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PR_kwDOJ0Z1Ps5t4mC3
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windows: init `cmakeTargets` in `init_vars`
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PR_kwDOJ0Z1Ps5eK5xF
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support raw generation requests
|
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- add the optional `raw` generate request parameter to bypass prompt formatting and response context
Add a `raw` parameter to `/generate` requests that allow directly specifying the prompt without the Ollama server applying additional formatting.
```bash
curl -X "POST" -d '{"model":"mistral", "prompt": "[INST] hi [/INST]", "raw": true, "stream": false}' 'http://127.0.0.1:11434/api/generate'
```
Example use case, few-shot prompting:
```python
import requests
def call_generate_endpoint(prompt, model="mistral", raw=True, stream=False):
url = "http://127.0.0.1:11434/api/generate"
formatted_prompt = f"""[INST] This is awesome! [/INST]
Postive
[INST] This is bad! [/INST]
Negative
[INST] I love this movie [/INST]
Positive
[INST] {prompt} [/INST]
"""
payload = {
"model": model,
"prompt": formatted_prompt,
"raw": raw,
"stream": stream
}
response = requests.post(url, json=payload)
return response.json()
resp = call_generate_endpoint("I hate this book")
print(resp.response) # Negative
```
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PR_kwDOJ0Z1Ps5fMqVc
| 1,082
|
Add support for Multimodel models
|
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| 2023-11-11T02:57:28
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NONE
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|
Interactive cli usage:
```bash
/set image add <image id int> <path to image file>
Please tell me what text is in this photo [img-<image id int>]
```
For the API I added support for the `image_data` prop with the type of `[]{id: int, data: string(base64)}`for the generate endpoint.
To support this, modelfile now has a `MMPROJ` key that points to the mmproj file path.
Tested with the following modelfile:
```
FROM ./ggml-model-q4_k.gguf
TEMPLATE """
USER:{{ .Prompt }}
ASSISTANT:
"""
MMPROJ ./mmproj-model-f16.gguf
```
And using the following pre-quantatized model - https://huggingface.co/mys/ggml_llava-v1.5-13b.
<img width="1226" alt="Screenshot 2023-11-09 at 11 30 10 PM" src="https://github.com/jmorganca/ollama/assets/192433/ec62e5e7-999b-44f2-9a32-e6b85ca5b404">
<img width="567" alt="screenshot" src="https://github.com/jmorganca/ollama/assets/192433/681c5cd5-9140-43b1-8383-08823cdbb002">
|
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I_kwDOJ0Z1Ps6jqPBV
| 8,141
|
command r plus model update
|
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| 2024-12-17T18:48:33
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> https://docs.cohere.com/docs/command-r-plus
They already have updated there model can we have the updated new model in olllama thanks
|
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PR_kwDOJ0Z1Ps52Je6A
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|
Bump Go patch version
|
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| 2024-07-22T23:17:38
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Fixes #5774
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| 1,846,003,602
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PR_kwDOJ0Z1Ps5XruCs
| 321
|
length check for parameters
|
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| 2023-08-10T23:09:25
| 2023-08-10T23:23:11
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|
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PR_kwDOJ0Z1Ps5hN7DP
| 1,392
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chat api endpoint
|
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| 2023-12-05T18:57:45
| 2023-12-11T07:33:21
| 2023-12-05T19:57:33
|
CONTRIBUTOR
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|
- add a new `/api/chat` API endpoint that takes an array of `message` objects. This endpoint is an alternative to `/api/generate`.
- deprecate generation context and template, but continue to support them
- rebuild chat content from messages
This changes adds a `/api/chat` endpoint to the API which takes an array of messages. This makes modifying and tracking the history on the fly much simpler. It is an alternative to prompt/response.
`context` will continue to work as expected for now, but at some point in the future we may want to replace it completely with `/api/chat`.
```
curl -X POST http://localhost:11434/api/generate -d '{
"model": "mistral",
"prompt": "hello, how are you?"
}'
OR
### Basic generate request with messages
curl -X POST http://localhost:11434/api/chat -d '{
"model": "mistral",
"messages": [
{
"role": "user",
"content": "why is the sky blue?"
