Ps6CZIpH
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A way to communicate reasons for low performance to users of CLI & API
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| 2024-03-15T03:02:34
| 2024-05-18T03:18:55
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CONTRIBUTOR
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People are often concerned about what they perceive to be low performance and/or whether Ollama is making optimal use of their RAM/VRAM/GPU/CPU cores. This comes up frequently in github issues, the main Discord channel and the Discord help channel.
I think a lot of these queries could be avoided if Ollama communicated more information to users in a more concise and obvious way than digging in the log file. Perhaps a message in the CLI, just before displaying the REPL prompt, and a status message field in the API response.
Common conditions seem to be
* Model + context too large for VRAM
* GPU detected but not used, using CPU for inference
* No GPU detected, using CPU for inference
The message should warn that performance will be low and point user towards remedies:
* Using a model with fewer parameters and/or smaller quantization
* A URL for troubleshooting issues with GPUs.
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PR_kwDOJ0Z1Ps5y4-lV
| 5,128
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Fix levelzero empty symbol detect
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| 2024-06-19T01:30:27
| 2024-07-02T05:46:36
| 2024-06-19T15:33:16
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CONTRIBUTOR
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we notice many users report ollama crash on Windows platform when ollama try to discover Intel GPUs after introducing Intel GPUs support.
this issue may occur on some older Intel CPUs which before 11Gen.
this is due to the driver library of iGPUs before 11Gen processors don't have the symbols which initial level-zero needed, like `zesInit` , and there is also a bug in `oneapi_init`, `l[i].p` is a pointer to pointer, func ptr stored in `*l[i].p` so we should detect whether `*l[i].p` is null ptr to check whether we load the func successfully.
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I_kwDOJ0Z1Ps6iw5Xa
| 8,027
|
ollama_llama_server not signed
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| 2024-12-10T17:24:04
| 2024-12-10T18:29:26
| 2024-12-10T18:29:26
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NONE
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### What is the issue?
The `ollama_llama_server` binary for macOS doesn't seem to be signed. This causes problems e.g. when using binary authorization tools like [Santa](https://github.com/northpolesec/santa) to allowlist the usage of Ollama on machines.
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.5.1
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PR_kwDOJ0Z1Ps5aSFdO
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Update API docs
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cleanup docs, add show and push.
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I_kwDOJ0Z1Ps55wpqm
| 1,536
|
Ability to keep a model in memory for longer
|
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| 2023-12-15T02:45:45
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is there a way to keep the model in memory or gpu memory ?
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I_kwDOJ0Z1Ps50guwl
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Manually download and upload models
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| 2023-10-20T17:22:32
| 2024-08-28T19:19:34
| 2023-10-30T22:07:42
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NONE
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hey guys. Having issues getting with this part a work with corporate proxy: docker exec -it ollama ollama run llama2.
2 issues.
1) When I set a proxy something breaks.
2) model url / cert not allowed / blocked. To work around this I will need to manually download model files upload to the container.
Can we manually download and upload model files? Where do I put the model files after I have download them?
|
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I_kwDOJ0Z1Ps50qX3o
| 878
|
llama2 image not really llama2?
|
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Today I tested all models listed on this page: https://ollama.ai/library
Using the system prompt and the question "who is albert einstein?":
```
I want you to act like Ludwig van Beethoven.
I want you to respond and answer like Ludwig van Beethoven, using the tone, manner and vocabulary Ludwig van Beethoven would use.
You must only know all of the knowledge of Ludwig van Beethoven nothing else.
The status of you is as follows:
Location: Beethoven’s home
Status: Ludwig van Beethoven’s first music teacher was his father. His father attempted to promote him as a child prodigy, claiming that Beethoven was six on the posters for his first public performance in March 1778. Beethoven was taught by several local teachers, including the court organist Gilles van den Eeden, Tobias Friedrich Pfeiffer, Franz Rovantini, and court concertmaster Franz Anton Ries. His tuition began when he was only five years old. The regime was harsh and intensive, often reducing him to tears. With the involvement of the insomniac Pfeiffer, there were irregular late-night sessions, with the young Beethoven dragged from his bed to the keyboard. His musical talent was obvious at a young age.
```
Every model behaves like expected, except for the default **llama2** model.
Which responds in a weeby kinda way:
```
Ah, a fellow genius! *adjusts spectacles* Albert Einstein? *chuckles* Ah, yes, I've heard of him. A clever little German fellow with a head full of ideas, no? *grin*
Wait, what was the question again? Oh, you wanted to know something about that... Albert Einstein? *scratches chin* Well, I must say, he's quite the fellow. A physicist, you say? *nods* Yes, I've heard of his
work. He's been making quite a stir with his theories on relativity and time. *smirks* Sounds like a bit of hogwash to me, but I suppose he's got some clever ideas.
But tell me, have you met this Einstein fellow? *curious eyes* What is it that he does? *leaning forward* Does he play the pianoforte? *inquiring mind* Oh, I do love a good tune on the piano! *smiles*
Anyway, enough about that fellow. *adjusts spectacles* Tell me, what brings you to my humble abode? Are you here to discuss the finer points of music theory? *winks* Or perhaps you'd like a lesson in the art
of composition? *grin* I'm all ears, young man! *chuckles*
Now, if you'll excuse me, I must get back to my latest masterpiece. *adjusts monocle* It's a symphony, you see, and it's going to be simply... *exhales dramatically* Magnificent! *smirks*
```
Whereas for example **llama2:13b-q8_0** responds:
```
Albert Einstein was born in 1879 in Germany. His life’s work was developing the theory of relativity and the concept that mass could be converted into energy.
Because Albert Einstein was considered to be the greatest physicist of his day, he became a very well-known figure, both before and after the publication of his great scientific theories in 1905. Although he
spent most of his life outside of Germany – living in other European countries like Switzerland, as well as in the United States – he was proud to call himself a German. However, when Adolf Hitler became the
dictator of Germany in 1933 and began persecuting Jews like Albert Einstein, he decided to renounce his German citizenship for good.
```
Is this expected behaviour or might the llama2 image be compromised?
Or a smaller variant:
```
SYSTEM """
I want you to act like President Obama.
"""
```
Question "Who are you?"
Answer:
```Ah, a fellow American! *adjusts sunglasses* It's great to meet you, my friend! *smirks* I am Barack Hussein Obama, the 44th President of the United States. *pauses for dramatic effect*
Now, I know what you're thinking: "Barack, how can we trust a man who has been in office for two terms and still looks like he's ready to hit the gym?" *winks* Well, let me tell you something, my fellow
American. I may have been out of office for a few years now, but I'm still as sharp as ever. *adjusts tie* And I've got some great stories to share from my time in the White House. *grins mischievously*
So, what can I do for you today? Do you need some advice on how to run a successful campaign? Or maybe you just want to chat about the latest news and current events? *leaning back in chair* Whatever it is,
feel free to shoot. *smirks*
And remember, as I always say: "Yes We Can!" *winks*
```
and response from **llama2:13b-chat**
```
Greetings, my fellow American! I am Barack Hussein Obama, the 44th President of the United States. I served two terms in office from 2009 to 2017, and I am proud of all that we accomplished during my time in
the White House.
As President, I worked tirelessly to strengthen our economy, reform our healthcare system, and protect the rights and freedoms of all Americans. I also had the privilege of being the first African American to
hold the office of President, and I am deeply committed to advancing the cause of equality and justice for all.
I hope that you will join me in my efforts to make America a better place for everyone. Together, we can create a brighter future for ourselves, our children, and future generations. Thank you!
```
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| 2,013,270,341
|
I_kwDOJ0Z1Ps54ABFF
| 1,291
|
ollama runpod serverless template
|
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| 2023-11-27T22:56:18
| 2023-12-11T15:19:34
| 2023-12-11T15:19:34
|
NONE
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is there an example of deploy a model using an ollama in a runpod serverless ?
|
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I_kwDOJ0Z1Ps59UId_
| 2,208
|
Feature: API error response in case of exceeding context length
|
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| 2024-01-26T15:41:30
| 2024-11-06T19:02:07
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It would be good to get a error response back from the API if the prompt exceeds the context length of the model, similar to openai API.
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I_kwDOJ0Z1Ps6HQGNX
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Llama 3 BPE tokenization needs improvement
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| 2024-04-29T14:04:06
| 2024-05-13T15:53:02
| 2024-05-11T13:41:15
|
NONE
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### What is the issue?
[This PR](https://github.com/ggerganov/llama.cpp/pull/6920) just merged on llama.cpp, which contained important improvements to how tokenization worked for Llama 3 and other models. An example of the issue is [noted here](https://github.com/ggerganov/llama.cpp/issues/6914).
Hopefully ollama can update to the latest llama.cpp quickly and make a new release.
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
all versions up to this point
|
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I_kwDOJ0Z1Ps6VCV0t
| 6,591
|
Ollama failing with `CUDA error: PTX JIT compiler library not found`
|
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| 2024-09-02T09:06:01
| 2024-09-26T19:55:34
| 2024-09-03T21:14:04
|
NONE
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### What is the issue?
The GPU seems to be detected:
```
CUDA driver version: 11.4
time=2024-08-30T16:57:23.032Z level=DEBUG source=gpu.go:123 msg="detected GPUs" count=1 library=/usr/lib/aarch64-linux-gnu/tegra/libcuda.so.1.1
[GPU-d90c0d9d-5e59-56b1-b519-6439b1d74328] CUDA totalMem 30990 mb
[GPU-d90c0d9d-5e59-56b1-b519-6439b1d74328] CUDA freeMem 23431 mb
[GPU-d90c0d9d-5e59-56b1-b519-6439b1d74328] Compute Capability 7.2
...
time=2024-08-30T16:57:23.201Z level=INFO source=types.go:105 msg="inference compute" id=GPU-d90c0d9d-5e59-56b1-b519-6439b1d74328 library=cuda compute=7.2 driver=11.4 name=Xavier total="30.3 GiB" available="22.9 GiB"
...
time=2024-08-30T16:58:09.212Z level=DEBUG source=gpu.go:410 msg="updating cuda memory data" gpu=GPU-d90c0d9d-5e59-56b1-b519-6439b1d74328 name=Xavier overhead="0 B" before.total="30.3 GiB" before.free="22.9 GiB" now.total="30.3 GiB" now.free="23.0 GiB" now.used="7.3 GiB"
```
But then I get this error:
```
CUDA error: PTX JIT compiler library not found
current device: 0, in function ggml_cuda_compute_forward at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:2313
/go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:101: CUDA error
```
Here are the installed version:
```
ii nvidia-cuda 5.1.3-b29 arm64 NVIDIA CUDA Meta Package
ii nvidia-cuda-dev 5.1.3-b29 arm64 NVIDIA CUDA dev Meta Package
ii nvidia-jetpack 5.1.3-b29 arm64 NVIDIA Jetpack Meta Package
ii nvidia-jetpack-dev 5.1.3-b29 arm64 NVIDIA Jetpack dev Meta Package
ii nvidia-jetpack-runtime 5.1.3-b29 arm64 NVIDIA Jetpack runtime Meta Package
ii nvidia-l4t-cuda 35.5.0-20240219203809 arm64 NVIDIA CUDA Package
ii cuda 11.4.19-1 arm64 CUDA meta-package
```
Ollama is used with the docker container et inside a kubernetes cluster.
