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| 2,759,878,106
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I_kwDOJ0Z1Ps6kgGXa
| 8,250
|
qwen qvq model
|
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CONTRIBUTOR
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Extremely good with reasoning and maths mainly better than openai o1
https://huggingface.co/Qwen/QVQ-72B-Preview
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I_kwDOJ0Z1Ps6OjeZZ
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unable to load nvcuda
|
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NONE
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### What is the issue?
my ollama does not run on the NVIDIA gpu and i use the debug mode and find this message
"time=2024-07-04T17:13:20.134+08:00 level=DEBUG source=gpu.go:385 msg="Unable to load nvcuda" library=/usr/lib/libcuda.so.418.74 error="Unable to load /usr/lib/libcuda.so.418.74 library to query for Nvidia GPUs: /usr/lib/libcuda.so.418.74: wrong ELF class: ELFCLASS32"
dlerr: /usr/lib64/libcuda.so.418.74: undefined symbol: cuCtxCreate_v3
time=2024-07-04T17:13:20.135+08:00 level=DEBUG source=gpu.go:385 msg="Unable to load nvcuda" library=/usr/lib64/libcuda.so.418.74 error="symbol lookup for cuCtxCreate_v3 failed: /usr/lib64/libcuda.so.418.74: undefined symbol: cuCtxCreate_v3"
time=2024-07-04T17:13:20.135+08:00 level=DEBUG source=gpu.go:304 msg="Searching for GPU library" name=libcudart.so*"
could you please clarify what this means and how can i resolving this issue?
[debug-ollama.txt](https://github.com/user-attachments/files/16104245/debug-ollama.txt)
[log20240704.txt](https://github.com/user-attachments/files/16104248/log20240704.txt)
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.40
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I_kwDOJ0Z1Ps6SUTpJ
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cross compiling issue
|
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### What is the issue?
**When cross platform cross compiling, for example, when I compile a Linux version of a program on a Mac, the following error is reported：**
`gpu/amd_linux.go:200:19: undefined: RocmComputeMin
gpu/amd_linux.go:273:20: undefined: IGPUMemLimit
gpu/amd_linux.go:295:20: undefined: rocmMinimumMemory`
<img width="739" alt="1111" src="https://github.com/user-attachments/assets/7525b048-97fc-4e75-8731-695f8643ee6b">
**My execution steps are：**
`export GOOS=linux`
`export GOARCH=amd64`
`go generate ./...`
`go build`
**### OS**
macOS
**### GPU**
Apple
**### CPU**
Apple
**### Ollama version**
the latest master branch
|
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PR_kwDOJ0Z1Ps52pC1G
| 6,014
|
server: add OLLAMA_RUNNERS_DIR to help description
|
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| 2024-07-27T09:55:05
| 2024-11-23T19:31:15
| 2024-11-23T19:31:15
|
NONE
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The env OLLAMA_RUNNERS_DIR is useful to avoid extract embedded files every time at `ollama serve` start.
|
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PR_kwDOJ0Z1Ps6AtjcF
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|
Fix: return direct URL when OCI registry is not redirecting
|
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When debugging why I couldn't use a regular OCI registry (See screenshot below), I found out the ollama server was doing a redirect to AWS. This change returns the direct URL when such redirect does not happen, while assuring regular behavior when it does, so that regular pulls still work!
<img width="1710" alt="SCR-20241102-rgnu" src="https://github.com/user-attachments/assets/26c97b1e-f0d6-4b9f-82ac-a33e14f64fb3">
This PR fixes https://github.com/ollama/ollama/issues/7244
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| 2,249,256,817
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PR_kwDOJ0Z1Ps5s-ZFw
| 3,712
|
add stablelm graph calculation
|
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6CK1xJ
| 3,102
|
Response_format not supported
|
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| 2024-03-13T12:25:32
| 2024-03-13T15:01:10
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NONE
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When sending this request to the open AI endpoint I don't get the requested JSON. The payload is the actual content send to the api. Exactly the same content works with for example the fireworks api.
ℹ INFO:
EndPoint: http://localhost:11434/api/chat
Payload:
{"format":"json","messages":[{"content":"What is the minimal age for a neonate 28 weeks to 32 weeks corrected gestational age. Reply just in one JSON.","role":"user"}],"model":"llama2","options":{"num_keep":null,"seed":101,"num_predict":null,"top_k":null,"top_p":null,"tfs_z":null,"typical_p":null,"repeat_last_n":64,"temperature":0.0,"repeat_penalty":null,"presence_penalty":null,"frequency_penalty":null,"mirostat":0,"mirostat_tau":null,"mirostat_eta":null,"penalize_newline":null,"stop":[],"numa":null,"num_ctx":2048,"num_batch":null,"num_gqa":null,"num_gpu":null,"main_gpu":null,"low_vram":null,"f16_kv":null,"vocab_only":null,"use_mmap":null,"use_mlock":null,"rope_frequency_base":null,"rope_frequency_scale":null,"num_thread":null},"response_format":{"schema":{
"$schema": "http://json-schema.org/draft-04/schema#",
"title": "<>f__AnonymousType3408661278OfIntegerAndString",
"type": "object",
"additionalProperties": false,
"properties": {
"number": {
"type": "integer",
"format": "int32"
},
"unit": {
"type": [
"null",
"string"
]
}
}
},"type":"json_object"},"stream":false}
val it: Result<Response<Ollama.OllamaResponse>,string> =
Ok
{ Original =
"{"model":"llama2","created_at":"2024-03-13T12:22:59.29158Z","message":{"role":"assistant","content":"{\n\"minimal age\": 4000,\n\"units\": \"days\"\n}\n\n\n\n"},"done":true,"total_duration":809391791,"load_duration":2388625,"prompt_eval_count":50,"prompt_eval_duration":341193000,"eval_count":28,"eval_duration":464799000}"
Response =
{ error = null
model = "llama2"
created_at = "2024-03-13T12:22:59.29158Z"
response = null
message = { content = "{
"minimal age": 4000,
"units": "days"
}
"
role = "assistant" }
done = true
context = null
total_duration = 809391791L
load_duration = 2388625L
prompt_eval_duration = 341193000L
eval_count = 28
eval_duration = 464799000L } }
The workaround mentioned in #2844 I don't get to work
|
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I_kwDOJ0Z1Ps53qUev
| 1,253
|
Error when downloading and running any dataset of any size.
|
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| 2023-11-23T06:51:02
| 2023-11-24T01:11:28
| 2023-11-23T17:27:07
|
NONE
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This is the error I get after d/l a dataset and when trying to run a dataset - "Error: llama runner process has terminated"
It pulls them down, verifies the hash, then says "success", the very next line is the error above.
I am running Xubuntu 22.04, 16GB RAM, Intel Pentium CPU G4560 @ 3.50GHz, 8x Nvidia 1080Ti GPUs. I get this with even small sets like the 1.8GB starcoder set.
After a reboot, trying to run a dataset with "ollama run <dataset-name>" results in several seconds of attempting to start. The "ollama serve" process is visible in task manager, then the error "Error: llama runner process has terminated" is displayed in the terminal. The "ollama serve" process remains running/hung in task manager, consuming roughly 400MB of RAM. Which is the amount it was consuming while the terminal process was trying to run the dataset. Manually killing the process and trying to run it again results in the exact same behavior as after a reboot, with the exception that is fails to the error within a second or so tops instead of taking several seconds to fail like it did after the reboot. It still is consuming ~400MB of RAM.
This looks similar to issue #788; but the newer version is supposed to prevent the AVX CPU requirement from causing this issue. However, I still have this issue.
results of "journalctl -u ollama"
Nov 22 23:07:12 <machine-name> systemd[1]: Started Ollama Service.
Nov 22 23:07:14 <machine-name> ollama[1572]: 2023/11/22 23:07:14 images.go:779: total blobs: 0
Nov 22 23:07:14 <machine-name> ollama[1572]: 2023/11/22 23:07:14 images.go:786: total unused blobs removed: 0
Nov 22 23:07:14 <machine-name> ollama[1572]: 2023/11/22 23:07:14 routes.go:777: Listening on 127.0.0.1:11434 (version 0.1.11)
Nov 22 23:15:09 <machine-name> systemd[1]: Stopping Ollama Service...
Nov 22 23:15:09 <machine-name> systemd[1]: ollama.service: Deactivated successfully.
Nov 22 23:15:09 <machine-name> systemd[1]: Stopped Ollama Service.
Nov 22 23:15:09 <machine-name> systemd[1]: Started Ollama Service.
Nov 22 23:15:09 <machine-name> ollama[30889]: 2023/11/22 23:15:09 images.go:779: total blobs: 0
Nov 22 23:15:09 <machine-name> ollama[30889]: 2023/11/22 23:15:09 images.go:786: total unused blobs removed: 0
Nov 22 23:15:09 <machine-name> ollama[30889]: 2023/11/22 23:15:09 routes.go:777: Listening on 127.0.0.1:11434 (version 0.1.11)
Nov 22 23:16:17 <machine-name> ollama[30889]: [GIN] 2023/11/22 - 23:16:17 | 200 | 93.552µs | 127.0.0.1 | HEAD "/"
Nov 22 23:16:17 <machine-name> ollama[30889]: [GIN] 2023/11/22 - 23:16:17 | 404 | 173.127µs | 127.0.0.1 | POST "/api/show"
Nov 22 23:16:19 <machine-name> ollama[30889]: 2023/11/22 23:16:19 download.go:123: downloading 6ae280299950 in 42 100 MB part(s)
Nov 22 23:17:18 <machine-name> ollama[30889]: 2023/11/22 23:17:18 download.go:123: downloading 22e1b2e8dc2f in 1 43 B part(s)
Nov 22 23:17:21 <machine-name> ollama[30889]: 2023/11/22 23:17:21 download.go:123: downloading e35ab70a78c7 in 1 90 B part(s)
Nov 22 23:17:24 <machine-name> ollama[30889]: 2023/11/22 23:17:24 download.go:123: downloading 1cb90d66f4d4 in 1 381 B part(s)
Nov 22 23:17:47 <machine-name> ollama[30889]: [GIN] 2023/11/22 - 23:17:47 | 200 | 1m30s | 127.0.0.1 | POST "/api/pull"
Nov 22 23:17:47 <machine-name> ollama[30889]: 2023/11/22 23:17:47 llama.go:291: 89320 MB VRAM available, loading up to 546 GPU layers
Nov 22 23:17:47 <machine-name> ollama[30889]: 2023/11/22 23:17:47 llama.go:420: starting llama runner
Nov 22 23:17:47 <machine-name> ollama[30889]: 2023/11/22 23:17:47 llama.go:478: waiting for llama runner to start responding
Nov 22 23:17:48 <machine-name> ollama[30889]: 2023/11/22 23:17:48 llama.go:435: signal: illegal instruction (core dumped)
Nov 22 23:17:48 <machine-name> ollama[30889]: 2023/11/22 23:17:48 llama.go:443: error starting llama runner: llama runner process has terminated
Nov 22 23:17:48 <machine-name> ollama[30889]: 2023/11/22 23:17:48 llama.go:509: llama runner stopped successfully
Nov 22 23:17:48 <machine-name> ollama[30889]: 2023/11/22 23:17:48 llama.go:420: starting llama runner
Nov 22 23:17:48 <machine-name> ollama[30889]: 2023/11/22 23:17:48 llama.go:478: waiting for llama runner to start responding
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PR_kwDOJ0Z1Ps6JduvO
| 8,688
|
Add library in Zig.
|
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| 2025-01-30T08:05:43
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I_kwDOJ0Z1Ps6Tiboi
| 6,440
|
Model architecture Gemma2ForCausalLm
|
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### What is the issue?