}
]
}'
```
resolves #981
resolves #1203
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I_kwDOJ0Z1Ps572dCD
| 1,940
|
`CUDA error 100` after detecting GPU libraries on system
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| 2024-02-19T19:50:24
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MEMBER
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It seems that upon detecting an Nvidia card, `ollama` may error with `CUDA error 100`:
```
Jan 11 15:37:50 LR9135SQP ollama[5616]: 2024/01/11 15:37:50 gpu.go:88: Detecting GPU type
Jan 11 15:37:50 LR9135SQP ollama[5616]: 2024/01/11 15:37:50 gpu.go:203: Searching for GPU management library libnvidia-ml.so
Jan 11 15:37:50 LR9135SQP ollama[5616]: 2024/01/11 15:37:50 gpu.go:248: Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.535.104.05 /usr/lib/wsl/lib/libnvidia-ml.so.1]
Jan 11 15:37:50 LR9135SQP ollama[5616]: 2024/01/11 15:37:50 gpu.go:259: Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.535.104.05: nvml vram init failure: 9
Jan 11 15:37:51 LR9135SQP ollama[5616]: 2024/01/11 15:37:51 gpu.go:94: Nvidia GPU detected
Jan 11 15:37:51 LR9135SQP ollama[5616]: 2024/01/11 15:37:51 gpu.go:135: CUDA Compute Capability detected: 7.5
```
```
Jan 11 15:55:41 LR9135SQP ollama[5616]: CUDA error 100 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:495: no CUDA-capable device is detected
Jan 11 15:55:41 LR9135SQP ollama[5616]: current device: 1881676272
Jan 11 15:55:41 LR9135SQP ollama[5616]: Lazy loading /tmp/ollama958766944/cuda/libext_server.so library
Jan 11 15:55:41 LR9135SQP ollama[5616]: GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:495: !"CUDA error"
```
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I_kwDOJ0Z1Ps6W39UJ
| 6,839
|
ollama request llama3.1 fail.
|
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| 2024-09-17T14:00:01
| 2024-10-28T07:39:53
| 2024-10-28T07:39:52
|
NONE
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### What is the issue?
like title i cant request llama3.1
on windows 10
it can be used one month ago,
cmd.exe:
C:\Users\123>ollama run llama3.1
2024/09/17 21:53:18 config.go:45: WARN invalid port, using default port="\\Users\\123\\AppData\\Local\\Programs\\Ollama\\ollama app.exe" default=11434
2024/09/17 21:53:18 config.go:45: WARN invalid port, using default port="\\Users\\123\\AppData\\Local\\Programs\\Ollama\\ollama app.exe" default=11434
Error: Head "http://C:11434/": dial tcp: lookup C: no such host
C:\Users\123 >ollama --version
2024/09/17 21:59:13 config.go:45: WARN invalid port, using default port="\\Users\\123\\AppData\\Local\\Programs\\Ollama\\ollama app.exe" default=11434
2024/09/17 21:59:13 config.go:45: WARN invalid port, using default port="\\Users\\123\\AppData\\Local\\Programs\\Ollama\\ollama app.exe" default=11434
Warning: could not connect to a running Ollama instance
Warning: client version is 0.3.9
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.9
|
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I_kwDOJ0Z1Ps5spXnn
| 220
|
Sending input with a + crashes
|
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Maybe an url escaping issue? I didn't dig much deeper but wanted to report.
```
$ ollama run llama2:13b
>>> I cannot send input with a + in it
Error: Post "http://127.0.0.1:11434/api/generate": EOF
```
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I_kwDOJ0Z1Ps6kf-w_
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Unexpected Connection Closure and GPU Memory Not Releasing
|
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### What is the issue?