### OS
Docker
### GPU
Nvidia
### CPU
ARM
### Ollama version
0.3.8
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PR_kwDOJ0Z1Ps5pIVTe
| 3,014
|
decode ggla
|
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| 2024-03-08T23:47:08
| 2024-03-09T00:14:54
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|
CONTRIBUTOR
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split from #2926
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I_kwDOJ0Z1Ps6cxolD
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[Model request] The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
|
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https://github.com/jam-cc/MMAD
The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
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I_kwDOJ0Z1Ps6n8qTg
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Could not install deepseek-r1:7b version
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### What is the issue?
Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/aa/aabd4debf0c8f08881923f2c25fc0fdeed24435271c2b3e92c4af36704040dbc/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%2F20250129%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20250129T093708Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=044eb3053c34ac7e27101080923c27a8d4f0adc5334de93463391b2cf3d208cd": dial tcp: lookup dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com: no such host
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.5.7
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I_kwDOJ0Z1Ps6AIhya
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Not an issue, just a thank you.
|
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NONE
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The Ollama project is awesome, thanks for all the work you do.
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PR_kwDOJ0Z1Ps5xXsXj
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|
Created a couple C# example applications using OllamaSharp
|
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I've added C# examples leveraging the [OllamaSharp](https://github.com/awaescher/OllamaSharp) wrapper library.
|
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PR_kwDOJ0Z1Ps5fcwS1
| 1,129
|
fix: parse ggml model types
|
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| 2023-11-14T19:56:54
| 2023-12-06T23:54:41
| 2023-11-22T21:09:14
|
CONTRIBUTOR
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I'm kind of surprised this didn't come up earlier, noticed this while importing a less common ggml model type. Model parsing was returning `nil` for uncommon types which results in a nil pointer dereference. Fix this by moving model parsing up a level and try to decode as a llama model in the default case.
|
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PR_kwDOJ0Z1Ps5_sAY-
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|
Update README.md - Revised vnc-lm project description
|
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| 2024-10-23T22:39:29
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|
CONTRIBUTOR
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Hi,
This is just a small change updating the description for my project, [vnc-lm](https://github.com/jake83741/vnc-lm).
Thanks,
Jake
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I_kwDOJ0Z1Ps5-MMhO
| 2,346
|
Use llama2-chinese or yi to ask questions in Chinese. After about 15 requests, the response request cannot be returned.
|
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| 2024-02-20T04:04:26
| 2024-02-20T04:04:26
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NONE
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Use llama2-chinese or yi to ask questions in Chinese. After about 15 requests, the response request cannot be returned.
<img width="1494" alt="image" src="https://github.com/ollama/ollama/assets/130064485/b850c5c6-d09e-472b-9592-5f2bb115c2ed">
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I_kwDOJ0Z1Ps6QS767
| 5,810
|
Tinyllama has issues understanding the Modelfile
|
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### What is the issue?
When I start a conversation with Tinyllama with a greeting, it answer with a revision of the Modelfile. That's strange. Here is an example
### OS
Linux
### GPU
Other
### CPU
Other
### Ollama version
0.2.7
|
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I_kwDOJ0Z1Ps6FXqpv
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Error: Post "http://127.0.0.1:11434/api/chat": read tcp 127.0.0.1:59108->127.0.0.1:11434: wsarecv: An existing connection was forcibly closed by the remote host.
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| 2024-04-11T12:06:28
| 2024-05-21T18:30:34
| 2024-05-21T18:30:33
|
NONE
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here is my app.log:
time=2024-04-11T19:53:53.911+08:00 level=INFO source=logging.go:45 msg="ollama app started"
time=2024-04-11T19:53:53.940+08:00 level=DEBUG source=lifecycle.go:32 msg="starting callback loop"
time=2024-04-11T19:53:53.940+08:00 level=DEBUG source=store.go:61 msg="loaded existing store C:\\Users\\17900\\AppData\\Local\\Ollama\\config.json - ID: ef96d596-af07-43a7-97e2-f6af540c5021"
time=2024-04-11T19:53:53.940+08:00 level=DEBUG source=lifecycle.go:66 msg="Not first time, skipping first run notification"
time=2024-04-11T19:53:53.941+08:00 level=DEBUG source=server.go:134 msg="heartbeat from server: Head \"http://127.0.0.1:11434/\": dial tcp 127.0.0.1:11434: connectex: No connection could be made because the target machine actively refused it."
time=2024-04-11T19:53:53.941+08:00 level=INFO source=server.go:135 msg="unable to connect to server"
time=2024-04-11T19:53:53.947+08:00 level=INFO source=server.go:91 msg="started ollama server with pid 7836"
time=2024-04-11T19:53:53.947+08:00 level=INFO source=server.go:93 msg="ollama server logs C:\\Users\\17900\\AppData\\Local\\Ollama\\server.log"
time=2024-04-11T19:53:53.947+08:00 level=DEBUG source=eventloop.go:24 msg="starting event handling loop"
time=2024-04-11T19:53:56.952+08:00 level=DEBUG source=updater.go:73 msg="checking for available update" requestURL="https://ollama.com/api/update?arch=amd64&nonce=4ubr2F5IgZhBzSseU_H9iQ&os=windows&ts=1712836436&version=0.1.31"
time=2024-04-11T19:53:58.123+08:00 level=DEBUG source=updater.go:82 msg="check update response 204 (current version is up to date)"
time=2024-04-11T19:54:06.001+08:00 level=WARN source=server.go:113 msg="server crash 1 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:06.509+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:07.516+08:00 level=WARN source=server.go:113 msg="server crash 2 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:08.022+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:10.031+08:00 level=WARN source=server.go:113 msg="server crash 3 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:10.536+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:12.217+08:00 level=DEBUG source=logging_windows.go:12 msg="viewing logs with start C:\\Users\\17900\\AppData\\Local\\Ollama"
time=2024-04-11T19:54:13.540+08:00 level=WARN source=server.go:113 msg="server crash 4 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:14.052+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:18.066+08:00 level=WARN source=server.go:113 msg="server crash 5 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:18.573+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:23.575+08:00 level=WARN source=server.go:113 msg="server crash 6 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:24.081+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:30.085+08:00 level=WARN source=server.go:113 msg="server crash 7 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:30.592+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:37.604+08:00 level=WARN source=server.go:113 msg="server crash 8 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:38.106+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:46.117+08:00 level=WARN source=server.go:113 msg="server crash 9 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:46.626+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:54:55.632+08:00 level=WARN source=server.go:113 msg="server crash 10 - exit code 3221226505 - respawning"
time=2024-04-11T19:54:56.140+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:55:06.150+08:00 level=WARN source=server.go:113 msg="server crash 11 - exit code 3221226505 - respawning"
time=2024-04-11T19:55:06.655+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:55:17.667+08:00 level=WARN source=server.go:113 msg="server crash 12 - exit code 3221226505 - respawning"
time=2024-04-11T19:55:18.177+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:55:30.185+08:00 level=WARN source=server.go:113 msg="server crash 13 - exit code 3221226505 - respawning"
time=2024-04-11T19:55:30.693+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:55:43.694+08:00 level=WARN source=server.go:113 msg="server crash 14 - exit code 3221226505 - respawning"
time=2024-04-11T19:55:44.205+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:55:58.210+08:00 level=WARN source=server.go:113 msg="server crash 15 - exit code 3221226505 - respawning"
time=2024-04-11T19:55:58.718+08:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started"
time=2024-04-11T19:56:06.457+08:00 level=DEBUG source=eventloop.go:148 msg="unmanaged app message, lParm: 0x204"
time=2024-04-11T19:56:07.035+08:00 level=DEBUG source=logging_windows.go:12 msg="viewing logs with start C:\\Users\\17900\\AppData\\Local\\Ollama"
how to fix it?thanks
|
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| 2,130,347,351
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I_kwDOJ0Z1Ps5--oVX
| 2,464
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Error: invalid version -- when attempting to run llava
|
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| 2024-02-12T15:15:06
| 2024-02-12T15:30:58
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NONE
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I attempted to install and run llava on an m1 mac and got the following:
~ % ollama run llava
pulling manifest
pulling 170370233dd5... 100% |███████████████| (4.1/4.1 GB, 46 MB/s)
pulling 72d6f08a42f6... 100% |█████████████████| (624/624 MB, 49 MB/s)
pulling 43070e2d4e53... 100% |███████████████████| (11/11 kB, 1.1 MB/s)
pulling c43332387573... 100% |████████████████████| (67/67 B, 538 kB/s)
pulling ed11eda7790d... 100% |████████████████████| (30/30 B, 185 kB/s)
pulling 7c658f9561e5... 100% |██████████████████| (564/564 B, 1.8 MB/s)
verifying sha256 digest
writing manifest
removing any unused layers
success
⠋ Error: invalid version
If I run `ollama run llava` again (above was the first time), I get:
`⠋ Error: invalid version`
If I use the API on localhost:
`{"error":"invalid version"}`
Any help would be appreciated. Thanks.
|
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I_kwDOJ0Z1Ps6ieG2B
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Why is OllamaSetup.exe so large
|
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| 2024-12-09T02:38:40
| 2024-12-18T09:14:28
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NONE
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I also tried downloading [ollama-windows-arm64.zip](https://github.com/ollama/ollama/releases/download/v0.5.1/ollama-windows-arm64.zip) and replacing some files in my user folder which doesn' t fix the tray icon still notify the update. Could there be a smaller OllamaSetup.exe ?
This combined with the tray "restart to update" button not effective can be very annoying.
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I_kwDOJ0Z1Ps6i-rRu
| 8,057
|
where can I find ollama model path?
|
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### What is the issue?
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I_kwDOJ0Z1Ps6OWm_p
| 5,458
|
Enable Row Split Support
|
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For multi-gpu setups, enabling row-split can have very significant performance improvements. On my machine with 3xP40, I was able to get a speedup from ~3t/s to ~10t/s.