Good afternoon, I would like to start marking I am not 100% sure whether this is an issue or maybe I am misunderstanding the concept of architecture.
I tried to create a model on ollama by using a Modelfile at version 0.3.0.
I go the previous error, so I decided to upgrade ollama to version 0.3.6
Which pops off error "unsupported architecture":
Configuration file of the model I have finetuned:
```json
{
"_name_or_path": "google/gemma-2-9b",
"architectures": [
"Gemma2ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": 50.0,
"bos_token_id": 2,
"cache_implementation": "hybrid",
"eos_token_id": 1,
"final_logit_softcapping": 30.0,
"head_dim": 256,
"hidden_act": "gelu_pytorch_tanh",
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 8192,
"model_type": "gemma2",
"num_attention_heads": 16,
"num_hidden_layers": 42,
"num_key_value_heads": 8,
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"sliding_window": 4096,
"sliding_window_size": 4096,
"torch_dtype": "float16",
"transformers_version": "4.43.3",
"use_cache": false,
"vocab_size": 256000
}
```
It's a model based on Gemma2 9b. Gemma2 works smoothly.
Modelfile:
```Dockerfile
FROM /path/model/
TEMPLATE """<start_of_turn>user/
{{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>"""
PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"
SYSTEM """Below is an instruction that describes a task. Write a response that appropriately completes the request.
Generate a concise summary in Spanish the following input. Here is the input"""
```
I don't understand by Gemma2 is supported but models based on it are not.
Thank you for reaching this point. Please if you have any clue about what's the issue please write it down bellow.
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.3.6
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PR_kwDOJ0Z1Ps6FbSk2
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|
darwin: restore multiple runners for x86
|
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In 0.5.2 we simplified packaging to have avx only for macos x86. It looks like there may still be some non-AVX systems out there, so this puts back the prior logic of building no-AVX for the primary binary, and now 2 runners for avx and avx2. These will be packaged in the App bundle only, so the stand-alone binary will now be without AVX support on macos. On arm, we'll also see these runners reported as available in the log, but they're dormant and will never be used at runtime.
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I_kwDOJ0Z1Ps53ZQTa
| 1,214
|
Cache models for system restarts to not download again in docker
|
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NONE
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I wrote a docker compose file and thought I mapped the right cache folder.
However after a system restart, the model is downloaded again.
My goal is to map the model cache dir to my local disk so when using the same model after a restart, it is not redownloaded again.
The .ollama folder contains a lot of sha256 files which seem to be the downloaded model files but not the final model.
My current docker file:
```
version: '3'
services:
ollama:
build: .
image: ollama/ollama
container_name: ollama
volumes:
- ./ollama:/root/.ollama
```
|
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I_kwDOJ0Z1Ps6KPb2h
| 4,666
|
ollama doesn't create a model from modelfile and gives an error
|
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[] | null | 5
| 2024-05-27T14:32:53
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### What is the issue?
Sorry in advance for any mistakes in text
when I trying to create a model in terminal, no matter what it based on, and even if the "modelfile" is a stock template of downloaded llm, after command "ollama create test" i got same output everytime
" Error: command must be one of "from", "license", "template", "system", "adapter", "parameter", or "message" "
i tried to change names of file, change command to e.g. "ollama create test -t modelfile" it's not working at all.
also i tried to edit commands in modelfile, so it doesn't get any reaction too. I've read the recent topic with similar error here, as it been suggested - converted modelfile in utf8, download file from topic - didn't helped. And also, i re-installed Ollama.
i tried to create modelfile thru Open WebUI from my models, and it works fine. And if i write in dialoge with llm in terminal: "/set system " you are something " and then " /save test " it saves and create new model without any trouble.
it is clear that the problem is not with modelfile itself, so what is it?
OS is WIN10pro. 16gb RAM, fx8320, gtx1650 with CUDA drivers installed.
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.38
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I_kwDOJ0Z1Ps6oD7uu
| 8,677
|
Wrote scripts to import gguf files/folder
|
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| 2025-01-30T00:09:02
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Don't see a "discussion" tab like I see for other repos, so just creating an issue.
Had a bunch of gguf's in a folder, so wrote 2 scripts (windows and shell) to import a single gguf and all ggufs in a given folder.
Don't know how to get a PR in but I can attach them here is any of you think they are useful.
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I_kwDOJ0Z1Ps6l08YG
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|
Return in a response a flag if the input request was truncated
|
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| 2025-01-11T19:53:52
| 2025-01-15T23:59:07
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|
CONTRIBUTOR
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| null | null | null |
Add to the response a flag that indicates true if Ollama truncated the input request.
As a developer, I would like the Ollama response to have a flag indicating that it truncated the input prompt so that I can initiate client-side behavior based on this information.
In some situations, the accuracy of the chat response depends upon processing the complete input prompt. A client is not informed if the input was truncated.
A client may be unable to match Ollama's calculation for the context size required for a given input.
The truncation works well for chat conversations, but the user may experience a degradation in the quality of the conversation.
The truncation might negatively affect an agent. An agent not interacting with a user might receive a longer-than-expected response in a previous iteration. In the following chat request, Ollama truncates the input prompt. The agent would not be aware the request was truncated. The agent may try to estimate but may not be able to match Ollama's calculation.
It also appears that in certain situations, the routes.go chatPrompt call will not truncate the input prompt, but the runner will. I saw this when I had put the context request size one less than the reported prompt eval for the same request. For example, a chat request that the prompt eval is 13. I sent the same request but with a context size of 12. The eval is 55. So, the runner also hit the context limit.
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I_kwDOJ0Z1Ps6AKsj1
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|
Piping to `stdin` does not work in windows
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| 2024-02-23T03:04:37
| 2024-03-14T18:55:20
| 2024-03-14T18:55:20
|
MEMBER
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Minor issue, but piping to stdin doesn't work on windows with git bash
```
$ cat README.md | ollama run gemma "What is in this document?"
failed to get console mode for stdin: The handle is invalid.
```
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I_kwDOJ0Z1Ps6BETMS
| 2,894
|
How to get Ollama to use my RTX 4090 on windows 11
|
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| 2024-03-03T14:36:27
| 2024-03-03T19:19:12
| 2024-03-03T19:19:12
|
NONE
|
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I have 12600K + 64GB RAM + RTX 4090. I use Ollama + OpenCHat.
For some reason Ollama won't use my RTX 4090. How can I show the program my graphics card?
```
messages = chat_histories[chat_id]
options = {
"num_ctx": 12768,
"num_thread": 10,
"num_predict": 300,
"repeat_last_n": 100,
"temperature": 0.75
}
response = ""
```
[config.json](https://github.com/ollama/ollama/files/14473300/config.json)
[server.log](https://github.com/ollama/ollama/files/14473301/server.log)
[app.log](https://github.com/ollama/ollama/files/14473302/app.log)
**small bursts of GPU work are the work of Stable Diffusion
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| 2,470,933,671
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I_kwDOJ0Z1Ps6TR3Sn
| 6,394
|
mistral-nemo:12b-instruct-2407-fp16 will return empty string using json mode while mistral-nemo:12b will return code
|
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| 2024-08-16T20:22:03
| 2024-08-17T22:00:18
| 2024-08-17T21:59:25
|
NONE
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currently using openai api support `mistral-nemo:12b-instruct-2407-fp16` returns an empty string
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I_kwDOJ0Z1Ps6d7U8W
| 7,621
|
ollama run connect to server failed use 780M iGPU after update rocm-core from 6.0.2 to 6.2.2 on arch linux.
|
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| 2024-11-11T14:51:56
| 2024-11-11T15:14:30
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### What is the issue?
I use Arch linux, i update my package to latest today.
Following is my upgraded version:
```
[2024-11-11T17:25:48+0800] [ALPM] upgraded rocm-opencl-sdk (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:48+0800] [ALPM] upgraded python-pytorch-rocm (2.3.1-8 -> 2.5.1-3)
[2024-11-11T17:25:45+0800] [ALPM] upgraded rocm-hip-sdk (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:45+0800] [ALPM] upgraded rocm-hip-libraries (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:44+0800] [ALPM] upgraded rocm-smi-lib (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:44+0800] [ALPM] upgraded rocm-hip-runtime (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:44+0800] [ALPM] upgraded rocm-cmake (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:44+0800] [ALPM] upgraded rocm-language-runtime (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:41+0800] [ALPM] upgraded rocm-clang-ocl (6.0.2-1 -> 6.1.2-1)
[2024-11-11T17:25:41+0800] [ALPM] upgraded rocm-opencl-runtime (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:06+0800] [ALPM] upgraded rocminfo (6.0.2-1 -> 6.2.2-1)
[2024-11-11T17:25:05+0800] [ALPM] upgraded rocm-device-libs (6.0.2-1 -> 6.2.2-2)
[2024-11-11T17:25:05+0800] [ALPM] upgraded rocm-llvm (6.0.2-1 -> 6.2.2-2)
[2024-11-11T17:24:59+0800] [ALPM] upgraded rocm-core (6.0.2-2 -> 6.2.2-1)
```
Before this update, when i run ollama serve use following command:
```
HSA_OVERRIDE_GFX_VERSION=11.0.0 OLLAMA_KEEP_ALIVE=-1 ollama serve
```
I can run `ollama run llama3.2` succssful.
But, after this update, i can still run serve without issue: (check following start log)
<details>
<summary> ollama serve start log</summary>
```
╰──➤ $ HSA_OVERRIDE_GFX_VERSION=11.0.0 OLLAMA_KEEP_ALIVE=-1 ollama serve
2024/11/11 22:46:36 routes.go:1189: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION:11.0.0 HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/home/zw963/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://*] OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2024-11-11T22:46:36.620+08:00 level=INFO source=images.go:755 msg="total blobs: 15"
time=2024-11-11T22:46:36.620+08:00 level=INFO source=images.go:762 msg="total unused blobs removed: 0"
time=2024-11-11T22:46:36.620+08:00 level=INFO source=routes.go:1240 msg="Listening on 127.0.0.1:11434 (version 0.4.1)"
time=2024-11-11T22:46:36.621+08:00 level=INFO source=common.go:135 msg="extracting embedded files" dir=/tmp/ollama3370373680/runners
time=2024-11-11T22:46:36.670+08:00 level=INFO source=common.go:49 msg="Dynamic LLM libraries" runners="[cuda_v11 cuda_v12 rocm cpu cpu_avx cpu_avx2]"
time=2024-11-11T22:46:36.670+08:00 level=INFO source=gpu.go:221 msg="looking for compatible GPUs"
time=2024-11-11T22:46:36.695+08:00 level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-11-11T22:46:36.695+08:00 level=INFO source=amd_linux.go:386 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION=11.0.0
time=2024-11-11T22:46:36.695+08:00 level=INFO source=types.go:123 msg="inference compute" id=0 library=rocm variant="" compute=gfx1103 driver=0.0 name=1002:15bf total="8.0 GiB" available="5.9 GiB"
```
</details>
But, it failed with following log when run `ollama run llama3.2`
```
2024/11/11 - 22:50:32 | 200 | 17.033µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/11/11 - 22:50:32 | 200 | 13.61116ms | 127.0.0.1 | POST "/api/show"
time=2024-11-11T22:50:32.351+08:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loading" model=/home/zw963/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff gpu=0 parallel=4 available=6257360896 required="3.7 GiB"
time=2024-11-11T22:50:32.352+08:00 level=INFO source=server.go:105 msg="system memory" total="54.7 GiB" free="45.2 GiB" free_swap="63.0 GiB"
time=2024-11-11T22:50:32.352+08:00 level=INFO source=memory.go:343 msg="offload to rocm" layers.requested=-1 layers.model=29 layers.offload=29 layers.split="" memory.available="[5.8 GiB]" memory.gpu_overhead="0 B" memory.required.full="3.7 GiB" memory.required.partial="3.7 GiB" memory.required.kv="896.0 MiB" memory.required.allocations="[3.7 GiB]" memory.weights.total="2.4 GiB" memory.weights.repeating="2.1 GiB" memory.weights.nonrepeating="308.2 MiB" memory.graph.full="424.0 MiB" memory.graph.partial="570.7 MiB"
time=2024-11-11T22:50:32.354+08:00 level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama3370373680/runners/rocm/ollama_llama_server --model /home/zw963/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff --ctx-size 8192 --batch-size 512 --n-gpu-layers 29 --threads 8 --parallel 4 --port 28751"
time=2024-11-11T22:50:32.354+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-11-11T22:50:32.354+08:00 level=INFO source=server.go:562 msg="waiting for llama runner to start responding"
time=2024-11-11T22:50:32.354+08:00 level=INFO source=server.go:596 msg="waiting for server to become available" status="llm server error"
/tmp/ollama3370373680/runners/rocm/ollama_llama_server: error while loading shared libraries: libggml_rocm.so: cannot open shared object file: No such file or directory
time=2024-11-11T22:50:32.605+08:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: exit status 127"
[GIN] 2024/11/11 - 22:50:32 | 500 | 288.728002ms | 127.0.0.1 | POST "/api/generate"
```
I can still use it if not set `HSA_OVERRIDE_GFX_VERSION=11.0.0` when start ollama server.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.4.1
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PR_kwDOJ0Z1Ps5zQH94
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Update README.md
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Added Mesop example to web & desktop
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PR_kwDOJ0Z1Ps5jLeS2
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update cmake flags for `amd64` macOS
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I_kwDOJ0Z1Ps6LM-CG
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llama3:7b cache size set
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I installed two NVIDIA RTX 2080 TI graphics cards in an experimental deployment and successfully ran the llama3:7b model. I want to know how to adjust the cache size.