### Problem Description
I am using Ubuntu 22.04 and making network requests to a local Ollama service with Python to run a series of models sequentially. After each model runs, it is unloaded using the following Python code. However, the task encounters an issue after reaching a certain point, where the Ollama network service unexpectedly stops when loading a model, resulting in the following error:
```
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 449, in _make_request
six.raise_from(e, None)
File "<string>", line 3, in raise_from
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 444, in _make_request
httplib_response = conn.getresponse()
File "/root/miniconda3/lib/python3.10/http/client.py", line 1374, in getresponse
response.begin()
File "/root/miniconda3/lib/python3.10/http/client.py", line 318, in begin
version, status, reason = self._read_status()
File "/root/miniconda3/lib/python3.10/http/client.py", line 287, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
http.client.RemoteDisconnected: Remote end closed connection without response
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.10/site-packages/requests/adapters.py", line 667, in send
resp = conn.urlopen(
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 787, in urlopen
retries = retries.increment(
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/util/retry.py", line 550, in increment
raise six.reraise(type(error), error, _stacktrace)
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/packages/six.py", line 769, in reraise
raise value.with_traceback(tb)
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 449, in _make_request
six.raise_from(e, None)
File "<string>", line 3, in raise_from
File "/root/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py", line 444, in _make_request
httplib_response = conn.getresponse()
File "/root/miniconda3/lib/python3.10/http/client.py", line 1374, in getresponse
response.begin()
File "/root/miniconda3/lib/python3.10/http/client.py", line 318, in begin
version, status, reason = self._read_status()
File "/root/miniconda3/lib/python3.10/http/client.py", line 287, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/autodl-tmp/EvalLLM/main.py", line 109, in <module>
generator.generate_and_save(section)
File "/root/autodl-tmp/EvalLLM/main.py", line 93, in generate_and_save
response_text = ollamaCaller.generate_response(prompt=question)
File "/root/autodl-tmp/EvalLLM/Caller/OllamaCaller.py", line 18, in generate_response
response = requests.post("http://localhost:11434/api/generate", json=payload)
File "/root/miniconda3/lib/python3.10/site-packages/requests/api.py", line 115, in post
return request("post", url, data=data, json=json, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/requests/adapters.py", line 682, in send
raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
```
This could be caused by the **service not responding for a long time**, or it might be due to the **daemon process being unexpectedly closed**, right? At the time of the error, The program was just finished loading a new model (**a relatively small model for my gpu, llama3:latest**), and the Ollama logs are as follows:
```log
time=2024-12-25T22:05:34.725+08:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loading" model=/root/autodl-tmp/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa gpu=GPU-30d02008-3980-d577-cd07-7e3fcabf1d31 parallel=4 available=25158156288 required="6.2 GiB"
time=2024-12-25T22:05:34.977+08:00 level=INFO source=server.go:104 msg="system memory" total="377.3 GiB" free="327.5 GiB" free_swap="0 B"
time=2024-12-25T22:05:34.977+08:00 level=INFO source=memory.go:356 msg="offload to cuda" layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[23.4 GiB]" memory.gpu_overhead="0 B" memory.required.full="6.2 GiB" memory.required.partial="6.2 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[6.2 GiB]" memory.weights.total="4.7 GiB" memory.weights.repeating="4.3 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-12-25T22:05:34.977+08:00 level=INFO source=server.go:376 msg="starting llama server" cmd="/usr/local/lib/ollama/runners/cuda_v12_avx/ollama_llama_server runner --model /root/autodl-tmp/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --n-gpu-layers 33 --threads 112 --parallel 4 --port 41461"
time=2024-12-25T22:05:34.978+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-12-25T22:05:34.978+08:00 level=INFO source=server.go:555 msg="waiting for llama runner to start responding"
time=2024-12-25T22:05:34.978+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server error"
time=2024-12-25T22:05:35.021+08:00 level=INFO source=runner.go:945 msg="starting go runner"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
time=2024-12-25T22:05:35.029+08:00 level=INFO source=runner.go:946 msg=system info="CUDA : ARCHS = 600,610,620,700,720,750,800,860,870,890,900 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 | cgo(gcc)" threads=112
time=2024-12-25T22:05:35.029+08:00 level=INFO source=.:0 msg="Server listening on 127.0.0.1:41461"
llama_load_model_from_file: using device CUDA0 (NVIDIA GeForce RTX 3090) - 23992 MiB free
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /root/autodl-tmp/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Meta-Llama-3-8B-Instruct
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 8192
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
time=2024-12-25T22:05:35.230+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.8000 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 8192
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.33 GiB (4.64 BPW)
llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOG token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CPU_Mapped model buffer size = 281.81 MiB
llm_load_tensors: CUDA0 model buffer size = 4155.99 MiB
llama_new_context_with_model: n_seq_max = 4
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_ctx_per_seq = 2048
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_per_seq (2048) < n_ctx_train (8192) -- the full capacity of the model will not be utilized
llama_kv_cache_init: CUDA0 KV buffer size = 1024.00 MiB
llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 2.02 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 560.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 24.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 2
```
The logs end here.