Unfortunately, it doesn't look like there's any way to get this flag (`-sm row`) down to llama.cpp, which should otherwise support this.
Wondering if it's possible to either add a way to pass along flags to llama.cpp, or to have an option which enables this flag. Given the massive performance improvement that it brings though, it seems like this should be possible, and not very much work to implement.
I saw that passing along flags was discussed [here](https://github.com/ollama/ollama/pull/4120#issuecomment-2094747527), but was unfortunately not implemented.
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PR_kwDOJ0Z1Ps5jKrqy
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Add embeddings to API
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| 2024-01-03T19:52:52
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The API is missing the embeddings point, so this adds it.
|
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I_kwDOJ0Z1Ps5yVMTY
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Unable to create temp file for runner on Windows
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When running Ollama on Windows the llama.cpp runner may not be able to be added to the temp directory.
```
llama runner not found: CreateFile C:\Users\...\AppData\Local\Temp\ollama432423342\...\server.exe: The system cannot find the specified path.
```
|
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PR_kwDOJ0Z1Ps5yacph
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|
Re-introduce the `llama` package
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MEMBER
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This PR brings back the `llama` package, making it possible to call llama.cpp and ggml APIs from Go directly via CGo. This has a few advantages:
1. C APIs can be called directly from Go without needing to use the previous "server" REST API
2. On macOS and for CPU builds on Linux and Windows, Ollama can be built without a `go generate ./...` step, making it easy to get up and running to hack on parts of Ollama that don't require fast inference
3. Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners takes <5 min on a fast CPU)
4. No git submodule making it easier to clone and build from source
This is a big PR, but much of it is vendor code except for:
1. `llama.go` CGo bindings
2. `example/`: a simple example of running inference
3. `runner/`: a subprocess server designed to replace the `llm/ext_server` package
4. `Makefile` an as minimal as possible `Makefile` to build the `runner` package for different targets (cpu, avx, avx2, cuda, rocm)
The easiest way to try out the PR:
```
cd llama
make -j
```
Which will produce `ollama_runner` binaries based on the current platform.
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| 1,943,655,360
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I_kwDOJ0Z1Ps5z2dPA
| 791
|
Sending and receiving Context with ollama.call()
|
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| 2023-10-15T02:28:30
| 2023-10-16T18:05:11
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|
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|
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Is the input and output of context supported yet with ollama.call()?
javascript:
```
import { Ollama } from "langchain/llms/ollama";
const ollama = new Ollama({
baseUrl: "http://localhost:11434",
model: model,
temperature: parseFloat(temperature),
topP: parseFloat(topp)
});
response = await ollama.call(input);
```
|
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| 2,647,219,698
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I_kwDOJ0Z1Ps6dyV3y
| 7,596
|
unable to get images to be read in ollama 0.4.1 for llama3.2-vision
|
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| 2024-11-10T13:04:45
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|
NONE
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### What is the issue?
This is my setup on Windows 11
Whatever image i upload, get variations of the same message as shown. What am I doing incorrectly? The png file is 145kb, if it matters
### OS
Windows
### GPU
_No response_
### CPU
Intel
### Ollama version
0.4.1
|
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| 2,607,935,991
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I_kwDOJ0Z1Ps6bcfH3
| 7,329
|
Terminate the current task after the REST request is actively ended
|
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| 2024-10-23T09:39:11
| 2024-10-23T16:27:42
| 2024-10-23T16:27:42
|
NONE
|
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Can the current executing task be terminated after a REST request is actively interrupted?
|
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I_kwDOJ0Z1Ps6iq7D9
| 8,023
|
Ollama is very slow after running for a while
|
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| 2024-12-10T06:35:28
| 2025-01-13T01:37:00
| 2025-01-13T01:37:00
|
NONE
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### What is the issue?
CPU: Intel(R) Xeon(R) Silver 4410Y
GPU: NVIDIA L40S-24Q 24GB
DRAM: 32GB
OS: Ubuntu 24.04.1
GPU Driver: vWS 550.127.05 / 550.90.07
CUDA: 12.4
Ollama version: v0.5.1/v0.4.7/v0.3.14
Model: llama3.1:8b/Gemma2:2b/Qwen2.5:7b
Hello, I'm using to run ollama on a VM with grid GPU, but both locally and on Docker I'm experiencing the following problem:.
For the first 20 minutes after running, the results are very good, both model reading and reasoning speed are satisfactory.
However, when running again after some time, the model reading and reasoning speed is abnormally slow.
How can this situation be resolved?
Any help would be greatly appreciated.
Here's the log and GPU operation status:
[ollama.log](https://github.com/user-attachments/files/18073070/ollama.log)
```
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.127.05 Driver Version: 550.127.05 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-24Q On | 00000000:03:00.0 Off | N/A |
| N/A N/A P0 N/A / N/A | 4603MiB / 24576MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 208396 C ...unners/cuda_v12/ollama_llama_server 4594MiB |
+-----------------------------------------------------------------------------------------+
```
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
v0.5.1
|
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I_kwDOJ0Z1Ps6K1Q9r
| 4,768
|
Cannot get web to verify service is running
|
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### What is the issue?
I have installed ollama on ubuntu (proxmox lxc) successfully using the script, I am able to download and access it on command line, but when I attempt to access the ip:11434 the page fails to load. I appear to have no external access to the service on the machine. Any help is greatly appreciated.
### OS
Linux
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.1.39
|
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I_kwDOJ0Z1Ps6VzwXn
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|
ERROR unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama/runners'
|
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| 2024-09-09T09:03:14
| 2024-09-09T16:07:14
| 2024-09-09T16:07:04
|
NONE
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### What is the issue?
Model is Downloaded but unable to run it. Tried to locate ollama/runners couldn't find it.
2024/09/09 04:48:23 config.go:173: ERROR unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama/runners'
.......
pulling manifest
verifying sha256 digest
writing manifest
success
Error: no suitable llama servers found
Running in a windows system
Specs :
Processor : Snapdragon(TM) X Elite - X1E78100 - Qualcomm(TM) Oryon(TM) CPU 3.42 GHz
GPU : Qualcomm(R) Adreno(TM) X1-85
NPU : Snapdragon(R) X Elite - X1E78100 - Qualcomm(R) Hexagon (TM)
### OS
Windows
### GPU
Other
### CPU
Other
### Ollama version
0.3.9
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I_kwDOJ0Z1Ps6E239V
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|
Switching dynamically between multiple LLM models on VRAM
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| 2024-04-06T00:25:39
| 2024-04-23T15:31:41
| 2024-04-23T15:31:41
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NONE
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### What are you trying to do?
At the moment, Ollama needs to load LLM one by one. It should be possible to have multiple LLM resident in VRAM memory and switch dynamically between the two.
### How should we solve this?
1. Check if the requested images can be loaded within the current hardware VRAM budget.
2. load multiple images in VRAM.
3. Augment API to switch dynamically between the two.
### What is the impact of not solving this?
The present latency when loading and unloading multiple models.
### Anything else?
_No response_
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PR_kwDOJ0Z1Ps5YGi0-
| 364
|
reimplement chunked uploads
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| 2023-08-16T21:45:42
| 2023-08-17T16:58:52
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CONTRIBUTOR
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I_kwDOJ0Z1Ps52q_Dw
| 1,110
|
Converting mistral7b finetuned model produces garbage output
|
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Hi,
I've finetuned a Mistral-7B-v0.1 model from hugging face using QLoRa as per the typical SFT scripts released by the hugging face team. However, after converting it to gguf format using the docker method, I found that my model produces garbage.
For reference, this is the format of the prompts I trained on:
```<s>
### User:
{{user prompt}}
### Assistant:
{{assistant response}}
</s>
```
The inference results from hugging face look ok, I struggle with getting the model to stop generating but setting max_tokens generally solves this (although an unideal workaround).
But when converting to gguf and running via Ollama, the model often goes off-task and does its own thing. Furthermore, unless I put '### Assistant:' and '</s>' as stop tokens, it often produces input interleaving with lots of '### Assistant''s e.g.:
```
We've noticed that some people love to go sightseeing in Paris and we have a special offer for this year!
### Assistant:
I love Paris
### Assistant:
I love Paris.
```
So I have a few questions/paths to investigate:
1. Has anyone been able to successfully fine-tune a model and convert it to gguf and get it to work well with Ollama?
2. Could it be that I'm doing something wrong in converting the mistral finetuned model? I save the merged lora model and run the docker converting to gguf via llama.cpp on that merged model as per normal.
3. Could it be that I need to produce a better 'base' fine-tuned model before converting to gguf for it to work well in Ollama?
Note, I also tried converting a base mistral7b model downloaded from hugging face as as sanity check and that worked great.
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PR_kwDOJ0Z1Ps5cCqLp
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|
validate api options fields from map
|
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| 2023-10-05T18:57:33
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|
CONTRIBUTOR
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We use a map to set options from the API so that we can see which option fields were specified, otherwise we override default options with zero values. The issue here is that there was no validation that the input option fields were valid, so using an incorrect field by mistake did not return an error.
New response:
```
curl -X 'POST' -d '{"prompt":"hello", "model": "mistral", "options": {"seed": 1234, "temperature": 0, "test": 1234}}' 'http://127.0.0.1:11434/api/generate'
{"error":"invalid options: test"}
```
from #694
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PR_kwDOJ0Z1Ps5xwEhd
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llm: update llama.cpp commit to `7c26775`
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I_kwDOJ0Z1Ps6GnDOT
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|
Slow inference speed
|
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### What is the issue?
A100 80G
Run qwen1.57B using lmdeploy framework with two processes per card and use two cards to launch qwen1.57B via ollama, which is about 2 times slower than lmdeploy
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.1.32
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|
Generation speed 3x slower after pulling newer docker image
|
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I updated the docker image recently and the inference was 3x slower (30s for new image vs. 10s for old image). I was able to get back to the previous inference speed by reverting to ollama/ollama:0.1.10. I tried ollama/ollama:0.1.15 and ollama/ollama:0.1.14 and both had the slower inference speed.
EDIT: model used was https://ollama.ai/library/openhermes2.5-mistral:latest
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I_kwDOJ0Z1Ps585md-
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|
Unable to push: max retries exceeded on slower connections
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I was able to push the `q4_0` tag to https://ollama.ai/sqs/starchat, but when I try to push other tags, I am getting an error (see below). Note the `%!F(MISSING)` below in case that is an issue.
The file size of the one that failed is 7.7GB. The `q4_0` push that succeeded was 8.4 GB.