thanks
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Problems with deepseek-r1:671b, ollama keeps crashing on long answers
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| 2025-01-27T20:04:40
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NONE
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### What is the issue?
Hi all,
I'm using an r960 with 2TB of ram, so ram is not a problem here. I'm experiencing constant crashes of ollama 0.5.7 and deepseek-r1:671b, even increasing the context window with the command /set parameter num_ctx 4096.
I also tried a second system, an r670 csp with 1TB of ram, but the problem occurs in the same way.
I'm not able to use gpu due to the massive size of the model, anyway plenty of cores do the job for my current pourposes.
os are ubuntu 22.04.5 and 24.04.1
### OS
Linux
### GPU
_No response_
### CPU
Intel
### Ollama version
0.5.7
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PR_kwDOJ0Z1Ps5zrkYW
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Update OpenAI Compatibility Docs with /v1/models/{model}
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`ollama run` doesn't continue after one reponse
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| 2023-07-17T16:43:08
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here are how you reproduce
```$ ollama run orca hello
Hello! How can I assist you today?Error: stream: EOF
$ logs ollama run orca "why is the sky blue"
The sky appears blue because of a process called scattering.
When sunlight enters the Earth's atmosphere, it collides with gas molecules
such as oxygen and nitrogen. These collisions cause the light to scatter in
all directions. Blue light has a shorter wavelength and is scattered more
easily than other colors, so it is scattered more widely across the sky,
making it appear blue. This effect is also why the sky is usually darker
during sunrise and sunset when the sun is below the horizon and
cannot be seen.Error: stream: EOF```
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PR_kwDOJ0Z1Ps5irqMT
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build cuda and rocm
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| 2023-12-22T20:22:28
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I_kwDOJ0Z1Ps5_jHql
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Feature - Support Custom Actions
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NONE
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Support for custom actions to cal custom API/Code as part of llama response. possibly part of modelfiles via python scripts? chatGPT has similar with integrations with Zapier
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How to update Ollama to the latest version?
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### What is the issue?
As the title described. How to update Ollama to the latest version?
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
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Slow performance on `/api/show`
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| 2024-06-23T18:36:53
| 2024-07-24T19:10:57
| 2024-07-24T19:10:57
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MEMBER
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### What is the issue?
Because we now show more model details, `/api/show` has gotten slower. The part that's slow specifically is reading the arrays (vocab, tensors, etc)
```
case ggufTypeArray:
v, err = readGGUFArray(llm, rs)
```
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.1.45
|
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I_kwDOJ0Z1Ps6E3e4l
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|
Experimental LLM Library Override does not appear to work on Windows
|
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### What is the issue?
I tried the [Experimental LLM Library Override](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#llm-libraries) on Windows via two means:
* Temporary environment variable definition: `SET OLLAMA_LLM_LIBRARY="cpu_avx2" & ollama run deepseek-coder`
* Permanent environment variable definition in the Windows System dialog.
Both failed, in server.log I get the following error:
`time=2024-04-06T10:11:46.333+02:00 level=INFO source=llm.go:147 msg="Invalid OLLAMA_LLM_LIBRARY \"cpu_avx2\" - not found"`
And ollama proceeds to use my GPU.
See the full server.log attached:
[server.log](https://github.com/ollama/ollama/files/14892936/server.log)
### What did you expect to see?
Ollama should be using cpu_avx2 instead of the GPU.
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Windows
### Architecture
x86
### Platform
_No response_
### Ollama version
0.1.30
### GPU
Nvidia
### GPU info
Nvidia GeForce 3060 Laptop
### CPU
Intel
### Other software
Intel i7-12700H
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I_kwDOJ0Z1Ps6JPCmv
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|
Ollama 0.1.38 has high video memory usage and runs very slowly.
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### What is the issue?
I am using Windows 10 with an NVIDIA 2080Ti graphics card that has 22GB of video memory. I upgraded from version 0.1.32 to 0.1.38 with the goal of supporting loading multiple models and handling multiple concurrent requests. However, I noticed that under version 0.1.38, the video memory usage is very high, and the speed has become much slower.
I am using the "codeqwen:7b-chat-v1.5-q8_0" model. Under version 0.1.32, it used around 8GB of video memory and output approximately 10 tokens per second. However, under version 0.1.38, it is using 18.8GB of video memory, and based on my observation, it is only outputting 1-2 tokens per second.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.38
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macos: detect potential version skew
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| 2024-12-20T22:17:24
| 2024-12-23T15:35:57
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During upgrade, we could get into a scenario where an old serve tries to start newer runner executables, which could have new expectations. This added check will handle non-zero exit status from the runner and double check the current process has the same version as the executable on disk. If the version has skewed, exit the serve process so the app can respawn the new binary.
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I_kwDOJ0Z1Ps6RqlTz
| 6,134
|
can't change ollama server address :127.0.0.1:11434 after binary ollama install
|
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| 2024-08-02T02:23:32
| 2024-08-09T21:13:42
| 2024-08-09T21:13:29
|
NONE
|
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### What is the issue?
i install ollama using binary package ,find that ollama server address 127.0.0.1:11434 ,ip why can't change it ? who run into the problem ? later i only used nginx proxy ,but ,open-webui reponse-webui was very slow vs command consle
### OS
Linux
### GPU
Intel
### CPU
Intel
### Ollama version
0.3.2
|
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I_kwDOJ0Z1Ps55GMkl
| 1,423
|
Allow Response Templating
|
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| 2023-12-07T23:11:26
| 2023-12-22T22:07:06
| 2023-12-22T22:07:06
|
CONTRIBUTOR
|
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In order to support formats like chatml Ollama must support post-response templating:
```
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
```
|
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| 2,624,183,476
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I_kwDOJ0Z1Ps6cady0
| 7,419
|
Integrating Into Desktop App
|
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[] | null | 2
| 2024-10-30T13:52:19
| 2024-11-01T18:56:26
| 2024-11-01T18:56:26
|
NONE
|
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| null | null | null |
I'd love to use Ollama for serving LLMs in my company's Mac/Linux/Windows desktop app, but I'm a little confused about some things.
I'd like to integrate it directly rather than requring the user to manually install Ollama because that UX isn't very good IMO. You [mention in the Windows docs](https://github.com/ollama/ollama/blob/main/docs/windows.md#standalone-cli:~:text=If%20you%27d%20like%20to%20install%20or%20integrate%20Ollama%20as%20a%20service%2C%20a%20standalone%20ollama%2Dwindows%2Damd64.zip%20zip%20file%20is%20available%20containing%20only%20the%20Ollama%20CLI%20and%20GPU%20library%20dependencies%20for%20Nvidia%20and%20AMD.%20This%20allows%20for%20embedding%20Ollama%20in%20existing%20applications) that this can be done by just utilizing the Windows release zip, but at 3.25 GB that's way too large to bundle with our app. It looks like it's so big because (1) different runners are included to allow Ollama to choose the optimal one, and (2) AMD and NVIDIA GPU libs are included.
I understand including different runners, but I don't understand why GPU libs like cudart, cublas, rocblas, etc. are included - shouldn't these be present on the system if the proper drivers are installed? If it's not possible to remove these, then it would be nice if you could generate separate releases for each runner / gpu libs to somewhat reduce build size.
That being said, thanks for making an awesome product!
|
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I_kwDOJ0Z1Ps6nZJTm
| 8,556
|
Please separate deepseek-r1 from deepseek-r1-Distill!
|
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| 2025-01-24T03:04:56
| 2025-01-24T03:20:18
| 2025-01-24T03:20:18
|
NONE
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Please separate deepseek-r1 from deepseek-r1-Distill!
This is not the same model and the architecture is different!
The model on the ollama official website is a perfect obfuscation!
|
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PR_kwDOJ0Z1Ps513q8w
| 5,790
|
Update llama.cpp submodule to 1bdd8ae1
|
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| 2024-07-19T08:28:04
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fix:https://github.com/ollama/ollama/issues/5769
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I_kwDOJ0Z1Ps6eisdj
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Only CPU is used after rebooting
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NONE
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[I found someone wrote a thread describing only cpu is used after rebooting in windows ](https://github.com/ollama/ollama/issues/4984#issue-2347076913)
I also had similar problems even in Ubuntu OS.
I used the latest version(0.4.1). I guess this bug comes from that the ollama service is started faster than the init. of GPUs. So I make an **ad-hoc** solution. Instead of service, I just make a script delaying start serve(ollama serve).
```bash
# ollama_run
echo "Delayed Ollama Runner Start, it delays 10 sec."
sleep 10
ollama serve
```
Then I make this called from *Ubuntu Startup Application Preferences*. I think its delaying may not be needed as it is called after GPUs initialization is finished anyway.
**WARNING**: After starting ollama by calling directly `ollama serve`, model storage directory is changed to `~/.ollama/models`.(I don't know why?) So previously dowonloaded model is not loaded. In that case, you can copy or move whole models folder to '[home folder]/.ollama` from `/usr/share/ollama/.ollama`.
_Originally posted by @3DAlgoLab in https://github.com/ollama/ollama/issues/4984#issuecomment-2477251430_
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| 1,830,020,783
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PR_kwDOJ0Z1Ps5W2Bla
| 249
|
Add "Awesome projects built with Ollama" section to README, including Continue
|
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| 2023-07-31T21:01:22
| 2023-08-01T15:07:50
| 2023-08-01T15:07:50
|
CONTRIBUTOR
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Format and text are up for debate, but here's a description of Continue
|
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I_kwDOJ0Z1Ps5_mV7K
| 2,569
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Connection with http://127.0.0.1:11434/api/chat forcibly closed
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I've installed Ollama in Windows 10, I launch it and it runs, I can pull a model but when I want to run it this is the error message I see:
"Error: Post "http://127.0.0.1:11434/api/chat": read tcp 127.0.0.1:52725->127.0.0.1:11434: wsarecv: An existing connection was forcibly closed by the remote host."