Regardless of the cause of this issue, after it occurs, **about 6GB of GPU memory remains unreleased and cannot be freed even by restarting Ollama**. Below is the output of `nvidia-smi`:
```
(base) root@autodl-container-d33848b29e-31b2d2f4:~# nvidia-smi
Thu Dec 26 22:29:15 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.78 Driver Version: 550.78 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3090 On | 00000000:10:00.0 Off | N/A |
| 30% 28C P8 17W / 350W | 6002MiB / 24576MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+
```
Has anyone encountered this issue? How can it be resolved? Thank you!
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.5.4
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Add basic mllama integration tests
|
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Draft until I run it against a few more systems and make sure the timeout values are reasonable
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I_kwDOJ0Z1Ps6lxt51
| 8,379
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Inquiry About Adding vikhyatk/moondream2 to Ollama
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Hi Ollama team, I was wondering if you plan to add the model vikhyatk/moondream2, which was just released yesterday, to your platform
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I_kwDOJ0Z1Ps5622TH
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[enhancement] Support FROM gpt-4:0314
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It would be nice if you could have a `Modelfile` that had an OpenAI model as a base. Then Ollama inference would call the OpenAI APIs instead of local inference with the built parameters. I know it seems like maybe it sorta defeats the point but I really like having the "Ollama facade" available. I use the March version of GPT-4 heavily but have to type "Let's think step by step" and all those other things all the time 😵💫
I could look at making a PR for this if there is interest. @jmorganca @mchiang0610
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PR_kwDOJ0Z1Ps5Wk6Px
| 226
|
refactor scan multiline for reuse
|
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| 2023-07-27T18:32:03
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It's not obvious values are ingested verbatim when not using multiline so `"` are included in the template. Instead, ingest the value inside the quotes
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I_kwDOJ0Z1Ps57hbDG
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Where is the model file stored?
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CONTRIBUTOR
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Hi there, I need to make a modification to the model file. Can you please tell me where do you store the model file?
Best regards
|
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`ollama create --quantize` does not show proper error if quantizing an unsupported model architecture
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### What is the issue?
When quantizing a model with `ollama create -q`, unknown model architecture errors are not properly bubbled up. E.g. from the logs:
```
llama_model_loader: - type f32: 121 tensors
llama_model_loader: - type f16: 162 tensors
llama_model_quantize: failed to quantize: unknown model architecture: 'chatglm'
```
User error:
```
% ollama create -f Modelfile -q q4_0 test
transferring model data
quantizing F16 model to Q4_0
Error: llama_model_quantize: 1
```
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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slow
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### What is the issue?
The program is very slow
3.10 ollama
### OS
Windows
### GPU
Intel
### CPU
Intel
### Ollama version
3.10
|
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I_kwDOJ0Z1Ps55YKjp
| 1,471
|
Publishing model fails with "Error: unable to push ..."
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| 2024-11-03T01:37:33
| 2024-01-20T00:25:23
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I followed the [import doc](https://github.com/jmorganca/ollama/blob/7a1b37ac64f0fb0585e279a0a840707843511ed3/docs/import.md?plain=1#L108) steps, but I'm getting the error in the title.