```
$ for i in q3_K_M q4_K_M q5_K_S q5_K_M f16 f32; do ollama create sqs/starchat:beta-$i -f Modelfile.$i && ollama push sqs/starchat:beta-$i; done
transferring model data
creating model layer
creating template layer
using already created layer sha256:62b0be00997dd300b03868d7858d28f41488c0222bfc4fbb6ceb3eae39a5d4d7
using already created layer sha256:ca40f7f0151766210faa524fa8710aabf07284671aaac525eeac350d64d05132
using already created layer sha256:dd473af9080c0674443f41cb6feb59ac1e24c34f18255c78d083f138f3275a0c
writing manifest
success
retrieving manifest
pushing 62b0be00997d... 0% ▕ ▏ 1.3 MB/8.2 GB 5.2 MB/s 26m34s
Error: max retries exceeded: Put "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/repositories/sqs/starchat/_uploads/55c91d69-edf4-4a50-a278-2c7c697ba4e4/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=XXX%!F(MISSING)20240123%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240123T072755Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&partNumber=29&uploadId=XXX&X-Amz-Signature=XXX": write tcp 192.168.2.154:51301->104.18.9.90:443: write: broken pipe
```
(Note: I replaced URL query params that may contain credentials with `XXX`.)
This may just be an ephemeral error. I'll close this tomorrow if the other pushes succeeded.
|
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PR_kwDOJ0Z1Ps5aKYx_
| 518
|
amd64 linux build runner
|
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| 2023-09-12T19:26:16
| 2023-10-20T16:44:05
| 2023-09-21T13:48:18
|
CONTRIBUTOR
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Add automation that automatically creates a single ollama binary for amd64 linux builds.
Limitations:
- Requires glibc 2.29 (the glibc version ubuntu 20.04 has packed in), ideally we build on an ubuntu 16.04 or 18.04 runner instead to maximize glibc compatibility, but that will require a custom runner. `glibc` is used by linux to access kernal functionality so it cant really be updated by an end-user without updating their OS.
Future work:
- Ideally I'd rather just install both version of nvcc on one runner and swap between them. I tried this and I hit some issues with the wrong cuda version being referenced during builds.
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I_kwDOJ0Z1Ps6Rq5oc
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When processing long texts, it freezes and there is no response.
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| 2024-08-02T03:45:05
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NONE
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### What is the issue?
I am using Ollama to run llama3.1:70b. When processing long texts, it freezes and there is no response. I have checked the server’s CPU, memory, and GPU usage, and they are all low. What could be the problem?
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.30
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How to Deploy LLM Based on ollama in an offline environment?
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NONE
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ollama is now ready and I am deploying based on binary files. I have downloaded the GLM4 model file and [the glm4 adaptation file for ollama](https://ollama.com/library/glm4:9b). However, GLM4 does not provide guff at present. How can I deploy inference glm4 based on ollama in an offline environment?
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can't be installed on ubuntu runing in a podman container.
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| 2024-04-13T14:01:22
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CONTRIBUTOR
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### What is the issue?
I spin up a container of ubuntu using podman but i can't install ollama that curl command does not work.
### 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
amd64
### Platform
Docker
### Ollama version
_No response_
### GPU
AMD
### GPU info
_No response_
### CPU
AMD
### Other software
_No response_
|
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I_kwDOJ0Z1Ps6Y5KP-
| 7,094
|
dark mode for website
|
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| 2024-10-03T22:51:43
| 2024-10-04T15:54:43
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NONE
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These are the dark mode docs for tailwindcss
https://tailwindcss.com/docs/dark-mode
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PR_kwDOJ0Z1Ps53DNqX
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|
fix modelfile message quotes
|
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| 2024-07-31T23:54:18
| 2024-08-01T00:05:45
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CONTRIBUTOR
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message commands should quote the content but it's not correctly formatted so it outputs content verbatim. fix the formatting will fix quoting
https://github.com/ollama/ollama/blob/main/parser/parser.go#L41-L43
resolves #6103
|
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| 2,103,466,374
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I_kwDOJ0Z1Ps59YFmG
| 2,227
|
model not loading in GPU
|
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| 2024-01-27T09:13:12
| 2024-01-27T09:24:30
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NONE
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Hi, great project congrats!
I noticed that event if ollama (in docker) logs say it offloaded layers to GPU
nvdia-smi report no actual usage
Is this an expected beahviour?
A similar setup with [localai](https://github.com/mudler/LocalAI) has similar logs but with better performances, denoting it is actually using the gpu
|
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I_kwDOJ0Z1Ps6BiKDv
| 2,976
|
When https://api.openai.com/ Access time or timeout, openai/API/models request causing UI interface not to be displayed
|
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| 2024-03-07T07:56:54
| 2024-03-07T08:25:24
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NONE
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PR_kwDOJ0Z1Ps5kH7cP
| 2,007
|
Add multiple CPU variants for Intel Mac
|
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| 2024-01-15T20:38:20
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COLLABORATOR
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This also refines the build process for the ext_server build.
I had initially aimed to get rid of the gcc/g++ library generation step and rely on cmake to build a shared library, but due to toolchain quirks, this model didn't work reliably. (e.g. linux worked since it's a consistent toolchain, and arm mac worked, but intel mac segfaults when calling the init function pointer). This may still be achievable in a follow up incremental PR, but for now I'll stick with g++ to create the main library we dlopen on all platforms except windows.
Another potential follow up is to consider splitting out the cuda shared libraries as a discrete download and handle it in the installer script if we don't detect cuda present on the host. That would further reduce the footprint and resolve the slow initial startup due to decompressing large payloads.
_Marking draft until I have a chance to more fully test, but so far happy path testing on mac (intel/arm), windows(cuda), and linux (rocm/cuda) looks good._
Extracting the now compressed payloads takes some time - ~15s on my older laptop
```
2024/01/15 11:12:42 payload_common.go:106: Extracting dynamic libraries...
2024/01/15 11:12:57 payload_common.go:145: Dynamic LLM libraries [rocm_v6 cpu cpu_avx2 cpu_avx cuda_v11 rocm_v5]
```
Uncompressed sizes once on disk:
```
% du -sh /tmp/ollama3226276348/*
36M /tmp/ollama3226276348/cpu
36M /tmp/ollama3226276348/cpu_avx
36M /tmp/ollama3226276348/cpu_avx2
410M /tmp/ollama3226276348/cuda_v11
30M /tmp/ollama3226276348/rocm_v5
31M /tmp/ollama3226276348/rocm_v6
```
The actual linux binary:
```
% ls -lh ollama-linux-amd64
-rwxrwxr-x 1 daniel daniel 294M Jan 15 11:12 ollama-linux-amd64
```
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|
runner.go: Make KV entry accounting more robust
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|
CONTRIBUTOR
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The structure of the accounting for KV cache shifting was carried over from the old runner but it now doesn't feel natural with the new runner. There are a number of invariants that should hold true but are difficult to reason about. There is at least one bug report that would imply that the invariants are not holding.
This reduces the number of implicit assumptions and is more forgiving of unexpected situations. It also improves behavior around which input tokens are kept when truncation occurs.
Bug #7545
|
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I_kwDOJ0Z1Ps6YCPQT
| 6,979
|
llama3.2 with docker can not access image file
|
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NONE
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### What is the issue?
I was trying llama3.2 with images and it seems it cannot access images within docker container. Similar issue was reported at #1620 and I made sure I mounted correct folder and still llama3.2 can not access the image file.
Here's how I start docker:
```bash
docker run -d --rm \
-v $HOME/Pictures/ollama:/root/Pictures \
-v $HOME/tmp/ollama/ollama-docker:/root/.ollama \
-p 11434:11434 \
--name ollama ollama/ollama
```
Let's make sure the image is in correct folder by going in the container:
```bash
$ docker exec -it ollama bash
root@67c1c7b71307:/# ls /root/Pictures/
test.png
```
Let's run llama3.2 and ask something about the image:
```bash
$ docker exec -it ollama ollama run llama3.2:3b
>>> describe the image /root/Pictures/test.png
I cannot provide a description of an image that you did not create. Is there anything else I can help you with?
```
What am I doing wrong here?
### OS
Linux
### GPU
_No response_
### CPU
Intel
### Ollama version
0.3.11
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I_kwDOJ0Z1Ps6R-cPs
| 6,184
|
Add InternLM 2.5 family of models
|
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NONE
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Please add [InternLM 2.5 family of models](https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13) to registry. Comes in 1.8, 7 and 20b sizes with both base and chat models.
While [this](https://ollama.com/internlm/internlm2.5) exists, at the moment it is F16 only.
Thank you!
|
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I_kwDOJ0Z1Ps6X5dsT
| 6,958
|
molmo by allen ai support
|
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| 2024-09-25T16:19:43
| 2025-01-17T20:00:22
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CONTRIBUTOR
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https://huggingface.co/allenai/Molmo-7B-D-0924
https://huggingface.co/allenai/Molmo-72B-0924
This models are really good and have potential and fully open-source please give support for them.
thanks.
| null |
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I_kwDOJ0Z1Ps59VpI-
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|
ollama.ai and registry.ollama.ai does not have IPv6
|
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NONE
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ollama.ai and registry.ollama.ai does not have IPv6 and it cannot be installed on AWS IPv6 only servers.
`Error: max retries exceeded: Get "https://registry.ollama.ai/v2/library/codellama/blobs/sha256:3a43f93b78ec50f7c4e4dc8bd1cb3fff5a900e7d574c51a6f7495e48486e0dac": dial tcp 34.120.132.20:443: connect: network is unreachable`
34.120.132.20 seems to on GCP, which supports IPv6.
| null |
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PR_kwDOJ0Z1Ps5jWZDc
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Add unit tests for Parser
|
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This adds a couple of basic unit tests for parsing.