I disabled the previous wsl service, I've also set the the port 11434 in the firewall but nothing happens. With WSL it runs.
Thanks
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I_kwDOJ0Z1Ps6CGQpM
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Please support Zephyr 7B Gemma
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NONE
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Please support [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)! This [HG Chat](https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat) is a lot better than Zephyr beta (fine-tuned on Mistral 7B).
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I_kwDOJ0Z1Ps6YnMXJ
| 7,072
|
Deepseek-v2.5 fails to load on a system with 24GB VRAM (RTX 3090) and 128GB RAM
|
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### What is the issue?
I'm unable to load the [deepseek-v2.5](https://ollama.com/library/deepseek-v2.5) model on a system with 24GB VRAM (RTX 3090) and 128GB RAM:
```
$ ollama --version
ollama version is 0.3.11
$
$ ollama list | grep -e ID -e deepseek-v2.5
NAME ID SIZE MODIFIED
deepseek-v2.5:latest 409b2dd8a3c4 132 GB 9 hours ago
$ ollama show deepseek-v2.5
Model
architecture deepseek2
parameters 235.7B
context length 163840
embedding length 5120
quantization Q4_0
Parameters
stop "<|begin?of?sentence|>"
stop "<|end?of?sentence|>"
stop "<|User|>"
stop "<|Assistant|>"
stop "<|fim?begin|>"
stop "<|fim?hole|>"
stop "<|fim?end|>"
License
DEEPSEEK LICENSE AGREEMENT
Version 1.0, 23 October 2023
$ ollama run deepseek-v2.5
Error: llama runner process has terminated: error:failed to create context with model '/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c'
$
```
Journatctl output:
```
$ sudo journalctl -f
Oct 02 09:20:41 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:20:41 | 200 | 14.89µs | 127.0.0.1 | HEAD "/"
Oct 02 09:20:41 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:20:41 | 200 | 22.898622ms | 127.0.0.1 | GET "/api/tags"
Oct 02 09:20:56 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:21:04 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:21:08 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:08 | 200 | 18.64µs | 127.0.0.1 | HEAD "/"
Oct 02 09:21:08 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:08 | 200 | 3.682586ms | 127.0.0.1 | GET "/api/tags"
Oct 02 09:21:23 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:23 | 200 | 29.73µs | 127.0.0.1 | HEAD "/"
Oct 02 09:21:23 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:23 | 200 | 3.880099ms | 127.0.0.1 | GET "/api/tags"
Oct 02 09:21:28 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:28 | 200 | 15.64µs | 127.0.0.1 | HEAD "/"
Oct 02 09:21:28 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:28 | 200 | 1.227682ms | 127.0.0.1 | GET "/api/tags"
Oct 02 09:21:35 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:35 | 200 | 17.3µs | 127.0.0.1 | HEAD "/"
Oct 02 09:21:35 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:21:35 | 200 | 29.058041ms | 127.0.0.1 | POST "/api/show"
Oct 02 09:22:02 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:22:02 | 200 | 15.01µs | 127.0.0.1 | HEAD "/"
Oct 02 09:22:02 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:22:02 | 200 | 13.210104ms | 127.0.0.1 | POST "/api/show"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.094+10:00 level=INFO source=sched.go:507 msg="updated VRAM based on existing loaded models" gpu=GPU-cebf1f58-0f47-0efa-a683-ceeb3c1755cc library=cuda total="23.7 GiB" available="8.8 GiB"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.909+10:00 level=INFO source=server.go:103 msg="system memory" total="125.7 GiB" free="118.6 GiB" free_swap="7.2 GiB"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.910+10:00 level=INFO source=memory.go:326 msg="offload to cuda" layers.requested=-1 layers.model=61 layers.offload=10 layers.split="" memory.available="[23.4 GiB]" memory.gpu_overhead="0 B" memory.required.full="134.5 GiB" memory.required.partial="22.1 GiB" memory.required.kv="9.4 GiB" memory.required.allocations="[22.1 GiB]" memory.weights.total="132.5 GiB" memory.weights.repeating="132.1 GiB" memory.weights.nonrepeating="410.2 MiB" memory.graph.full="642.0 MiB" memory.graph.partial="891.5 MiB"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.911+10:00 level=INFO source=server.go:388 msg="starting llama server" cmd="/tmp/ollama1470999858/runners/cuda_v12/ollama_llama_server --model /mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 10 --no-mmap --parallel 1 --port 43133"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.911+10:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.911+10:00 level=INFO source=server.go:587 msg="waiting for llama runner to start responding"
Oct 02 09:22:03 xlr2 ollama[2576620]: time=2024-10-02T09:22:03.912+10:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error"
Oct 02 09:22:03 xlr2 ollama[3244519]: INFO [main] build info | build=10 commit="9225b05" tid="139874777862144" timestamp=1727824923
Oct 02 09:22:03 xlr2 ollama[3244519]: INFO [main] system info | n_threads=8 n_threads_batch=8 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139874777862144" timestamp=1727824923 total_threads=16
Oct 02 09:22:03 xlr2 ollama[3244519]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="43133" tid="139874777862144" timestamp=1727824923
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: loaded meta data with 46 key-value pairs and 959 tensors from /mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c (version GGUF V3 (latest))
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 0: general.architecture str = deepseek2
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 1: general.type str = model
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 2: general.name str = DeepSeek V2.5
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 3: general.version str = V2.5
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 4: general.basename str = DeepSeek
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 5: general.size_label str = 160x14B
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 6: general.license str = other
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 7: general.license.name str = deepseek
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 8: general.license.link str = https://github.com/deepseek-ai/DeepSe...
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 9: deepseek2.block_count u32 = 60
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 10: deepseek2.context_length u32 = 163840
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 11: deepseek2.embedding_length u32 = 5120
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 12: deepseek2.feed_forward_length u32 = 12288
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 13: deepseek2.attention.head_count u32 = 128
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 14: deepseek2.attention.head_count_kv u32 = 128
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 15: deepseek2.rope.freq_base f32 = 10000.000000
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 16: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 17: deepseek2.expert_used_count u32 = 6
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 18: general.file_type u32 = 2
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 19: deepseek2.leading_dense_block_count u32 = 1
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 20: deepseek2.vocab_size u32 = 102400
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 21: deepseek2.attention.q_lora_rank u32 = 1536
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 22: deepseek2.attention.kv_lora_rank u32 = 512
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 23: deepseek2.attention.key_length u32 = 192
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 24: deepseek2.attention.value_length u32 = 128
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 25: deepseek2.expert_feed_forward_length u32 = 1536
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 26: deepseek2.expert_count u32 = 160
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 27: deepseek2.expert_shared_count u32 = 2
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 28: deepseek2.expert_weights_scale f32 = 16.000000
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 29: deepseek2.rope.dimension_count u32 = 64
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 30: deepseek2.rope.scaling.type str = yarn
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 31: deepseek2.rope.scaling.factor f32 = 40.000000
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 32: deepseek2.rope.scaling.original_context_length u32 = 4096
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 33: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 34: tokenizer.ggml.model str = gpt2
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 35: tokenizer.ggml.pre str = deepseek-llm
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 36: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ...
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 37: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 38: tokenizer.ggml.merges arr[str,99757] = ["G G", "G t", "G a", "i n", "h e...
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 39: tokenizer.ggml.bos_token_id u32 = 100000
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 100001
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 100001
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 43: tokenizer.ggml.add_eos_token bool = false
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 44: tokenizer.chat_template str = {% if not add_generation_prompt is de...
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - kv 45: general.quantization_version u32 = 2
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - type f32: 300 tensors
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - type q4_0: 658 tensors
Oct 02 09:22:03 xlr2 ollama[2576620]: llama_model_loader: - type q6_K: 1 tensors
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_vocab: special tokens cache size = 18
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_vocab: token to piece cache size = 0.6411 MB
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: format = GGUF V3 (latest)
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: arch = deepseek2
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: vocab type = BPE
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_vocab = 102400
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_merges = 99757
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: vocab_only = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_ctx_train = 163840
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_embd = 5120
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_layer = 60
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_head = 128
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_head_kv = 128
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_rot = 64
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_swa = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_embd_head_k = 192
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_embd_head_v = 128
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_gqa = 1
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_embd_k_gqa = 24576
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_embd_v_gqa = 16384
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: f_norm_eps = 0.0e+00
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: f_norm_rms_eps = 1.0e-06
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: f_logit_scale = 0.0e+00
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_ff = 12288
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_expert = 160
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_expert_used = 6
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: causal attn = 1
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: pooling type = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: rope type = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: rope scaling = yarn
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: freq_base_train = 10000.0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: freq_scale_train = 0.025
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_ctx_orig_yarn = 4096
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: rope_finetuned = unknown
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: ssm_d_conv = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: ssm_d_inner = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: ssm_d_state = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: ssm_dt_rank = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: ssm_dt_b_c_rms = 0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: model type = 236B
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: model ftype = Q4_0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: model params = 235.74 B
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: model size = 123.78 GiB (4.51 BPW)
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: general.name = DeepSeek V2.5
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: BOS token = 100000 '<|begin?of?sentence|>'
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: EOS token = 100001 '<|end?of?sentence|>'
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: PAD token = 100001 '<|end?of?sentence|>'
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: LF token = 126 'Ä'
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: max token length = 256
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_layer_dense_lead = 1
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_lora_q = 1536
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_lora_kv = 512
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_ff_exp = 1536
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: n_expert_shared = 2
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: expert_weights_scale = 16.0
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_print_meta: rope_yarn_log_mul = 0.1000
Oct 02 09:22:04 xlr2 ollama[2576620]: time=2024-10-02T09:22:04.163+10:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model"
Oct 02 09:22:04 xlr2 ollama[2576620]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
Oct 02 09:22:04 xlr2 ollama[2576620]: ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
Oct 02 09:22:04 xlr2 ollama[2576620]: ggml_cuda_init: found 1 CUDA devices:
Oct 02 09:22:04 xlr2 ollama[2576620]: Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Oct 02 09:22:04 xlr2 ollama[2576620]: llm_load_tensors: ggml ctx size = 0.80 MiB
Oct 02 09:22:05 xlr2 ollama[2576620]: time=2024-10-02T09:22:05.618+10:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding"
Oct 02 09:22:33 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:22:37 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:22:47 xlr2 ollama[2576620]: time=2024-10-02T09:22:47.399+10:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model"
Oct 02 09:22:51 xlr2 ollama[2576620]: llm_load_tensors: offloading 10 repeating layers to GPU
Oct 02 09:22:51 xlr2 ollama[2576620]: llm_load_tensors: offloaded 10/61 layers to GPU
Oct 02 09:22:51 xlr2 ollama[2576620]: llm_load_tensors: CUDA_Host buffer size = 105416.00 MiB
Oct 02 09:22:51 xlr2 ollama[2576620]: llm_load_tensors: CUDA0 buffer size = 21335.35 MiB
Oct 02 09:23:10 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:23:17 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: n_ctx = 2048
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: n_batch = 512
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: n_ubatch = 512
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: flash_attn = 0
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: freq_base = 10000.0
Oct 02 09:23:44 xlr2 ollama[2576620]: llama_new_context_with_model: freq_scale = 0.025
Oct 02 09:23:48 xlr2 ollama[2576620]: llama_kv_cache_init: CUDA_Host KV buffer size = 8000.00 MiB
Oct 02 09:23:48 xlr2 ollama[2576620]: llama_kv_cache_init: CUDA0 KV buffer size = 1600.00 MiB
Oct 02 09:23:48 xlr2 ollama[2576620]: llama_new_context_with_model: KV self size = 9600.00 MiB, K (f16): 5760.00 MiB, V (f16): 3840.00 MiB
Oct 02 09:23:48 xlr2 ollama[2576620]: llama_new_context_with_model: CUDA_Host output buffer size = 0.41 MiB
Oct 02 09:23:48 xlr2 ollama[2576620]: ggml_backend_cuda_buffer_type_alloc_buffer: allocating 842.00 MiB on device 0: cudaMalloc failed: out of memory
Oct 02 09:23:48 xlr2 ollama[2576620]: ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 882903040
Oct 02 09:23:48 xlr2 ollama[2576620]: llama_new_context_with_model: failed to allocate compute buffers
Oct 02 09:23:50 xlr2 ollama[2576620]: llama_init_from_gpt_params: error: failed to create context with model '/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c'
Oct 02 09:23:52 xlr2 kernel: clocksource: Long readout interval, skipping watchdog check: cs_nsec: 1586308203 wd_nsec: 1586308525
Oct 02 09:24:05 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:24:09 xlr2 systemd-networkd[3394145]: enp7s0: DHCPv6 lease lost
Oct 02 09:24:10 xlr2 ollama[3244519]: ERROR [load_model] unable to load model | model="/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c" tid="139874777862144" timestamp=1727825050
Oct 02 09:24:10 xlr2 ollama[2576620]: terminate called without an active exception
Oct 02 09:24:10 xlr2 ollama[2576620]: time=2024-10-02T09:24:10.958+10:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error"
Oct 02 09:24:11 xlr2 ollama[2576620]: time=2024-10-02T09:24:11.213+10:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: error:failed to create context with model '/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c'"
Oct 02 09:24:11 xlr2 ollama[2576620]: [GIN] 2024/10/02 - 09:24:11 | 500 | 2m8s | 127.0.0.1 | POST "/api/generate"
Oct 02 09:24:16 xlr2 ollama[2576620]: time=2024-10-02T09:24:16.336+10:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.122496579 model=/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c
Oct 02 09:24:16 xlr2 ollama[2576620]: time=2024-10-02T09:24:16.586+10:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.372482202 model=/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c
Oct 02 09:24:16 xlr2 ollama[2576620]: time=2024-10-02T09:24:16.837+10:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.623037141 model=/mnt/ssd/ai/ollama/.ollama/models/blobs/sha256-799587243b19fdcc715a4aab927f5700d1b9508bd0b8b0db9dc2bd6fc622979c
```
System info:
```
$ uname -a
Linux xlr2 5.15.0-112-generic #122-Ubuntu SMP Thu May 23 07:48:21 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
$ nproc
16
$ free -h
total used free shared buff/cache available
Mem: 125Gi 5.3Gi 113Gi 33Mi 6.5Gi 119Gi
Swap: 8.0Gi 887Mi 7.1Gi
$ nvidia-smi
Wed Oct 2 10:04:06 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.08 Driver Version: 535.161.08 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3090 On | 00000000:0C:00.0 Off | N/A |
| 0% 33C P8 38W / 390W | 3MiB / 24576MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
$
```
I tried with both `0.3.11` and the latest `0.3.12` versions of Ollama.