I've:
1. Created an account.
2. Copied my `id_ed25519.pub` file to `~/.ollama/` and `/usr/share/ollama/.ollama`.
3. Added the public key to my account.
4. Copied the model to my username's namespace.
5. Attempted `ollama push stephenwithav/book-summary`
```
Error: unable to push stephenwithav/book-summary, make sure this namespace exists and you are authorized to push to it
```
|
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PR_kwDOJ0Z1Ps5tx0Ne
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|
precalculate output tensor memory for metal and mmap
|
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| 2024-04-25T22:00:38
| 2024-04-25T23:34:18
| 2024-04-25T23:34:17
|
CONTRIBUTOR
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on metal with mmap, the output tensors are always allocated even if the offloaded layers < total layers + 1. other backends are unaffected
|
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I_kwDOJ0Z1Ps6IzXj2
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|
Ollama Api not working after adding format:"json"
|
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| 2024-05-14T11:23:41
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NONE
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### What is the issue?
Ollama api is giving empty response if i add add format as json
### OS
Windows
### GPU
AMD
### CPU
Intel
### Ollama version
0.1.37
|
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PR_kwDOJ0Z1Ps506G4S
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|
Remove `GGML_CUDA_FORCE_MMQ=on` from build
|
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| 2024-07-10T03:08:09
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MEMBER
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Removing this build flag seems to cause mmq to be decided at runtime vs always forcing it on. This stops some cuda calls from failing on V100 and other CC 7.0 cards. This may also help with [RDNA3 cards](https://github.com/ggerganov/llama.cpp/blob/a59f8fdc85e1119d470d8766e29617962549d993/docs/build.md?plain=1#L175)
Fixes https://github.com/ollama/ollama/issues/5571
Changes to mmq functionality related to this bug: https://github.com/ggerganov/llama.cpp/compare/7c26775...a8db2a9c#diff-b2fe862fcd5119199ae59ea13d1b6a46e0d23e41e727e39d90913f828a5ff66bR78
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PR_kwDOJ0Z1Ps5iZ9OH
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|
fix: set template without triple quotes
|
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[] | null | 1
| 2023-12-19T19:37:53
| 2024-01-09T17:36:26
| 2024-01-09T17:36:25
|
CONTRIBUTOR
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this changes updates `/set` to better handle multiline strings. `/set` now correctly sets template or system without using triple quotes
```
>>> /set template {{ .Prompt }}
Set prompt template.
```
additionally, use a strings.Builder instead of concatenating string values for prompt building
```
>>> """hello
... world"""
```
```
>>> """
... hello
... world
... """
```
```
>>> /set system """
... you are a llama
... """
```
```
>>> /set template """
... {{.System}}
... User: {{.Prompt}}
... Assistant: {{.Response}}
... """
```
resolves #1609
resolves #1607
|
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PR_kwDOJ0Z1Ps5Zn-ay
| 473
|
create manifests directory
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| 2023-09-06T00:12:07
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`ollama list` on a brand new install will panic because the manifests directory doesn't exist
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I_kwDOJ0Z1Ps52bOxT
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API for models on `ollama.com`
|
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| 2023-11-10T04:23:05
| 2024-11-21T17:25:46
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CONTRIBUTOR
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Hi I was wondering if you could add a way to either search for, or get a list of models available to pull off ollama.ai.
Currently the https://ollama.ai/library endpoint serves model information as HTML, it would be better if it was served as JSON so it could be better integrated into software
|
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Outputting the response leaves a bunch of control characters.
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NONE
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I get a bunch of control characters. How can I get a clean output?