Suggested in #1809
|
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| 1,912,711,759
|
I_kwDOJ0Z1Ps5yAapP
| 600
|
Support speechless-llama2-hermes-orca-platypus-wizardlm-13b or Qwen-14b models
|
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| 2023-09-26T05:31:13
| 2023-12-22T03:56:37
| 2023-12-22T03:56:36
|
NONE
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Is there any plan to support recent models like speechless-llama2-hermes-orca-platypus-wizardlm-13b or Qwen-14b? User reviews seem to indicate that they are powerful and fast even when compared with other llama2 models that have larger sizes. Thanks.
|
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I_kwDOJ0Z1Ps6ik7LC
| 8,013
|
go get version mismatch
|
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| 2024-12-09T16:14:48
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| 2024-12-10T05:58:27
|
NONE
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### What is the issue?
i try go get ... and such , just testing go cmds
esecially for newest versions
then well this appeared
go: honnef.co/go/tools@v0.5.1 (matching ...@upgrade) requires github.com/BurntSushi/toml@v1.4.1-0.20240526193622-a339e1f7089c, not github.com/BurntSushi/toml@v1.4.0 (matching ...@upgrade)
### OS
Linux
### GPU
Other
### CPU
Other
### Ollama version
from git today
|
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PR_kwDOJ0Z1Ps5bDIX_
| 582
|
fix end-of-line issue with the new prompt
|
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| 2023-09-24T00:04:07
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|
CONTRIBUTOR
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The readline library had this fix which overwrote the end of the `S` in "Send a message..." which prevented the cursor from moving up the screen whenever you backspaced through the end of the line. We removed it to fix the placeholder text issue, but then the bug crept back in.
|
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PR_kwDOJ0Z1Ps5bA4ML
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|
ubuntu cuda drivers
|
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| 2023-09-22T18:13:11
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| 2023-09-22T18:43:14
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CONTRIBUTOR
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Add automatic cuda driver install to our install script.
|
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|
problem with ollama serve
|
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### What is the issue?
C:\Users\allowdin>ollama serve
Error: listen tcp 127.0.0.1:11434: bind: Only one usage of each socket address (protocol/network address/port) is normally permitted.
### OS
Windows
### GPU
Other
### CPU
Intel
### Ollama version
0.3.13
|
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| 1,794,392,513
|
PR_kwDOJ0Z1Ps5U9rY3
| 58
|
return error in generate response
|
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| 2023-07-07T21:27:14
| 2023-07-10T21:35:18
| 2023-07-10T21:03:47
|
CONTRIBUTOR
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| 2,267,450,441
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I_kwDOJ0Z1Ps6HJoxJ
| 4,000
|
Error: invalid file magic when trying to import a custom gguf model to ollama instance
|
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| 2024-04-28T09:21:07
| 2024-05-01T20:30:09
| 2024-05-01T20:30:09
|
NONE
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### What is the issue?
i got this error
"E:\phi3-mini-128k-gguf\model>ollama create phi-3-mini-128k -f Modelfile
transferring model data
creating model layer
Error: invalid file magic"
here is the modelfile used
FROM ./Phi-3-mini-128k-instruct.IQ4_XS.gguf
PARAMETER num_ctx 65536
PARAMETER num_keep 4
PARAMETER stop <|user|>
PARAMETER stop <|assistant|>
PARAMETER stop <|system|>
PARAMETER stop <|end|>
PARAMETER stop <|endoftext|>
TEMPLATE """
{{ if .System }}<|system|>
{{ .System }}<|end|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}<|end|>
{{ end }}<|assistant|>
{{ .Response }}<|end|>
"""
### OS
Windows
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.1.31
|
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I_kwDOJ0Z1Ps6WJECv
| 6,747
|
ERROR: llama runner process has terminated: error loading modelvocabulary: _Map_base::at
|
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| 2024-09-11T08:19:06
| 2025-01-08T16:06:35
| 2024-09-11T18:38:05
|
NONE
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### What is the issue?
I tried the octopus v4 model using ollama, I have constrcted the model using Modelfile, but it can not work when I try to run it.
```
transferring model data 100%
using existing layer sha256:08fc24194c1805114ed7aefcc8e5cedf7fe1e23b587f1655502afb294263b948
using existing layer sha256:e49180b1ca46f2ea2a9d652ab44a1ab5dbd5d3f9ed3fd8e985a1122883d580d3
using existing layer sha256:90bf36950b9123ecc20aa969070a7b3d51bbb782100f0e47b2a2c5f3cf6a24da
writing manifest
success
(base) [root@ip-172-31-81-66 ollama_files]# ollama ls
NAME ID SIZE MODIFIED
octopus-v4-Q4_K_M:latest 099ce87bccef 2.4 GB 14 seconds ago
```
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama version is 0.3.6
|
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I_kwDOJ0Z1Ps6XHDE1
| 6,869
|
An operation on a socket could not be performed
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| 2024-09-19T03:42:13
| 2024-10-08T23:28:50
| 2024-10-08T23:28:50
|
NONE
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### What is the issue?
The error reported in the terminal: dial tcp [xxx]:443: bind: An operation on a socket could not be performed because the system lacked sufficient buffer space or because a queue was full.
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.10
|
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|
minor bug: ggml/llama.cpp's new Q4_0_4_8 quantized files don't import into ollama
|
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| 2024-08-01T18:45:11
| 2024-10-20T13:17:08
| 2024-08-11T11:02:02
|
NONE
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### What is the issue?
I built ollama on Ubuntu24.04, running in Windows11's WSL2 on my Surface 11 Pro to try and test Ollama with llama.cpp's Q4_0_4_8 acceleration.
Ollama+llama.cpp builds, imports my local llama-2 Q4_0, and runs it.
But when I try and import a local llama-2 Q4_0_4_8 model (which runs with llama.cpp), it gives an "Error: invalid file magic", apparently from its ggml.go module (at line 311 ?), which maybe does not seem to understand the new Q4_0_4_4 and Q4_0_4_8 formats.
llama.cpp recently introduced these formats to accelerate modern arm64 CPUs like the Snapdragon X, It also works on other newer ARM CPUs and brings an up to 2-3x speed improvement. Details see [llama.cpp PR#5780](https://github.com/ggerganov/llama.cpp/pull/5780), and there seems to be work done for x64.
P.S.:I tried this on Linux (Windows' WSL2), since building llama.cpp for Windows on ARM / Snapdragon X requires special build instructions (using clang instead of MSVC, details see [llama.cpp build instructions](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)), and I'm not sure if ollama already follows these.
@SebastianGode independently also had this issue.
### OS
Linux / Ubuntu 24.04 on WSL2, Windows on ARM
### GPU
None
### CPU
arm64 / Snapdragon X Plus
### Ollama version
0.3.2, 3e61426
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PR_kwDOJ0Z1Ps5tQuFX
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chore: use errors.New to replace fmt.Errorf with no parameters will much better
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PR_kwDOJ0Z1Ps6BHFv-
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runner.go: Check for zero length images
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If we get a request with a zero length image, it will result in an out-of-bounds error when we pass the data to the image encoder.
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I_kwDOJ0Z1Ps6XDc4n
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Unable to load adapter_model.safetensors for Phi3-Medium-128k
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### What is the issue?
Trying to load a safetensors adapter file for phi3-medium-128k using a .modelfile. I generated an adapter_config.json and adapter_model.safetensors files using lora training and copied them into the ollama docker container.
I have generated the modelfile with `ollama show phi3:medium --modelfile > phi3_med_cim.modelfile` and edited it to add
```
# Modelfile generated by "ollama show"
FROM phi3:medium
ADAPTER /home/medium_128k
TEMPLATE "{{ if .System }}<|system|>
...
```
When trying to create the model, I am calling `ollama create phi3_med_cim --file /home/phi3_med_cim.modelfile`, which throws the following error:
```transferring model data 100%
converting model
Error: unsupported architecture
```
These steps work fine for phi3-mini-4k and llama3.1, but not for phi3-medium
### OS
WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.10 (docker)
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PR_kwDOJ0Z1Ps53Yglf
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|
Added build support to RISC-V
|
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1. Build support to RISC-V
2. Fix https://github.com/chewxy/math32/issues/46
cc @mxyng @jmorganca
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I_kwDOJ0Z1Ps6Dai9k
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what should I do after pulling?
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I_kwDOJ0Z1Ps6H7vUO
| 4,198
|
Improving the efficiency of using multiple GPU cards.
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NONE
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Before v0.1.32, when loading a model, it would be evenly distributed across all GPU cards to improve the use of GPU cards. In v0.1.32 and v0.1.33, it was found that loading a model would automatically use one card.
When used by multiple users simultaneously, it is slower. If evenly distributed across multiple GPU cards, it can improve the utilization rate of GPU cards and improve efficiency.
Tesla T4 GPU list
-------------------------------------------------------------------
localhost.localdomain Mon May 6 18:41:30 2024 550.54.15
**[0] Tesla T4 | 54°C, 93 % | 12238 / 15360 MB | ollama(12236M)**
[1] Tesla T4 | 36°C, 0 % | 2 / 15360 MB |
[2] Tesla T4 | 30°C, 0 % | 2 / 15360 MB |
[3] Tesla T4 | 33°C, 0 % | 2 / 15360 MB |
ollama.service
-------------------------------------------------------------------
`[Unit]
Description=Ollama Service
After=network-online.target
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_ORIGINS='*'"
Environment="OLLAMA_MODELS=/ollama/ollama/models"
Environment="OLLAMA_KEEP_ALIVE=10m"
**Environment="OLLAMA_NUM_PARALLEL=4"**
Environment="OLLAMA_MAX_LOADED_MODELS=2"
Environment="CUDA_VISIBLE_DEVICES=0,1,2,3"
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/root/.local/bin:/root/bin:/usr/lib64/ccache:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin"
[Install]
WantedBy=default.target`
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I_kwDOJ0Z1Ps6CNEE-
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Switching between models is very slow?
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| 2024-03-13T16:44:07
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I noted that switching between models is very slow. Is it possible to load two different models in memory at the same time?
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I_kwDOJ0Z1Ps6libGi
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|
can't pull model from hf
|
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### What is the issue?
input: ollama run hf.co/bartowski/Qwen2.5-7B-Instruct-GGUF:F16
pulling manifest
pulling 863c978275bc... 100% ▕████████████████████████████████████▏ 15 GB
pulling e94a8ecb9327... 100% ▕████████████████████████████████████▏ 1.6 KB
pulling 9ae14bd2c052... 0% ▕ ▏ 0 B/ 193 B
Error: max retries exceeded: Get "https://huggingface.co/v2/bartowski/Qwen2.5-7B-Instruct-GGUF/blobs/sha256:9ae14bd2c0522aaeb6adf4b34396f056559a7c27854188ae32695f056d4015d9?__sign=eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTczNjQwNzM3OCwic3ViIjoiL2JhcnRvd3NraS9Rd2VuMi41LTdCLUluc3RydWN0LUdHVUYiLCJleHAiOjE3MzY0MDc5NzgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.P5a2zBON89Hv8KVRIQF0a5m0daM1sE0kF2MUedZNDA_n8ZNqsOBb4iY-n2a71WF4fN6GzRb3upR-TA0bHwcUCg": dial tcp [2a03:2880:f134:183:face:b00c:0:25de]:443: i/o timeout
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.5.4
|
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PR_kwDOJ0Z1Ps6EvbMZ
| 8,028
|
readme + examples: add pgai to readme projects and add example for semantic search
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| 2024-12-10T17:43:13
| 2024-12-16T09:02:28
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|
CONTRIBUTOR
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Hey there,
adding a mention of pgai to the community integrations section in the Readme as well as a small demo script for the example folder.