Here is a snapshot of `htop` and `nvtop` outputs:
<img width="820" alt="htop_nvtop" src="https://github.com/user-attachments/assets/e21266eb-1d3b-4ab6-9324-5604fc99efb4">
Here is the recording showing the entire process:
https://github.com/user-attachments/assets/e306990a-a340-4a8f-b7de-b52d9b3de5e1
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.11
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I_kwDOJ0Z1Ps6n1Tbv
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Ollama is not installing on Termux
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NONE
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~ $ curl -fsSL https://ollama.com/install.sh | sh
```
>>> Installing ollama to /usr
No superuser binary detected.
Are you rooted?
```
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PR_kwDOJ0Z1Ps53gV83
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|
fix concurrency test
|
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CONTRIBUTOR
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errors were hidden by `integration` build tag
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readme: add phi4 model
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MEMBER
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readme: add phi4 model
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Error LLama runner process has terminated: %!w(<nil>)
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### What is the issue?
Sometimes when infering from ollama using the python module I get this error. After retrying a couple of times it works and looks random to me.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7
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PR_kwDOJ0Z1Ps5q-g51
| 3,376
|
only generate on changes to llm subdirectory
|
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| 2024-03-27T19:45:40
| 2024-03-27T21:12:54
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follow up to #3375 to also skip generate (linux, macos, windows) if there's no changes to the llm subdirectory
|
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| 2,616,050,717
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I_kwDOJ0Z1Ps6b7cQd
| 7,374
|
Reinstate OLLAMA_RUNNERS_DIR
|
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| 2024-10-26T18:28:24
| 2024-11-06T15:38:33
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NONE
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It appears that the OLLAMA_RUNNERS_DIR was removed from the code - at least I couldn't find it in github's search function. Currently (0.3.14) it is using /tmp/ollama<number>/runners again, as before the introduction of the OLLAMA_RUNNERS_DIR (or when not set).
IMHO, using /tmp for executables is not a good idea. I'd prefer to have some control over where the executables are extracted to.
So, please, bring back the OLLAMA_RUNNERS_DIR.
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I_kwDOJ0Z1Ps6EldUs
| 3,482
|
Please add Qwen-VL!
|
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| 2024-04-04T03:59:14
| 2024-07-29T01:25:43
| 2024-07-25T15:15:28
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NONE
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### What model would you like?
This image recognition model is very popular in China, so please add it to ollama：https://huggingface.co/Qwen/Qwen-VL
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PR_kwDOJ0Z1Ps5bLAcR
| 595
|
ignore systemctl is-system-running exit code
|
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| 2023-09-25T22:47:54
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PR_kwDOJ0Z1Ps6EkWZ1
| 8,014
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Avoid underflow when FreeMemory < overhead
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| 2024-12-09T16:15:23
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Fixes: #8011
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PR_kwDOJ0Z1Ps5e0LjA
| 1,031
|
Added logit_bias support
|
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| 2023-11-07T14:38:14
| 2024-03-16T16:23:18
| null |
NONE
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This PR brings the `logit_bias` functionality, already present in llama.cpp, which allows users to adjust the likelihood of token occurrences in generated text.
For example, for the prompt `"Once upon a "` we have:
1. Without `logit_bias`
```bash
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Once upon a ",
"stream": false,
"options": {"seed": 0, "num_predict" : 10}
}'
{"model":"llama2:7b", ... , "response":"time, in a far-off land, there", ...}
```
2. With `logit_bias` removing "time" and "Time" words:
```bash
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Once upon a ",
"stream": false,
"options": {"seed": 0, "num_predict" : 10,
"logit_bias": [[2230,false],[931,false],[2481,false],[5974,false]]}
}'
{"model":"llama2:7b", ... , "response":"Once upon a...! What a great prompt!", ...}
```
2. With `logit_bias` removing "time" and "Time" words, and increasing "night" likelihood :
```bash
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Once upon a ",
"stream": false,
"options": {"seed": 0, "num_predict" : 10,
"logit_bias": [[2230,false],[931,false],[2481,false],[5974,false],[11147, 15]]}
}'
{"model":"llama2:7b", ... , "response":"night, in a land far away, there lived"", ...}
```
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I_kwDOJ0Z1Ps5_7hlN
| 2,628
|
libext_server.a(llava.cpp.o) { in archive is not an object | not an ELF file }
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| 2024-03-27T20:53:23
| 2024-03-27T20:53:22
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NONE
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I'm trying to build ollama with AMD GPU support via command
```
ROCM_PATH=/opt/rocm CLBlast_DIR=/usr/lib/x86_64-linux-gnu/cmake/CLBlast go generate -tags rocm ./...
```
and in final stage of build I got an error mentioned in issue header:
I tried to replace `g++` with `clang++` at llm/generate/gen_common.sh as it was mentioned in [this comment](https://github.com/ollama/ollama/issues/2473#issuecomment-1942090475) but error in the end of build is still same.
[Adding](https://github.com/ollama/ollama/issues/2473#issuecomment-1941640723) `-fcf-protection=none` also wasn't helped, just changed error message a little bit
```
ld.lld: error: ../llama.cpp/build/linux/x86_64/cpu/examples/server/libext_server.a(llava.cpp.o): not an ELF file
clang++: error: linker command failed with exit code 1 (use -v to see invocation)
```
What do I need to fix for proper compiling of ext_server static library ?
Seems like issue related to [this part of ollama sources](https://github.com/ollama/ollama/blob/e1f50377f499b1784a866a6463af364987443238/llm/generate/gen_common.sh#L89)
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I_kwDOJ0Z1Ps54_mqR
| 1,414
|
Windows install runs into errors
|
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| 2023-12-07T05:33:03
| 2023-12-11T15:48:16
| 2023-12-11T15:48:16
|
NONE
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after manual install instructions and
```go build```
I receive the following errors:
```
# github.com/jmorganca/ollama/readline
readline\readline.go:199:12: undefined: syscall.Kill
readline\readline.go:199:28: undefined: syscall.SIGSTOP
```
After messing with readline.go I just manage to get a .exe file that doesn't work
@deadcoder0904 or anyone who compiled on windows and got a working executable, could you describe how you did it?
|
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PR_kwDOJ0Z1Ps5YNGuk
| 377
|
Strip protocol from model path
|
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| 2023-08-18T00:40:27
| 2023-08-22T04:56:57
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|
CONTRIBUTOR
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Took a whack at fixing https://github.com/jmorganca/ollama/issues/371 and reorganized the switch logic slightly as well.
Wasn't sure if it was better to strip all protocols or just `https://`, so if you'd like the latter I can switch it to just a `strings.TrimPrefix`. Happy to back out the updated switch code as well, just figured I'd do it while I was in there.
Thanks for a great tool, loving it so far. Hope this is helpful.
|
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I_kwDOJ0Z1Ps6Sm4f9
| 6,312
|
how to force ollama to use different cpu runners / how to compile windows avx512 runner?
|
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| 2024-11-04T19:16:38
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NONE
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### What is the issue?
according to logs ollama seems to only be using AVX not AVX2, how would i fix this and force avx2 or higher?
also wondering how i compile the avx512 runner for windows? i have compiled other runners and the cuda runner fine but it seems regardless of what i try to set it just generates cpu, avx, avx2 and stops
running `$env:OLLAMA_CUSTOM_CPU_DEFS="-DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=on"` or any other combo of this command seems to have no effect on generating a different runner aside from defaults, is there a file i need to edit to change what is compiled? i would also like to skip cpu, avx, avx2 if possible as it takes a fair amount of time and those are already compiled
cpu is i7-7820x with support for avx2 & avx512
### OS
Windows
### GPU
Nvidia, Intel
### CPU
Intel
### Ollama version
0.3.4
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PR_kwDOJ0Z1Ps55wQ1a
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|
add llama3.1 chat template
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| 2024-08-28T20:30:23
| 2024-08-28T21:03:22
| 2024-08-28T21:03:20
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CONTRIBUTOR
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This change adds the llama3.1 chat template do that it will be autodetected in `ollama create`.
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I_kwDOJ0Z1Ps6ebjg0
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|
Dealing with passing huge attachments to models?
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| 2024-11-14T08:36:09
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Hi all, I'm wondering what the best way is to pass huge amounts of "attachment" data to any of the models? A typical doc might be > 2 million words (~16 million characters).
I know this amount of data can't be fed to any under 70B as prompts due to context windows. If I go with llama 3.1 405B on a 4 x H100 NVL or 8 x H100 NVL, would that be the smart way to go?