_Originally posted by @pongnguy in https://github.com/ollama/ollama/issues/1710#issuecomment-2142294830_
It seems when you run something like: `ollama run granite-code:20b show me a python fuction that does fizzbuzz > out.txt` you end up with a bunch of:
```
^[[?25l^[[?25hThis^[[?25l^[[?25h function^[[?25l^[[?25h takes^[[?2
5l^[[?25h a^[[?25l^[[?25h positive^[[?25l^[[?25h integer^[[?25l^[[
?25h `^[[?25l^[[?25hn^[[?25l^[[?25h`^[[?25l^[[?25h as^[[?25l^[[?25
h input^[[?25l^[[?25h and^[[?25l^[[?25h prints^[[?25l^[[?25h the^[
[?25l^[[?25h numbers^[[?25l^[[?25h from^[[?25l^[[?25h ^[[?25l^[[?2
5h1^[[?25l^[[?25h to^[[?25l^[[?25h `^[[?25l^[[?25hn^[[?25l^[[?25h`
,^[[?25l^[[?25h replacing^[[?25l^[[?25h multip^[[?25l^[[?25hles^[[
?25l^[[?25h of^[[?25l^[[?25h ^[[?25l^[[?25h3^[[?25l^[[?25h with^[[
?25l^[[?25h "^[[?25l^[[?25hF^[[?25l^[[?25hizz^[[?25l^[[?25h",^[[?2
5l^[[?25h multip^[[?25l^[[?25hles^[[?25l^[[?25h of^[[?25l^[[?25h ^
[[?25l^[[?25h5^[[?25l^[[?25h with^[[?25l^[[?25h
```
Over and over. If you try to do the `--json` output, it's no better. It would be nice to have the simple text output with nothing else.
|
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llm: loosen format check to default to no format
|
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I_kwDOJ0Z1Ps57zTbw
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Ollama is running in background in MacOS
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Even if I exit the ollama app I can see the ollama among run processes
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I_kwDOJ0Z1Ps57OKRo
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Add cli switch to show generation time and tokens/sec output time
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Would it be possible to add a metrics switch to show net generation time and output time with tokens/seconds. This would make comparing the performance of LLMs easier.
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PR_kwDOJ0Z1Ps5vhZ4t
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add ability to create a client without env file
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If you use Ollama as a Go package, you are forced to use an environment variable as a configuration to change the host. With the CreateClient function, you are free to use your own configuration passed into the Ollama client.
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PR_kwDOJ0Z1Ps5_f5eG
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|
docs: use mingw32-make rather than MSYS Makefiles
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Since I'm trying to port ggml-sycl to llama package, MSYS Makefiles doesn't support icx compiler but MinGW Makefiles does, and it also works well with cpu, rocm and cuda. I think it's an ideal choice.
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I_kwDOJ0Z1Ps590TDt
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Manifest file?
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qua@equa-Swift-SF314-54:~$ ollama run orca
pulling manifest
Error: pull model manifest: file does not exist
equa@equa-Swift-SF314-54:~$
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I_kwDOJ0Z1Ps6SRppX
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Not executed in gpu amd rx 6750 GRE
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| 2024-09-03T19:10:36
| 2024-08-09T19:25:52
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NONE
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<img width="898" alt="image" src="https://github.com/user-attachments/assets/87a706b4-7749-443f-86ea-86a3c7de1cc1">
2024/08/08 14:17:22 routes.go:1108: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/Users/lsmir2/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[* http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR:]"
time=2024-08-08T14:17:22.106+08:00 level=INFO source=images.go:781 msg="total blobs: 5"
time=2024-08-08T14:17:22.106+08:00 level=INFO source=images.go:788 msg="total unused blobs removed: 0"
time=2024-08-08T14:17:22.107+08:00 level=INFO source=routes.go:1155 msg="Listening on 127.0.0.1:11434 (version 0.3.4)"
time=2024-08-08T14:17:22.113+08:00 level=WARN source=assets.go:100 msg="unable to cleanup stale tmpdir" path=/var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama1166271814 error="remove /var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama1166271814: directory not empty"
time=2024-08-08T14:17:22.114+08:00 level=WARN source=assets.go:100 msg="unable to cleanup stale tmpdir" path=/var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama2116948276 error="remove /var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama2116948276: directory not empty"
time=2024-08-08T14:17:22.114+08:00 level=WARN source=assets.go:100 msg="unable to cleanup stale tmpdir" path=/var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama3670480486 error="remove /var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama3670480486: directory not empty"