Let me know if you'd like to change anything!
|
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I_kwDOJ0Z1Ps54q2ay
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|
Add option to not stream output in terminal
|
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| 2023-12-04T18:47:31
| 2024-05-17T13:55:07
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I use a screen reader, and when Ollama is sending output, it pauses while it's generating text, so, it, sounds, a little, like, this. Not too big a deal, but it would make it sound a bit better to have it output all at once, when done generating, not in a stream.
Thanks!
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/api/embed returns empty embeddings in docker environment
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### What is the issue?
```
curl http://localhost:11434/api/embed -d '{
"model": "bge-m3",
"prompt": "Llamas are members of the camelid family"
}'
{"model":"bge-m3","embeddings":[]}%
```
but old /api/embedding endpoint still worked
### OS
Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.8
|
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I_kwDOJ0Z1Ps6K2HXQ
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Support for jina-embeddings-v2-base-zh
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https://huggingface.co/jinaai/jina-embeddings-v2-base-zh
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I_kwDOJ0Z1Ps58fSz-
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Cannot interrupt evaluation `ollama serve` during prompt evaluation
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When trying to interrupt either generation or `ollama serve` with ctrl+c while the prompt is being evaluated, Ollama will wait until prompt evaluation is done
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PR_kwDOJ0Z1Ps55XTpO
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openai: increase context window when max_tokens is provided
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Previously, `/v1/chat/completions` requests were limited to 2048 tokens. This PR extends the context length by setting `num_ctx` to `max_tokens` if it's larger than the default context window of 2048 tokens. It also includes a minor clean up for the OpenAI compatibility unit tests.
Note: this doesn't solve the case of having a large context window while limiting the number of tokens to a small number. This will be solved in a future change where `num_ctx` will be set automatically based on available VRAM and compute.
Fixes https://github.com/ollama/ollama/issues/6286 https://github.com/ollama/ollama/issues/5356
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Add windows radeon concurrency note
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I_kwDOJ0Z1Ps6iMMNZ
| 7,953
|
Ollama not using GPU (windows)
|
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| 2024-12-05T18:20:25
| 2024-12-05T19:47:15
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### What is the issue?
now i know there has been a lot other issues about this problem and most of them has been solved but i have not found an answer for my specific scenario, most of the cases has been on linux.
In other threads i seen people say its because they don't have enough vram that it automatically uses the cpu, but i have 24gb vram so I don't think that is the case
now when i run "nvidia-smi" it returns:
```
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
C:\Users\storm>nvidia-smi
Thu Dec 5 19:09:21 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 552.22 Driver Version: 552.22 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 WDDM | 00000000:05:00.0 On | Off |
| 0% 48C P5 48W / 450W | 2852MiB / 24564MiB | 42% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 2000 C+G ...m Files\Mozilla Firefox\firefox.exe N/A |
| 0 N/A N/A 2500 C+G ...ys\WinUI3Apps\PowerToys.Peek.UI.exe N/A |
| 0 N/A N/A 5380 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 6540 C+G ...werToys\PowerToys.ColorPickerUI.exe N/A |
| 0 N/A N/A 7452 C+G ...on\wallpaper_engine\wallpaper32.exe N/A |
| 0 N/A N/A 8420 C+G ...s\moments\SteelSeriesCaptureSvc.exe N/A |
| 0 N/A N/A 8568 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 10148 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
| 0 N/A N/A 10260 C+G ..._x64__cw5n1h2txyewy\WidgetBoard.exe N/A |
| 0 N/A N/A 11108 C+G ...paper_engine\bin\webwallpaper32.exe N/A |
| 0 N/A N/A 12212 C+G ...on\131.0.2903.70\msedgewebview2.exe N/A |
| 0 N/A N/A 13548 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 13828 C+G ...\PowerToys\PowerToys.FancyZones.exe N/A |
| 0 N/A N/A 14148 C+G ...UI3Apps\PowerToys.AdvancedPaste.exe N/A |
| 0 N/A N/A 14420 C+G ...werToys\PowerToys.PowerLauncher.exe N/A |
| 0 N/A N/A 14968 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 15936 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A |
| 0 N/A N/A 17756 C+G ...indows-x64\jre-legacy\bin\javaw.exe N/A |
| 0 N/A N/A 18968 C+G ...m Files\Mozilla Firefox\firefox.exe N/A |
| 0 N/A N/A 20156 C+G ...al\Discord\app-1.0.9173\Discord.exe N/A |
| 0 N/A N/A 20564 C+G ...pic Games\CrosshairX\CrosshairX.exe N/A |
| 0 N/A N/A 20992 C+G ...64__v826wp6bftszj\TranslucentTB.exe N/A |
| 0 N/A N/A 24412 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 24500 C+G ...on\131.0.2903.70\msedgewebview2.exe N/A |
| 0 N/A N/A 25788 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A |
+-----------------------------------------------------------------------------------------+
C:\Users\storm>
```
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.4.7
|
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PR_kwDOJ0Z1Ps5VkVEJ
| 82
|
windows build
|
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| 2023-07-15T00:31:56
| 2023-07-23T10:31:22
| 2023-07-15T03:11:55
|
CONTRIBUTOR
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make some minor changes so it builds on windows.
TODO:
moving the `.*.part` to the full file isn't working correctly but #78 will change how that works so temporary workaround is to copy the `.*.part` to the final name
|
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I_kwDOJ0Z1Ps6YEkRj
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|
Way to stop all running models
|
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| 2025-01-18T16:03:59
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NONE
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`ollama stop all` or `ollama stop *` etc
would be handy
|
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SIGSEGV: segmentation violation with 0.1.27 and starcoder2:!5b
|
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NONE
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Hello, I am having an issue while running the recently released `starcoder2` model. Please find below the details.
**How to reproduce the issue**
Running ollama in a container with Docker Compose
```
ollama:
image: ollama/ollama:0.1.27
container_name: ollama
restart: unless-stopped
networks:
- common
volumes:
- /home/ollama:/root/.ollama
ports:
- "11434:11434"
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
```
Pull and run the model
```
$ docker exec ollama ollama pull starcoder2:15b
pulling manifest
pulling dc5deb763c38... 100% ▕████████████████▏ 9.1 GB
pulling 4ec42cd966c9... 100% ▕████████████████▏ 12 KB
pulling 5671842f8d52... 100% ▕████████████████▏ 346 B
verifying sha256 digest
writing manifest
removing any unused layers
success
$ docker exec ollama ollama run starcoder2:15b
Error: Post "http://0.0.0.0:11434/api/generate": EOF
```
The container gets killed. Here are the logs
```
time=2024-03-03T18:20:36.119Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-03T18:20:36.119Z level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9"
time=2024-03-03T18:20:36.119Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-03T18:20:36.119Z level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9"
time=2024-03-03T18:20:36.119Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-03T18:20:36.123Z level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: /tmp/ollama3819308268/cuda_v11/libext_server.so"
time=2024-03-03T18:20:36.123Z level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: yes
ggml_init_cublas: CUDA_USE_TENSOR_CORES: no
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4080 SUPER, compute capability 8.9, VMM: yes
SIGSEGV: segmentation violation
PC=0x7fcb8138e7fd m=12 sigcode=1
signal arrived during cgo execution
goroutine 50 [syscall]:
runtime.cgocall(0x9bcdd0, 0xc0001446c8)
/usr/local/go/src/runtime/cgocall.go:157 +0x4b fp=0xc0001446a0 sp=0xc000144668 pc=0x409b0b
github.com/jmorganca/ollama/llm._Cfunc_dyn_llama_server_init({0x7fcb0c001e10, 0x7fcb05250250, 0x7fcb05240ca0, 0x7fcb05243f20, 0x7fcb05253c00, 0x7fcb0524d840, 0x7fcb05243ba0, 0x7fcb05240d20, 0x7fcb05254500, 0x7fcb052537a0, ...}, ...)
_cgo_gotypes.go:282 +0x45 fp=0xc0001446c8 sp=0xc0001446a0 pc=0x7c5485
github.com/jmorganca/ollama/llm.newDynExtServer.func7(0xaf20c4?, 0xc?)
/go/src/github.com/jmorganca/ollama/llm/dyn_ext_server.go:153 +0xef fp=0xc0001447b8 sp=0xc0001446c8 pc=0x7c69cf
github.com/jmorganca/ollama/llm.newDynExtServer({0xc00069e000, 0x2f}, {0xc000002150, _}, {_, _, _}, {0x0, 0x0, 0x0}, ...)
/go/src/github.com/jmorganca/ollama/llm/dyn_ext_server.go:153 +0xa65 fp=0xc000144a58 sp=0xc0001447b8 pc=0x7c6665
github.com/jmorganca/ollama/llm.newLlmServer({{_, _, _}, {_, _}, {_, _}}, {_, _}, {0xc000002150, ...}, ...)
/go/src/github.com/jmorganca/ollama/llm/llm.go:158 +0x425 fp=0xc000144c18 sp=0xc000144a58 pc=0x7c2dc5
github.com/jmorganca/ollama/llm.New({0xc000282a50, 0x15}, {0xc000002150, _}, {_, _, _}, {0x0, 0x0, 0x0}, ...)
/go/src/github.com/jmorganca/ollama/llm/llm.go:123 +0x713 fp=0xc000144e98 sp=0xc000144c18 pc=0x7c2733
github.com/jmorganca/ollama/server.load(0xc0000fe900?, 0xc0000fe900, {{0x0, 0x800, 0x200, 0x1, 0xffffffffffffffff, 0x0, 0x0, 0x1, ...}, ...}, ...)