Or is there a better approach to this that I'm missing (like training my own model or something).
Ideally I'd want to be able to just drop one of these docs into the model at any time as these files come new to me, frequently.
Thanks!
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PR_kwDOJ0Z1Ps505WvW
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|
Create SECURITY.md
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| 2024-07-09T23:25:27
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NONE
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I know that Ollama is under active development, but it would be great to have a security policy in place for folks to report bugs.
Also, would Ollama be open to enabling code scanning? https://docs.github.com/en/code-security/code-scanning/enabling-code-scanning/configuring-default-setup-for-code-scanning
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I_kwDOJ0Z1Ps6UgEKk
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|
actively retrieves the content returned from the web page
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expected that ollama can automatically identify the model, and then when the problem exceeds the capacity of the model, ollama actively retrieves the content returned from the web page to the model, and the model analyzes the content returned and finally gives the answer.
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I_kwDOJ0Z1Ps6K0df1
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I created Ollama - Open WebUI Script - Give it a try!
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I created "Ollama-Open-WebUI-Script" for those who, like me, prefer not to overload their PC at startup with too many resources. Instead of manually starting Docker, then Ollama, and finally Open WebUI, this script simplifies the entire process, giving you direct and easy access to these two fantastic projects.
Give it a try here https://github.com/Special-Niewbie/Ollama-Open-WebUI-Script, and enjoy a smoother experience with Ollama and Open WebUI!
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PR_kwDOJ0Z1Ps56E8ab
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Add serve step to quickstart
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| 2024-09-01T09:11:18
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NONE
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This missing step trips up beginners, as shown in https://github.com/ollama/ollama/issues/2727#issuecomment-1969331044
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I_kwDOJ0Z1Ps6PyTRK
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|
How to Set Up RAG / LLamaIndex with Windows Preview?
|
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I was wondering how, or if there is a way, to set up RAG with the Windows version?
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Error: listen tcp 127.0.0.1:11434: bind:
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Windows 10,
I cannot start Ollama,
```
$ ollama serve
Error: listen tcp 127.0.0.1:11434: bind: An attempt was made to access a socket in a way forbidden by its access permissions.
```
from **app.log**
```
time=2024-02-21T10:04:42.504+01:00 level=WARN source=server.go:109 msg="server crash 332 - exit code 1 - respawning"
time=2024-02-21T10:04:43.018+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:07:58.072+01:00 level=DEBUG source=logging_windows.go:12 msg="viewing logs with start C:\\Users\\user\\AppData\\Local\\Ollama"
time=2024-02-21T10:10:15.024+01:00 level=WARN source=server.go:109 msg="server crash 333 - exit code 1 - respawning"
time=2024-02-21T10:10:15.526+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:11:20.094+01:00 level=DEBUG source=eventloop.go:151 msg="unmanaged app message, lParm: 0x204"
time=2024-02-21T10:11:20.792+01:00 level=DEBUG source=lifecycle.go:30 msg="QUIT called"
time=2024-02-21T10:11:20.842+01:00 level=INFO source=lifecycle.go:78 msg="Waiting for ollama server to shutdown..."
time=2024-02-21T10:15:48.537+01:00 level=DEBUG source=server.go:104 msg="server shutdown with exit code 1"
time=2024-02-21T10:15:48.537+01:00 level=INFO source=lifecycle.go:82 msg="Ollama app exiting"
time=2024-02-21T10:37:42.782+01:00 level=INFO source=logging.go:45 msg="ollama app started"
time=2024-02-21T10:37:42.863+01:00 level=DEBUG source=lifecycle.go:26 msg="starting callback loop"
time=2024-02-21T10:37:42.863+01:00 level=DEBUG source=store.go:61 msg="loaded existing store C:\\Users\\user\\AppData\\Local\\Ollama\\config.json - ID: 6fbe69d4-62a6-4ad8-9768-da087897ebe2"
time=2024-02-21T10:37:42.864+01:00 level=DEBUG source=lifecycle.go:57 msg="Not first time, skipping first run notification"
time=2024-02-21T10:37:42.865+01:00 level=DEBUG source=server.go:130 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=2024-02-21T10:37:42.865+01:00 level=INFO source=server.go:131 msg="unable to connect to server"
time=2024-02-21T10:37:42.870+01:00 level=INFO source=server.go:87 msg="started ollama server with pid 40576"
time=2024-02-21T10:37:42.870+01:00 level=INFO source=server.go:89 msg="ollama server logs C:\\Users\\user\\AppData\\Local\\Ollama\\server.log"
time=2024-02-21T10:37:42.870+01:00 level=DEBUG source=eventloop.go:24 msg="starting event handling loop"
time=2024-02-21T10:37:42.941+01:00 level=WARN source=server.go:109 msg="server crash 1 - exit code 1 - respawning"
time=2024-02-21T10:37:43.445+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:37:44.461+01:00 level=WARN source=server.go:109 msg="server crash 2 - exit code 1 - respawning"
time=2024-02-21T10:37:44.636+01:00 level=WARN source=tray.go:365 msg="failed to bring menu to foreground: The operation completed successfully."
time=2024-02-21T10:37:44.971+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:37:45.525+01:00 level=DEBUG source=logging_windows.go:12 msg="viewing logs with start C:\\Users\\user\\AppData\\Local\\Ollama"
time=2024-02-21T10:37:45.882+01:00 level=DEBUG source=updater.go:87 msg="checking for available update at https://ollama.com/api/update?arch=amd64&nonce=RENbdDvJrUl9L0FRExH2xw&os=windows&ts=1708508265&version=0.1.25 with headers map[Authorization:[AAAAC3NzaC1lZDI1NTE5AAAAIAhd0mgYr2Vcgo+erwm80qQblGZaB2w4wE8iKVd5CEFx:dwydcT5JCB5WxCq5C7VD6Six77HhMVstFn6HMCBxU54YhtiEEYsOcHWMwC4EDADcM5NZD4oTgKf3/naoGa50CA==] User-Agent:[ollama/0.1.25 (amd64 windows) Go/go1.22.0]]"
time=2024-02-21T10:37:46.246+01:00 level=DEBUG source=updater.go:96 msg="check update response 204 (current version is up to date)"
time=2024-02-21T10:37:46.982+01:00 level=WARN source=server.go:109 msg="server crash 3 - exit code 1 - respawning"
time=2024-02-21T10:37:47.493+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:37:50.502+01:00 level=WARN source=server.go:109 msg="server crash 4 - exit code 1 - respawning"
time=2024-02-21T10:37:51.016+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
time=2024-02-21T10:37:55.030+01:00 level=WARN source=server.go:109 msg="server crash 5 - exit code 1 - respawning"
time=2024-02-21T10:37:55.543+01:00 level=ERROR source=server.go:112 msg="failed to restart server exec: already started"
```
**server.log**
`Error: listen tcp 127.0.0.1:11434: bind: An attempt was made to access a socket in a way forbidden by its access permissions.`
What I tried:
[Ollama issue 2560](https://github.com/ollama/ollama/issues/2560#issuecomment-1950690705), but seems that there is no proxy at that port
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Support for Tinyllava
|
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In addition to support for moondream #2259 which is lightning faster than llava, I believe there are some good guys there that can successfully make a GGUF version of Tinyllava which is faster than light. Tried it on safetensors to work really well. its been trained on the llava dataset, just like moondream.
https://huggingface.co/bczhou/tiny-llava-v1-hf
https://huggingface.co/YouLiXiya/tinyllava-v1.0-1.1b-hf
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I_kwDOJ0Z1Ps57houL
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|
ollama barely uses any Ram
|
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Hey Guys,
I run ollama on docker and use mostly 7b models. But my Ram usage stays under 4 GB. Sometimes even below 3 GB.
But the recommendations are 8 GB of Ram.
It has 4 Core CPU, and it generates very slow even though I got 24 GB of Ram. I don't have a Video Card, though.
I'm new to this, so can anyone tell me what I might need to do differently?
|
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| 2,136,985,321
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PR_kwDOJ0Z1Ps5nAGCZ
| 2,516
|
Fix a couple duplicate instance bugs
|
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| 2024-02-15T16:41:42
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- Prevent the installer running multiple times concurrently
- Detect multiple apps running and exit ~~with a message~~
|
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I_kwDOJ0Z1Ps6kjBGS
| 8,255
|
question: the Windows version is very slow when accessing the API
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| 2024-12-27T09:34:12
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Excuse me, the Windows version is very slow when accessing the API. What is the reason? But using the command line is faster
system : windows
memory : 24G
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PR_kwDOJ0Z1Ps5vl1FP
| 4,462
|
Port cuda/rocm skip build vars to linux
|
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| 2024-05-15T22:59:12
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Windows already implements these, carry over to linux.
|
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I_kwDOJ0Z1Ps527jTW
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Support forvietnamese-llama2-7b
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| 2023-11-15T18:38:50
| 2024-12-23T01:10:18
| 2024-12-23T01:10:17
|
NONE
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Hi folk
Really love this project
I just wonder can you guy support this model
https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB
It would be fantastic to have this run inside Ollama
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I_kwDOJ0Z1Ps5_4tWB
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[Thankyou] Thanks for the ollama community
|
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| 2024-02-21T01:06:32
| 2024-03-11T21:09:58
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NONE
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In Taiwanese community, we have done a survey to see which are the top 5 local LLMs used by AI enthusiasts.
ollama is list as the top 5.
Here are the candidate list.
https://matilabs.ai/2024/02/07/run-llms-locally/
Thanks for the ollama community's hard work. We really love this project. I feel it's easy and straight forward. Developer friendly.
<img width="412" alt="Local-LLMs-2024-02-21 085848" src="https://github.com/ollama/ollama/assets/2288469/f8534a53-f4b3-439b-a2ba-2fa37e783bee">
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I_kwDOJ0Z1Ps6E3X6b
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Databases
|
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| 2024-04-06T06:42:46
| 2024-05-09T02:26:26
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### What are you trying to do?
A way to manage, and create and train databases that can run on-top of models
### How should we solve this?
Docker has images, containers and volumes. Ollama currently only has models.
To add a new category for databases. Databases can be managed by...
- Import from file
- Export to file
- Download from registry
- Uploaded to registry
- Create new empty database
- Rename database
- Delete database
- Duplicate database (Copy/paste database)
Databases can be edited by training the databases on files (documents, images, videos, audios, web pages, etc). A Ollama GUI would be best for training a database.
Now you have models and databases, when you run a model, you can add one or more databases with it which it will use as additional trained data.
### What is the impact of not solving this?
Ollama will be limited to LLMs and unable to add additional training data on-top of LLMs.
### Anything else?
Adding an additional feature when running models to choose a database to send any trained data from prompt. If a response is good, user can send a like (thumbs up) to the database to train the model that the response was good. If a response is bad, user can send a dislike (thumbs down) to the database to train the model that the response was bad.
Adding a tagging feature to databases would be useful
https://github.com/ollama/ollama/issues/2847
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I_kwDOJ0Z1Ps6i51Dr
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Toggle theme
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| 2024-12-11T14:03:39
| 2024-12-25T21:51:26
| 2024-12-25T21:51:26
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NONE
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Hey,
Could you please add a feature for toggling between dark and light modes to ollama site? It would greatly enhance user experience by allowing customization based on preference and environment. A simple toggle button would be ideal!
|
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PR_kwDOJ0Z1Ps6JWxCt
| 8,656
|
Add DeepSeek R1 in README
|
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| 2025-01-29T12:38:21
| 2025-01-30T05:37:47
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I_kwDOJ0Z1Ps6CRwuZ
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How to install offline? Is there an installation package available for download
|
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| 2024-03-14T08:05:29
| 2024-03-14T20:26:34
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How to install offline? Is there an installation package available for download
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I_kwDOJ0Z1Ps6RQODd
| 6,068
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ollama serve --choice a model name
|
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| 2024-07-30T05:44:11
| 2024-07-31T01:39:26
| 2024-07-31T01:39:26
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NONE
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hi guys I have a problem with aggregation, I am using it on windows, when I pass ollama serve, how do I select the aggregation model? For example, the following operation
(base) PS C:\Users\Administrator> ollama serve --model codellama:7b
Error: unknown flag: --model
(base) PS C:\Users\Administrator>
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I_kwDOJ0Z1Ps6ix9Go
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nvcc compilation problem -- error: user-defined literal operator not found
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| 2025-01-10T21:35:32
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### What is the issue?