time=2024-08-08T14:17:22.115+08:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama4115735691/runners
time=2024-08-08T14:17:22.146+08:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2]"
time=2024-08-08T14:17:22.146+08:00 level=INFO source=types.go:105 msg="inference compute" id="" library=cpu compute="" driver=0.0 name="" total="32.0 GiB" available="16.1 GiB"
time=2024-08-08T14:17:24.808+08:00 level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=41 layers.offload=0 layers.split="" memory.available="[16.1 GiB]" memory.required.full="5.7 GiB" memory.required.partial="0 B" memory.required.kv="320.0 MiB" memory.required.allocations="[5.7 GiB]" memory.weights.total="4.6 GiB" memory.weights.repeating="4.1 GiB" memory.weights.nonrepeating="485.6 MiB" memory.graph.full="561.0 MiB" memory.graph.partial="789.6 MiB"
time=2024-08-08T14:17:24.809+08:00 level=INFO source=server.go:392 msg="starting llama server" cmd="/var/folders/3j/0tc5g9350n128f02l1tm845m0000gn/T/ollama4115735691/runners/cpu_avx2/ollama_llama_server --model /Users/lsmir2/.ollama/models/blobs/sha256-816441b33390807d429fbdb1de7e33bb4d569ac68e2203bdbca5d8d79b5c7266 --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 57883"
time=2024-08-08T14:17:24.816+08:00 level=INFO source=sched.go:445 msg="loaded runners" count=1
time=2024-08-08T14:17:24.816+08:00 level=INFO source=server.go:592 msg="waiting for llama runner to start responding"
time=2024-08-08T14:17:24.817+08:00 level=INFO source=server.go:626 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=3535 commit="1e6f6554" tid="0x107f92600" timestamp=1723097844
INFO [main] system info | n_threads=6 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="0x107f92600" timestamp=1723097844 total_threads=6
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="6" port="57883" tid="0x107f92600" timestamp=1723097844
llama_model_loader: loaded meta data with 23 key-value pairs and 283 tensors from /Users/lsmir2/.ollama/models/blobs/sha256-816441b33390807d429fbdb1de7e33bb4d569ac68e2203bdbca5d8d79b5c7266 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = chatglm
llama_model_loader: - kv 1: general.name str = codegeex4-all-9b
llama_model_loader: - kv 2: chatglm.context_length u32 = 131072
llama_model_loader: - kv 3: chatglm.embedding_length u32 = 4096
llama_model_loader: - kv 4: chatglm.feed_forward_length u32 = 13696
llama_model_loader: - kv 5: chatglm.block_count u32 = 40
llama_model_loader: - kv 6: chatglm.attention.head_count u32 = 32
llama_model_loader: - kv 7: chatglm.attention.head_count_kv u32 = 2
llama_model_loader: - kv 8: chatglm.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 9: general.file_type u32 = 2
llama_model_loader: - kv 10: chatglm.rope.dimension_count u32 = 64
llama_model_loader: - kv 11: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 12: chatglm.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = chatglm-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,151552] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,151552] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,151073] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 151329
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 151329
llama_model_loader: - kv 20: tokenizer.ggml.eot_token_id u32 = 151336
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 151329
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - type f32: 121 tensors
llama_model_loader: - type q4_0: 161 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-08-08T14:17:25.069+08:00 level=INFO source=server.go:626 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 223
llm_load_vocab: token to piece cache size = 0.9732 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = chatglm
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151552
llm_load_print_meta: n_merges = 151073
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 2
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 16
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 13696
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 5000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 9B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 9.40 B
llm_load_print_meta: model size = 5.08 GiB (4.64 BPW)
llm_load_print_meta: general.name = codegeex4-all-9b
llm_load_print_meta: EOS token = 151329 '<|endoftext|>'
llm_load_print_meta: UNK token = 151329 '<|endoftext|>'
llm_load_print_meta: PAD token = 151329 '<|endoftext|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 151336 '<|user|>'
llm_load_print_meta: max token length = 1024
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: CPU buffer size = 5196.84 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 5000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 320.00 MiB
llama_new_context_with_model: KV self size = 320.00 MiB, K (f16): 160.00 MiB, V (f16): 160.00 MiB