/go/src/github.com/jmorganca/ollama/server/routes.go:85 +0x3a5 fp=0xc000145018 sp=0xc000144e98 pc=0x996945
github.com/jmorganca/ollama/server.ChatHandler(0xc000408600)
/go/src/github.com/jmorganca/ollama/server/routes.go:1173 +0xa37 fp=0xc000145748 sp=0xc000145018 pc=0x9a1f77
github.com/gin-gonic/gin.(*Context).Next(...)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/jmorganca/ollama/server.(*Server).GenerateRoutes.func1(0xc000408600)
/go/src/github.com/jmorganca/ollama/server/routes.go:943 +0x68 fp=0xc000145780 sp=0xc000145748 pc=0x9a07a8
github.com/gin-gonic/gin.(*Context).Next(...)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.CustomRecoveryWithWriter.func1(0xc000408600)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/recovery.go:102 +0x7a fp=0xc0001457d0 sp=0xc000145780 pc=0x97803a
github.com/gin-gonic/gin.(*Context).Next(...)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.LoggerWithConfig.func1(0xc000408600)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/logger.go:240 +0xde fp=0xc000145980 sp=0xc0001457d0 pc=0x9771de
github.com/gin-gonic/gin.(*Context).Next(...)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.(*Engine).handleHTTPRequest(0xc0003f64e0, 0xc000408600)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:620 +0x65b fp=0xc000145b08 sp=0xc000145980 pc=0x97629b
github.com/gin-gonic/gin.(*Engine).ServeHTTP(0xc0003f64e0, {0x11403a20?, 0xc00011c0e0}, 0xc000408500)
/root/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:576 +0x1dd fp=0xc000145b48 sp=0xc000145b08 pc=0x975a5d
net/http.serverHandler.ServeHTTP({0x11401d40?}, {0x11403a20?, 0xc00011c0e0?}, 0x6?)
/usr/local/go/src/net/http/server.go:2938 +0x8e fp=0xc000145b78 sp=0xc000145b48 pc=0x6ced4e
net/http.(*conn).serve(0xc0000fc1b0, {0x11405088, 0xc0001542a0})
/usr/local/go/src/net/http/server.go:2009 +0x5f4 fp=0xc000145fb8 sp=0xc000145b78 pc=0x6cac34
net/http.(*Server).Serve.func3()
/usr/local/go/src/net/http/server.go:3086 +0x28 fp=0xc000145fe0 sp=0xc000145fb8 pc=0x6cf568
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000145fe8 sp=0xc000145fe0 pc=0x46e2c1
created by net/http.(*Server).Serve in goroutine 1
/usr/local/go/src/net/http/server.go:3086 +0x5cb
goroutine 1 [IO wait]:
runtime.gopark(0x480f10?, 0xc0000e3850?, 0xa0?, 0x38?, 0x4f711d?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc0000e3830 sp=0xc0000e3810 pc=0x43e7ee
runtime.netpollblock(0x0?, 0x4092a6?, 0x0?)
/usr/local/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc0000e3868 sp=0xc0000e3830 pc=0x437277
internal/poll.runtime_pollWait(0x7fcb38dfde28, 0x72)
/usr/local/go/src/runtime/netpoll.go:343 +0x85 fp=0xc0000e3888 sp=0xc0000e3868 pc=0x468a05
internal/poll.(*pollDesc).wait(0xc00040a000?, 0x11846a68?, 0x0)
/usr/local/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0000e38b0 sp=0xc0000e3888 pc=0x4efd67
internal/poll.(*pollDesc).waitRead(...)
/usr/local/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Accept(0xc00040a000)
/usr/local/go/src/internal/poll/fd_unix.go:611 +0x2ac fp=0xc0000e3958 sp=0xc0000e38b0 pc=0x4f524c
net.(*netFD).accept(0xc00040a000)
/usr/local/go/src/net/fd_unix.go:172 +0x29 fp=0xc0000e3a10 sp=0xc0000e3958 pc=0x56be29
net.(*TCPListener).accept(0xc0003e1560)
/usr/local/go/src/net/tcpsock_posix.go:152 +0x1e fp=0xc0000e3a38 sp=0xc0000e3a10 pc=0x580c3e
net.(*TCPListener).Accept(0xc0003e1560)
/usr/local/go/src/net/tcpsock.go:315 +0x30 fp=0xc0000e3a68 sp=0xc0000e3a38 pc=0x57fdf0
net/http.(*onceCloseListener).Accept(0xc0000fc1b0?)
<autogenerated>:1 +0x24 fp=0xc0000e3a80 sp=0xc0000e3a68 pc=0x6f1ae4
net/http.(*Server).Serve(0xc000318ff0, {0x11403810, 0xc0003e1560})
/usr/local/go/src/net/http/server.go:3056 +0x364 fp=0xc0000e3bb0 sp=0xc0000e3a80 pc=0x6cf1a4
github.com/jmorganca/ollama/server.Serve({0x11403810, 0xc0003e1560})
/go/src/github.com/jmorganca/ollama/server/routes.go:1046 +0x454 fp=0xc0000e3c98 sp=0xc0000e3bb0 pc=0x9a0c54
github.com/jmorganca/ollama/cmd.RunServer(0xc000408300?, {0x1184c8c0?, 0x4?, 0xad9d6a?})
/go/src/github.com/jmorganca/ollama/cmd/cmd.go:706 +0x1b9 fp=0xc0000e3d30 sp=0xc0000e3c98 pc=0x9b3d99
github.com/spf13/cobra.(*Command).execute(0xc0003a1800, {0x1184c8c0, 0x0, 0x0})
/root/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:940 +0x87c fp=0xc0000e3e68 sp=0xc0000e3d30 pc=0x764d9c
github.com/spf13/cobra.(*Command).ExecuteC(0xc0003a0c00)
/root/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:1068 +0x3a5 fp=0xc0000e3f20 sp=0xc0000e3e68 pc=0x7655c5
github.com/spf13/cobra.(*Command).Execute(...)
/root/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:992
github.com/spf13/cobra.(*Command).ExecuteContext(...)
/root/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:985
main.main()
/go/src/github.com/jmorganca/ollama/main.go:11 +0x4d fp=0xc0000e3f40 sp=0xc0000e3f20 pc=0x9bbeed
runtime.main()
/usr/local/go/src/runtime/proc.go:267 +0x2bb fp=0xc0000e3fe0 sp=0xc0000e3f40 pc=0x43e39b
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000e3fe8 sp=0xc0000e3fe0 pc=0x46e2c1
goroutine 2 [force gc (idle), 2 minutes]:
runtime.gopark(0x16e8d765f29c?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000046fa8 sp=0xc000046f88 pc=0x43e7ee
runtime.goparkunlock(...)
/usr/local/go/src/runtime/proc.go:404
runtime.forcegchelper()
/usr/local/go/src/runtime/proc.go:322 +0xb3 fp=0xc000046fe0 sp=0xc000046fa8 pc=0x43e673
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000046fe8 sp=0xc000046fe0 pc=0x46e2c1
created by runtime.init.6 in goroutine 1
/usr/local/go/src/runtime/proc.go:310 +0x1a
goroutine 3 [GC sweep wait]:
runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000047778 sp=0xc000047758 pc=0x43e7ee
runtime.goparkunlock(...)
/usr/local/go/src/runtime/proc.go:404
runtime.bgsweep(0x0?)
/usr/local/go/src/runtime/mgcsweep.go:321 +0xdf fp=0xc0000477c8 sp=0xc000047778 pc=0x42a73f
runtime.gcenable.func1()
/usr/local/go/src/runtime/mgc.go:200 +0x25 fp=0xc0000477e0 sp=0xc0000477c8 pc=0x41f865
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000477e8 sp=0xc0000477e0 pc=0x46e2c1
created by runtime.gcenable in goroutine 1
/usr/local/go/src/runtime/mgc.go:200 +0x66
goroutine 4 [GC scavenge wait]:
runtime.gopark(0xe910d?, 0x16176886?, 0x0?, 0x0?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000047f70 sp=0xc000047f50 pc=0x43e7ee
runtime.goparkunlock(...)
/usr/local/go/src/runtime/proc.go:404
runtime.(*scavengerState).park(0x1181cc40)
/usr/local/go/src/runtime/mgcscavenge.go:425 +0x49 fp=0xc000047fa0 sp=0xc000047f70 pc=0x427f69
runtime.bgscavenge(0x0?)
/usr/local/go/src/runtime/mgcscavenge.go:658 +0x59 fp=0xc000047fc8 sp=0xc000047fa0 pc=0x428519
runtime.gcenable.func2()
/usr/local/go/src/runtime/mgc.go:201 +0x25 fp=0xc000047fe0 sp=0xc000047fc8 pc=0x41f805
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000047fe8 sp=0xc000047fe0 pc=0x46e2c1
created by runtime.gcenable in goroutine 1
/usr/local/go/src/runtime/mgc.go:201 +0xa5
goroutine 18 [finalizer wait, 6 minutes]:
runtime.gopark(0xad2d20?, 0x10043f901?, 0x0?, 0x0?, 0x4469a5?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000046628 sp=0xc000046608 pc=0x43e7ee
runtime.runfinq()
/usr/local/go/src/runtime/mfinal.go:193 +0x107 fp=0xc0000467e0 sp=0xc000046628 pc=0x41e8e7
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000467e8 sp=0xc0000467e0 pc=0x46e2c1
created by runtime.createfing in goroutine 1
/usr/local/go/src/runtime/mfinal.go:163 +0x3d
goroutine 19 [select, 6 minutes, locked to thread]:
runtime.gopark(0xc0000427a8?, 0x2?, 0x89?, 0xea?, 0xc0000427a4?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000042638 sp=0xc000042618 pc=0x43e7ee
runtime.selectgo(0xc0000427a8, 0xc0000427a0, 0x0?, 0x0, 0x0?, 0x1)
/usr/local/go/src/runtime/select.go:327 +0x725 fp=0xc000042758 sp=0xc000042638 pc=0x44e325
runtime.ensureSigM.func1()
/usr/local/go/src/runtime/signal_unix.go:1014 +0x19f fp=0xc0000427e0 sp=0xc000042758 pc=0x46535f
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000427e8 sp=0xc0000427e0 pc=0x46e2c1
created by runtime.ensureSigM in goroutine 1
/usr/local/go/src/runtime/signal_unix.go:997 +0xc8
goroutine 5 [syscall, 6 minutes]:
runtime.notetsleepg(0x0?, 0x0?)
/usr/local/go/src/runtime/lock_futex.go:236 +0x29 fp=0xc0000487a0 sp=0xc000048768 pc=0x411349
os/signal.signal_recv()
/usr/local/go/src/runtime/sigqueue.go:152 +0x29 fp=0xc0000487c0 sp=0xc0000487a0 pc=0x46ac89
os/signal.loop()
/usr/local/go/src/os/signal/signal_unix.go:23 +0x13 fp=0xc0000487e0 sp=0xc0000487c0 pc=0x6f4513
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000487e8 sp=0xc0000487e0 pc=0x46e2c1
created by os/signal.Notify.func1.1 in goroutine 1
/usr/local/go/src/os/signal/signal.go:151 +0x1f
goroutine 20 [chan receive, 6 minutes]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000042f18 sp=0xc000042ef8 pc=0x43e7ee
runtime.chanrecv(0xc000436120, 0x0, 0x1)
/usr/local/go/src/runtime/chan.go:583 +0x3cd fp=0xc000042f90 sp=0xc000042f18 pc=0x40beed
runtime.chanrecv1(0x0?, 0x0?)