Compilation fails during 'make cuda_v12'
### Environment
```
$ /opt/cuda/bin/nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0
$ gcc --version
gcc (GCC) 14.2.1 20240910
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 565.77 Driver Version: 565.77 CUDA Version: 12.7 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce GTX 980 Off | 00000000:01:00.0 Off | N/A |
| 29% 31C P8 13W / 180W | 2MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
# Log
$ make cuda_v12 CUDA_12_PATH=/opt/cuda
/usr/bin/ccache /opt/cuda/bin/nvcc -c -Xcompiler -fPIC -D_GNU_SOURCE -fPIC -Wno-unused-function -std=c++11 -Xcompiler "-mavx" -t2 -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_MMV_Y=1 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_USE_CUDA=1 -DGGML_SHARED=1 -DGGML_BUILD=1 -DGGML_USE_LLAMAFILE -DK_QUANTS_PER_ITERATION=2 -DNDEBUG -D_GNU_SOURCE -D_XOPEN_SOURCE=600 -Wno-deprecated-gpu-targets --forward-unknown-to-host-compiler -use_fast_math -I./llama/ -O3 --generate-code=arch=compute_60,code=[compute_60,sm_60] --generate-code=arch=compute_61,code=[compute_61,sm_61] --generate-code=arch=compute_62,code=[compute_62,sm_62] --generate-code=arch=compute_70,code=[compute_70,sm_70] --generate-code=arch=compute_72,code=[compute_72,sm_72] --generate-code=arch=compute_75,code=[compute_75,sm_75] --generate-code=arch=compute_80,code=[compute_80,sm_80] --generate-code=arch=compute_86,code=[compute_86,sm_86] --generate-code=arch=compute_87,code=[compute_87,sm_87] --generate-code=arch=compute_89,code=[compute_89,sm_89] --generate-code=arch=compute_90,code=[compute_90,sm_90] --generate-code=arch=compute_90a,code=[compute_90a,sm_90a] -DGGML_CUDA_USE_GRAPHS=1 -o llama/build/linux-amd64/llama/ggml-cuda.cuda_v12.o llama/ggml-cuda.cu
/usr/include/c++/14.2.1/x86_64-pc-linux-gnu/bits/c++config.h(827): error: user-defined literal operator not found
typedef __decltype(0.0bf16) __bfloat16_t;
^
/usr/include/c++/14.2.1/type_traits(529): error: type name is not allowed
: public __bool_constant<__is_array(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(529): error: identifier "__is_array" is undefined
: public __bool_constant<__is_array(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(581): error: type name is not allowed
: public __bool_constant<__is_member_object_pointer(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(581): error: identifier "__is_member_object_pointer" is undefined
: public __bool_constant<__is_member_object_pointer(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(603): error: type name is not allowed
: public __bool_constant<__is_member_function_pointer(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(603): error: identifier "__is_member_function_pointer" is undefined
: public __bool_constant<__is_member_function_pointer(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(695): error: type name is not allowed
: public __bool_constant<__is_reference(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(695): error: identifier "__is_reference" is undefined
: public __bool_constant<__is_reference(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(731): error: type name is not allowed
: public __bool_constant<__is_object(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(731): error: identifier "__is_object" is undefined
: public __bool_constant<__is_object(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(760): error: type name is not allowed
: public __bool_constant<__is_member_pointer(_Tp)>
^
/usr/include/c++/14.2.1/type_traits(760): error: identifier "__is_member_pointer" is undefined
: public __bool_constant<__is_member_pointer(_Tp)>
^
/usr/include/c++/14.2.1/bits/utility.h(237): error: __type_pack_element is not a template
{ using type = __type_pack_element<_Np, _Types...>; };
^
/usr/include/c++/14.2.1/type_traits(138): error: class "std::enable_if<<error-constant>, void>" has no member "type"
using __enable_if_t = typename enable_if<_Cond, _Tp>::type;
^
detected during:
instantiation of type "std::__enable_if_t<<error-constant>, void>" at line 176
instantiation of "std::__detail::__or_fn" based on template arguments <std::is_reference<std::allocator<char>>, std::is_function<std::allocator<char>>, std::is_void<std::allocator<char>>, std::__is_array_unknown_bounds<std::allocator<char>>> at line 194
instantiation of class "std::__or_<_Bn...> [with _Bn=<std::is_reference<std::allocator<char>>, std::is_function<std::allocator<char>>, std::is_void<std::allocator<char>>, std::__is_array_unknown_bounds<std::allocator<char>>>]" at line 1195
instantiation of class "std::is_nothrow_default_constructible<_Tp> [with _Tp=std::allocator<char>]" at line 528 of /usr/include/c++/14.2.1/bits/basic_string.h
instantiation of "std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::basic_string() [with _CharT=char, _Traits=std::char_traits<char>, _Alloc=std::allocator<char>]" at line 4248 of /usr/include/c++/14.2.1/bits/basic_string.h
15 errors detected in the compilation of "llama/ggml-cuda.cu".
make[1]: *** [make/gpu.make:54: llama/build/linux-amd64/llama/ggml-cuda.cuda_v12.o] Error 255
make: *** [Makefile:50: cuda_v12] Error 2
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
git clone
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I_kwDOJ0Z1Ps6WS7V5
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|
Inconsistent Responses from Identical Models
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| 2024-09-12T07:11:03
| 2024-11-06T00:32:29
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### What is the issue?
I am new to Ollama and have noticed that when I ask a query using Ollama, the model's responses are quite poor. However, if I ask the same query using https://www.llama2.ai/, I receive much better responses. Can anyone explain what might be causing this difference? What could I be doing wrong?
```
# ! pip install -q pyautogen
# ! pip install -q 'litellm[proxy]'
import requests
import json
# Define the payload
payload = {
"model": "llama3:8b", # Changed model to match the curl version
"prompt": """How does a neural network work?""" ,
"format": "json", # To ensure response is in JSON format
"stream": False # To disable streaming in the response
}
# Send the POST request to the API
response = requests.post("http://127.0.0.1:11434/api/generate", json=payload)
output = response.json()
print(output)
```
`{'model': 'llama3:8b', 'created_at': '2024-09-12T06:26:59.001500912Z', 'response': '{ }\n\n ', 'done': True, 'done_reason': 'stop', 'context': [128006, 882, 128007, 271, 4438, 1587, 264, 30828, 4009, 990, 30, 128009, 128006, 78191, 128007, 271, 90, 557, 220], 'total_duration': 243709137, 'load_duration': 56054134, 'prompt_eval_count': 17, 'prompt_eval_duration': 39720000, 'eval_count': 4, 'eval_duration': 103353000}`
**Ollama model response:** response': '{ }\n\n
**URL: https://www.llama2.ai/**
**Model: Meta LLAMA3-8B**
**Response:**
I'd be happy to explain how a neural network works!
A neural network is a type of machine learning model inspired by the structure and function of the human brain. It's a complex system made up of many interconnected nodes or "neurons," which process and transmit information.
Here's a simplified overview of how a neural network works:
1. **Data Input**: The neural network receives input data, which can be images, sound waves, text, or any other type of data that can be digitized.
2. **Neuron Layers**: The input data is fed into the first layer of neurons, called the input layer. Each neuron in this layer receives a set of input values and performs a calculation on them.
3. **Activation Functions**: The output of each neuron is then passed through an activation function, which determines whether the neuron should "fire" or not. Common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).
4. **Hidden Layers**: The output from the input layer is then passed to one or more hidden layers, which are made up of neurons that perform complex calculations on the input data. Each hidden layer can have multiple neurons, and each neuron in the layer receives input from the previous layer.
5. **Output Layer**: The output from the hidden layers is then passed to the output layer, which produces the final output of the neural network.
6. **Training**: During training, the neural network is presented with a large dataset of input-output pairs. The network adjusts the weights and biases of the connections between neurons to minimize the error between its predictions and the actual output.
7. **Forward Propagation**: During forward propagation, the input data flows through the network, and the output is calculated at each layer.
8. **Backpropagation**: During backpropagation, the error between the predicted output and the actual output is calculated, and the error is propagated backwards through the network. This process helps the network adjust the weights and biases to improve its performance.
9. **Optimization**: The network uses an optimization algorithm, such as stochastic gradient descent (SGD), to adjust the weights and biases based on the error calculated during backpropagation.
10. **Repeat**: The process of forward propagation, backpropagation, and optimization is repeated multiple times until the network converges or reaches a desired level of accuracy.
Neural networks can be used for a wide range of applications, including image and speech recognition, natural language processing, and predictive modeling. They're particularly useful when dealing with complex, non-linear relationships between inputs and outputs.
I hope this helps! Do you have any specific questions about neural networks or would you like me to elaborate on any of these points?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
_No response_
|
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| 1,907,923,348
|
PR_kwDOJ0Z1Ps5a8JIo
| 570
|
fix HEAD request
|
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| 2023-09-21T23:41:17
| 2023-09-21T23:56:18
| 2023-09-21T23:56:17
|
CONTRIBUTOR
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HEAD request should respond like their GET counterparts except without a response body.
The previous implementation didn't quite satisfy this since it doesn't attach a response body so the content length is zero while the GET request responded with `Ollama is running`, content length 17.
Despite having a response body, gin will omit it in the actual response:
```
$ http HEAD :11434/
HTTP/1.1 200 OK
Content-Length: 17
Content-Type: text/plain; charset=utf-8
Date: Thu, 21 Sep 2023 23:41:08 GMT
$ http GET :11434/
HTTP/1.1 200 OK
Content-Length: 17
Content-Type: text/plain; charset=utf-8
Date: Thu, 21 Sep 2023 23:41:11 GMT
Ollama is running
```
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| 2,303,962,194
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I_kwDOJ0Z1Ps6JU6xS
| 4,511
|
Feature Request: Force-Off ROCm and CUDA builds in `gen_linux.sh` even if they are present.
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| 2024-05-18T09:38:09
| 2024-06-07T15:30:10
| 2024-05-21T20:24:55
|
NONE
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| null | null | null |
Ahoj,
there is a need to force a build without CUDA or ROCm, even if some of their libraries are present on the system.
But current `gen_linux.sh` forces ROCm build if some libraries are found, even if ROCm is not desired.
[Arch Linux AUR package `ollama-nogpu-git`](https://aur.archlinux.org/pkgbase/ollama-nogpu-git) has a [patch to patch out all CUDA and ROCm stuff in `gen_linux.sh`](https://aur.archlinux.org/cgit/aur.git/tree/disable-rocm-cuda.gen_linux.sh.patch?h=ollama-nogpu-git), but [this upstream here seems to change things too fast that it is not feasible to stay up to date with that patch](https://aur.archlinux.org/pkgbase/ollama-nogpu-git#comment-973477).
So I here wish for a feature to make it built-in that ROCm (and CUDA) build can be forced-off (e.g. by an environment variable or command line switch) _even if their libraries or just some of them_ are present. (On my system, I have only some stuff in `/opt/rocm/`, which is needed by some package, but is not a full and not a functional ROCm installation. This currently breaks building `ollama`.)
Regards!
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Provide a way to override system prompt at runtime
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| 2023-08-06T05:16:49
| 2023-08-08T04:56:23
| 2023-08-08T04:56:23
|
MEMBER
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```bash
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2",
"system": "You are a helpful assistant.",
"prompt": "hello"
}'
```
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C4AI Command
|
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| 2024-03-13T10:27:24
| 2024-04-15T15:36:36
| 2024-04-15T15:36:36
|
NONE
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Please add the c4ai-command model
Its really good at translation and can handle 100 languages.
https://huggingface.co/CohereForAI/c4ai-command-r-v01
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I_kwDOJ0Z1Ps6NFl3B
| 5,203
|
OLLAMA_MODELS is not honored after being changed (beyond the first time it is set)
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| 2024-06-21T18:28:08
| 2024-06-21T22:42:32
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NONE
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### What is the issue?
I set `OLLAMA_MODELS` to a new directory to ensure a large download would have space, and then tried to move `OLLAMA_MODELS` back to the default directory on Windows - and although the environmental variable is set, ollama continues to search in the previous `OLLAMA_MODELS` path, even after restarting all terminals or rebooting.
### OS
Windows (not via WSL)
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.44
|
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|
I_kwDOJ0Z1Ps5yCOwi
| 603
|
Considering graphql instead of classic http
|
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| 2023-09-26T10:19:39
| 2023-09-30T05:09:45
| 2023-09-30T05:09:45
|
NONE
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I won't explain here what [graphql](https://graphql.org) is.
## How does this project benefit from graphql vs classical http?
With graphql you can get more info / less info per http call. Why is this relevant? Third party integration.
Example with my own frontend:
- I want to get all models (1 call)
- I want to inspect all models (x calls)
I have right now 20 - 30 models and those I would make models + 1 call to the backend. Depending on the network conditions this would significantly impact loading time of the third party integration. Also I would always fetch all info, which isn't always wanted. Also I would always fetch "modified_at" for the all models, and for the model inspection I would always fetch "modelfile", "parameters" and "template". This increases the network payload size significantly
How does graphql help with this?
With graphql I can request a list of all models and related fields in a single field. Example:
```graphql
query getAllModels {
models {
name
size
details {
license
}
}
}
```
With this I can make a single http request and can get all the info I need and want.
Another plus is that there is a code generator available for many languages and framework.
For the ollama cli client this api doesn't make much sense.
This is a fundamental decision for the project and thus requires much thought before making a decision.
I'm looking forward to feedback on this
|
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I_kwDOJ0Z1Ps6marF0
| 8,450
|
ollama v0.5.6 /save bug？
|
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| 2025-01-16T08:23:31
| 2025-01-18T05:25:03
| 2025-01-18T05:25:03
|
NONE
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### What is the issue?
Excuse me, v0.5.6 version /save error.
Operating System: Rocky Linux 9.4.
The CPU architecture is x86.
v0.5.5 VS v0.5.6
### OS
Linux
### GPU
Intel
### CPU
Intel
### Ollama version
0.5.6
|
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I_kwDOJ0Z1Ps6mNpv_
| 8,430
|
Multi GPU, default GPU setting, specific model pin to specific GPU
|
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| 2025-01-15T01:08:46
| 2025-01-16T18:05:05
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I have a multi GPU configuration and they are different GPU models with different memory size.
I wish:
1. we could select default GPU for all models (potentially the fastest one with higher memory)
2. we could select specific model per specific GPU to use small models into small VRAM GPU and use fastest/highest_VRAM card for other large models.
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I_kwDOJ0Z1Ps6e0CGb
| 7,701
|
[question] Why are there such large differences in installation package sizes for different CPU architectures and systems?
|
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| 2024-11-16T14:31:49
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More than 10x
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| 2,002,338,841
|
PR_kwDOJ0Z1Ps5f6uPA
| 1,206
|
README: link to LangChainGo for talking to ollama, with an example
|
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| 2023-11-20T14:33:01
| 2023-11-20T15:35:07
| 2023-11-20T15:35:07
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PR_kwDOJ0Z1Ps51Bni3
| 5,616
|
fix: quant err message
|
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| 2024-07-10T22:30:02
| 2024-07-12T00:24:31
| 2024-07-12T00:24:29
|
CONTRIBUTOR
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`Error: quantization of this model is not supported by your version of Ollama. You may need to upgrade`
Resolves: https://github.com/ollama/ollama/issues/5531
|
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I_kwDOJ0Z1Ps6SEsKK
| 6,198
|
Request to Add JAIS 70B Model
|
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| 2024-10-22T13:32:44
| 2024-10-22T13:32:44
|
NONE
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**Description:**
I would like to suggest adding the JAIS 70B model to Ollama. The model is available on Hugging Face [here](https://huggingface.co/inceptionai/jais-adapted-70b) and offers significant advancements in Arabic-English natural language processing. It would be a valuable addition for users interested in Arabic-centric NLP applications.
**Reason for Request:**
The JAIS 70B model is specifically designed to handle complex Arabic-English tasks, and its availability on Ollama would allow for broader testing and application in various use cases. This addition would enhance the platform’s offerings for users working with Arabic and bilingual datasets.
**Reference:**
- [JAIS 70B on Hugging Face](https://huggingface.co/inceptionai/jais-adapted-70b)
**Additional Information:**
This model was recently launched by Inception under the G42 group and has been well-received for its large-scale capabilities and efficient training on a vast dataset, including 330 billion Arabic tokens.
Thank you for considering this suggestion!
|
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| 2,108,235,095
|
I_kwDOJ0Z1Ps59qR1X
| 2,277
|
How to set ROCR_VISIBLE_DEVICES to 0
|
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| 2024-01-30T16:10:19
| 2024-01-31T21:15:19
| 2024-01-31T21:15:19
|
NONE
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I have installed ollama (v0.1.22) and ROCm (v5.7.1) to Arch Linux via the following commands
```
pacman -S ollama rocm-hip-sdk rocm-opencl-sdk clblast
systemctl daemon-reload
systemctl enable ollama.service
systemctl start ollama.service
```
and then run
`ollama run mistral`
Checking `htop` and `nvtop`, I see that only CPU is being used.
Ollama log in `journalctl -u ollama` shows the following:
```
Searching for GPU management library libnvidia-ml.so
Discovered GPU libraries: []
Searching for GPU management library librocm_smi64.so
Discovered GPU libraries: [/opt/rocm/lib/librocm_smi64.so.5.0]
Radeon GPU detected
ROCm integrated GPU detected - ROCR_VISIBLE_DEVICES=1
```
I believe the `ROCR_VISIBLE_DEVICES` parameter should be set to `0`.
My system info is below:
```
-` misaligar@arch
.o+` ---------
`ooo/ OS: Arch Linux x86_64
`+oooo: Host: B650 AORUS ELITE AX
`+oooooo: Kernel: 6.7.2-arch1-1
-+oooooo+: Uptime: 18 mins
`/:-:++oooo+: Packages: 1065 (pacman), 7 (flatpak)
`/++++/+++++++: Shell: bash 5.2.26
`/++++++++++++++: Resolution: 2560x1440
`/+++ooooooooooooo/` DE: Plasma 5.27.10
./ooosssso++osssssso+` WM: kwin
.oossssso-````/ossssss+` Theme: [Plasma], Breeze [GTK2/3]
-osssssso. :ssssssso. Icons: kora [Plasma], kora [GTK2/3]
:osssssss/ osssso+++. Terminal: konsole
/ossssssss/ +ssssooo/- Terminal Font: Hack Nerd Font Mono 10
`/ossssso+/:- -:/+osssso+- CPU: AMD Ryzen 9 7900X (24) @ 5.733GHz
`+sso+:-` `.-/+oso: GPU: AMD ATI 13:00.0 Raphael
`++:. `-/+/ GPU: AMD ATI Radeon RX 7900 XT/7900 XTX
.` `/ Memory: 4811MiB / 63438MiB
```
How can I tell ollama to use the external GPU?
|
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| 2,702,895,284
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I_kwDOJ0Z1Ps6hGui0
| 7,874
|
Provide a slash command to clear screen.
|
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| 2024-11-28T17:54:54
| 2024-11-28T17:58:59
| 2024-11-28T17:56:21
|
NONE
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Hi! Is it possible to provide a slash command to clear the screen of the terminal chat window which appears when running a model with ollama run?
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I_kwDOJ0Z1Ps5zG7RU
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Currently create is recrating whole model , how to just update?
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I am tuning pramaeters nad i have to re run `ollama create name modelfile` . That remake the model . If there is no model file changes can i just update the parameters?
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PR_kwDOJ0Z1Ps5opVJh
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refactor model parsing
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CONTRIBUTOR
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- refactor metadata
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I_kwDOJ0Z1Ps6myRuD
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Requesting this new multimodal model.
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Please add the openbmb/MiniCPM-o-2_6 Model.
https://huggingface.co/openbmb/MiniCPM-o-2_6
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PR_kwDOJ0Z1Ps6DGnAU
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|
Update README.md
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Hi,
This is an update on the description of my Discord bot project - [vnc-lm](https://github.com/jake83741/vnc-lm) in the README.
Thank you,
Jake
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I_kwDOJ0Z1Ps6dMZ_B
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[FEAT] Add ollama installation selection
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| 2024-11-06T07:33:58
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My computer has only 46MB left on my `C:` and ollama installation program fails to install. I tried my best to get more space but I could not. It seems like the installation program does not support installation path selection, thus I suggest adding a path selection before installation.
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I_kwDOJ0Z1Ps6k3RiB
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Enable auto-save functionality via CLI flag
|
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| 2025-01-08T18:01:09
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NONE
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Having ollama models keep track of past conversations improves the quality of answers dramatically. Using `/save <model>` manually with every run is time-consuming, and prone to be forgotten. Allowing a flag similar to `ollama run --auto-save-on-exit <model>` would make this process a lot smoother. The solutions proposed on https://github.com/ollama/ollama/issues/1238 and https://github.com/ollama/ollama/issues/3800 are not sufficient to satisfy this feature request.
Thank you for the hard work already done 🙏
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I_kwDOJ0Z1Ps6OKFbo
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Does having the default quant type being Q4_0 (a legacy format) on the model hub still make sense?
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| 2024-07-02T01:28:02
| 2024-12-29T19:18:56
| 2024-12-29T19:18:56
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CONTRIBUTOR
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The Ollama model hub still has the default quant type of Q4_0 which is a legacy format that under-performs compared to K-quants (Qn_K, e.g. Q4_K_M, Q6_K, Q5_K_L etc...).
- Would it perhaps make sense to change the default quant to Q4_K_M for future models uploaded to the hub?
Reference
- https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix (Note that the legacy quant types don't even appear on the feature matrix).
- https://github.com/ggerganov/llama.cpp/wiki/Tensor-Encoding-Schemes
- https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/
- https://github.com/ggerganov/llama.cpp/discussions/406#discussioncomment-6176448
- https://github.com/ggerganov/llama.cpp/discussions/2094
- https://huggingface.co/datasets/christopherthompson81/quant_exploration
<img width="684" alt="image" src="https://github.com/ollama/ollama/assets/862951/2ab178bb-399e-4fb6-a628-9e60ffb694ab">
<img width="949" alt="image" src="https://github.com/ollama/ollama/assets/862951/520fcec7-1808-4893-ae02-401fee430e6e">
(Sorry if an issue already exists for this - if it did my search-foo let me down)
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PR_kwDOJ0Z1Ps51QMhb
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template: preprocess message and collect system
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