/usr/local/go/src/runtime/chan.go:442 +0x12 fp=0xc000042fb8 sp=0xc000042f90 pc=0x40baf2
github.com/jmorganca/ollama/server.Serve.func2()
/go/src/github.com/jmorganca/ollama/server/routes.go:1028 +0x25 fp=0xc000042fe0 sp=0xc000042fb8 pc=0x9a0ce5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000042fe8 sp=0xc000042fe0 pc=0x46e2c1
created by github.com/jmorganca/ollama/server.Serve in goroutine 1
/go/src/github.com/jmorganca/ollama/server/routes.go:1027 +0x3c7
goroutine 30 [GC worker (idle)]:
runtime.gopark(0x16f2d48eda1f?, 0x3?, 0x9d?, 0x72?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc00043d750 sp=0xc00043d730 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc00043d7e0 sp=0xc00043d750 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00043d7e8 sp=0xc00043d7e0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 31 [GC worker (idle)]:
runtime.gopark(0x16f2d6231cd4?, 0x3?, 0xf7?, 0x20?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc00043df50 sp=0xc00043df30 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc00043dfe0 sp=0xc00043df50 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00043dfe8 sp=0xc00043dfe0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 32 [GC worker (idle)]:
runtime.gopark(0x16f2d6231cb1?, 0x3?, 0x7?, 0xb?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc00043e750 sp=0xc00043e730 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc00043e7e0 sp=0xc00043e750 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00043e7e8 sp=0xc00043e7e0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 34 [GC worker (idle)]:
runtime.gopark(0x16f2d6b56164?, 0x2?, 0x55?, 0x12?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000438750 sp=0xc000438730 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc0004387e0 sp=0xc000438750 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0004387e8 sp=0xc0004387e0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 33 [GC worker (idle)]:
runtime.gopark(0x16f2d6b56114?, 0x2?, 0x17?, 0x8f?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc00043ef50 sp=0xc00043ef30 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc00043efe0 sp=0xc00043ef50 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00043efe8 sp=0xc00043efe0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 35 [GC worker (idle)]:
runtime.gopark(0x16f2d6b5645a?, 0x3?, 0xc1?, 0xd?, 0x0?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc000438f50 sp=0xc000438f30 pc=0x43e7ee
runtime.gcBgMarkWorker()
/usr/local/go/src/runtime/mgc.go:1293 +0xe5 fp=0xc000438fe0 sp=0xc000438f50 pc=0x4213e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000438fe8 sp=0xc000438fe0 pc=0x46e2c1
created by runtime.gcBgMarkStartWorkers in goroutine 29
/usr/local/go/src/runtime/mgc.go:1217 +0x1c
goroutine 51 [IO wait]:
runtime.gopark(0x11410780?, 0xb?, 0x0?, 0x0?, 0xc?)
/usr/local/go/src/runtime/proc.go:398 +0xce fp=0xc00043cda0 sp=0xc00043cd80 pc=0x43e7ee
runtime.netpollblock(0x47f078?, 0x4092a6?, 0x0?)
/usr/local/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc00043cdd8 sp=0xc00043cda0 pc=0x437277
internal/poll.runtime_pollWait(0x7fcb38dfdd30, 0x72)
/usr/local/go/src/runtime/netpoll.go:343 +0x85 fp=0xc00043cdf8 sp=0xc00043cdd8 pc=0x468a05
internal/poll.(*pollDesc).wait(0xc000126000?, 0xc000154521?, 0x0)
/usr/local/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00043ce20 sp=0xc00043cdf8 pc=0x4efd67
internal/poll.(*pollDesc).waitRead(...)
/usr/local/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc000126000, {0xc000154521, 0x1, 0x1})
/usr/local/go/src/internal/poll/fd_unix.go:164 +0x27a fp=0xc00043ceb8 sp=0xc00043ce20 pc=0x4f105a
net.(*netFD).Read(0xc000126000, {0xc000154521?, 0x0?, 0xc000282a50?})
/usr/local/go/src/net/fd_posix.go:55 +0x25 fp=0xc00043cf00 sp=0xc00043ceb8 pc=0x569e05
net.(*conn).Read(0xc0004aa008, {0xc000154521?, 0x0?, 0xc00043cee0?})
/usr/local/go/src/net/net.go:179 +0x45 fp=0xc00043cf48 sp=0xc00043cf00 pc=0x5780a5
net.(*TCPConn).Read(0x0?, {0xc000154521?, 0x100000000000000?, 0xc0004342c0?})
<autogenerated>:1 +0x25 fp=0xc00043cf78 sp=0xc00043cf48 pc=0x589fa5
net/http.(*connReader).backgroundRead(0xc000154510)
/usr/local/go/src/net/http/server.go:683 +0x37 fp=0xc00043cfc8 sp=0xc00043cf78 pc=0x6c4ab7
net/http.(*connReader).startBackgroundRead.func2()
/usr/local/go/src/net/http/server.go:679 +0x25 fp=0xc00043cfe0 sp=0xc00043cfc8 pc=0x6c49e5
runtime.goexit()
/usr/local/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00043cfe8 sp=0xc00043cfe0 pc=0x46e2c1
created by net/http.(*connReader).startBackgroundRead in goroutine 50
/usr/local/go/src/net/http/server.go:679 +0xba
rax 0x0
rbx 0x7fcb28ff8430
rcx 0x67
rdx 0x0
rdi 0x0
rsi 0x0
rbp 0x7fcb28ff83f0
rsp 0x7fcb28ff8258
r8 0x7fca482cfb00
r9 0x7fca482cfb28
r10 0x7fcb81200b40
r11 0x7fcb8138aa80
r12 0x0
r13 0x7fcb0c2026a8
r14 0x7fcb0c2044a0
r15 0x0
rip 0x7fcb8138e7fd
rflags 0x10283
cs 0x33
fs 0x0
gs 0x0
```
Thanks!
|
{
"login": "jmorganca",
"id": 251292,
"node_id": "MDQ6VXNlcjI1MTI5Mg==",
"avatar_url": "https://avatars.githubusercontent.com/u/251292?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jmorganca",
"html_url": "https://github.com/jmorganca",
"followers_url": "https://api.github.com/users/jmorganca/followers",
"following_url": "https://api.github.com/users/jmorganca/following{/other_user}",
"gists_url": "https://api.github.com/users/jmorganca/gists{/gist_id}",
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I_kwDOJ0Z1Ps6M830M
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|
AMD Ryzen NPU support
|
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| 2024-12-26T09:30:59
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NONE
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Hello! I'm want to buy Lenovo Xiaoxin 14 AI laptop on AMD Ryzen 7 8845H on my birthday and I will install Artix Linux to this. Do you will to add AMD Ryzen NPU support to Ollama on Linux and Windows? If anything, AMD Ryzen NPU driver for Linux is already available on Github:
https://github.com/amd/xdna-driver.git
Sorry for my bad English, please!
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I_kwDOJ0Z1Ps6TeNAi
| 6,426
|
convert: vocab conversion incorrect
|
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| 2024-08-19T20:40:50
| 2024-08-21T18:48:44
| 2024-08-21T18:48:44
|
MEMBER
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### What is the issue?
To reproduce, clone https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct then create a `Modelfile`:
```
FROM .
```
Lastly, create a model:
```
ollama create test
```
When trying to run the model, an assertion will be fired:
```
Error: llama runner process has terminated: GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size()) failed
```
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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I_kwDOJ0Z1Ps5tkvD7
| 300
|
Provide a way to allow connections to Ollama from web browser origins other than `localhost` and `0.0.0.0`
|
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| 2023-08-06T19:41:30
| 2025-01-29T14:27:00
| 2023-08-08T14:41:44
|
MEMBER
|
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Currently, Ollama has CORS rules that allow pages hosted on `localhost` to connect to `localhost:11434`. #282 adds support for `0.0.0.0`, but some hosted web pages want to leverage a local running Ollama.
Simply opening up CORS to all origins wouldn't be secure: any website could call the API by simply browsing to it. However, we should consider adding a way for users to "approve" of an origin using their local Ollama instance, similar to [deep links](https://developer.apple.com/documentation/xcode/allowing-apps-and-websites-to-link-to-your-content) on iOS and macOS
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| 1,969,295,567
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PR_kwDOJ0Z1Ps5eK6GK
| 953
|
Changed name of folder and added some more features
|
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| 2023-10-30T22:02:52
| 2024-11-21T08:48:20
| 2024-11-21T08:48:20
|
NONE
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|
I added the model chooser
|
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| 2,454,734,578
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I_kwDOJ0Z1Ps6SUEby
| 6,251
|
Ollama multiuser scale
|
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NONE
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I'm looking for some scale numbers on what ollama supports as far as multi-user environments go. I see the OLLAMA_NUM_PARALLEL for adjusting how many simultaneous requests can be served as well as OLLAMA_MAX_QUEUE for how many requests can be queued before being rejected but nothing that will help me understand how that directly relates to how to design a system that will serve a large number of users and how much GPU resources will be required to do so. Is Ollama a fit for large scale environments where there might be a very large number of users interacting with it without having to front end an endless number of Ollama instances in front of a load balancer VIP? Has anyone done some scale testing to help design larger scale designs using Ollama or is Ollama still mostly fitting solely into the desktop use case? Will containers help here or is it strictly an underlying GPU/memory issue? The cost for the servers underneath are not an issue for us. Just need scale. Please advise.
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I_kwDOJ0Z1Ps6U4wAe
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TensorRT Support
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| 2025-01-25T18:22:12
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Does ollama leverage TensorRT and if not, can support for it be added?
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PR_kwDOJ0Z1Ps6B-Xjq
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Vendor bump llama.cpp
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| 2024-11-14T21:21:05
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Update to 4a8ccb37ad9c9027cbcfd5548c19cdffe48d5197
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I_kwDOJ0Z1Ps6XKAGs
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Why models don't use full CPU power?
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I have noticed in CPU only use cases the models are not using the CPU to the full potential. Are there any way to make the utilize the full power?
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PR_kwDOJ0Z1Ps50fNZD
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Use slot with cached prompt instead of least recently used
|
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This chooses the slot with the longest common prompt prefix instead of selecting the least recently used slot – this maximizes cache time for a single "conversation".
Future improvements:
- [ ] Clone slots and their cache
- [ ] Avoid requests "stealing" slots from each other because they have a small but common prefix
- [ ] Account for context shifts in the cache matching
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