llama_new_context_with_model: CPU output buffer size = 2.38 MiB
llama_new_context_with_model: CPU compute buffer size = 561.01 MiB
llama_new_context_with_model: graph nodes = 1606
llama_new_context_with_model: graph splits = 1
INFO [main] model loaded | tid="0x107f92600" timestamp=1723097848
time=2024-08-08T14:17:28.352+08:00 level=INFO source=server.go:631 msg="llama runner started in 3.54 seconds"
[GIN] 2024/08/08 - 14:17:33 | 200 | 8.515598533s | 127.0.0.1 | POST "/v1/chat/completions"
|
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"followers_url": "https://api.github.com/users/dhiltgen/followers",
"following_url": "https://api.github.com/users/dhiltgen/following{/other_user}",
"gists_url": "https://api.github.com/users/dhiltgen/gists{/gist_id}",
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"received_events_url": "https://api.github.com/users/dhiltgen/received_events",
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https://github.com/ollama/ollama/pull/5511
| 2,393,285,539
|
PR_kwDOJ0Z1Ps50kwRU
| 5,511
|
llm: fix missing dylibs by restoring old build behavior on Linux and macOS
|
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[] | null | 0
| 2024-07-06T01:00:34
| 2024-07-08T16:55:51
| 2024-07-06T01:48:32
|
MEMBER
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| 2,657,322,190
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I_kwDOJ0Z1Ps6eY4TO
| 7,660
|
on ollama model specifics web sites , add comaptible copypaste <name>:<tag> text
|
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"url": "https://api.github.com/repos/ollama/ollama/labels/ollama.com",
"name": "ollama.com",
"color": "ffffff",
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closed
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"followers_url": "https://api.github.com/users/hoyyeva/followers",
"following_url": "https://api.github.com/users/hoyyeva/following{/other_user}",
"gists_url": "https://api.github.com/users/hoyyeva/gists{/gist_id}",
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| 2024-11-14T02:16:13
| 2024-12-01T07:31:54
| 2024-11-15T02:24:09
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NONE
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eg , user visits page of a model
chooses select list , and selects some model version
there on that version of model page i want
say , for qwen2.5-coder.on its 14b-instruct-q4_K_M page
simply qwen2.5-coder:14b-instruct-q4_K_M
name:tag
like hf s use that button with ollama copypaste run cmd in
oh and yea , peeferably also a small ' copy me ' button left on it
greets
|
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I_kwDOJ0Z1Ps5yQrxe
| 630
|
Error: failed to start a llama runner
|
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When I run
ollama run mistral
it downloads properly but then fails to run it, with the following error:
Error: failed to start a llama runner
I'm running this on my intel mbp with 64g ram
|
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PR_kwDOJ0Z1Ps5svQOm
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|
better checking for OLLAMA_HOST variable
|
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| 2024-04-15T23:39:21
| 2024-04-29T23:14:08
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CONTRIBUTOR
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This change adds better validation to the `OLLAMA_HOST` variable when used with `ollama serve`. It should work with both IPv4 and IPv6, and includes unit tests.
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I_kwDOJ0Z1Ps52qIDy
| 1,109
|
Exploring Multimodal LLMs: Incorporating Image as Input in ModelFile.
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Hi, I'd like to experiment with multimodal language models that can handle both images and text as input. Is there a way to input an image.
My ModelFile looks like this
```
FROM ./my_model.gguf
PARAMERTER..
SYSTEM You are An Artist describe the image
```
I'm trying to run this on macos.
M1 chip
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I_kwDOJ0Z1Ps6GGkVB
| 3,728
|
ollama prune
|
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I think ollama needs a prune command, something similar to `docker system prune`. Just to keep the environment tidy
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Exception using command-r-plus:104b-q4_0
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### What is the issue?
Ollama: exception done_getting_tensors: wrong number of tensors; expected 642, got 514
### What did you expect to see?
_No response_
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
_No response_
### Architecture
_No response_
### Platform
_No response_
### Ollama version
_No response_
### GPU
_No response_
### GPU info
_No response_
### CPU
_No response_
### Other software
_No response_
|
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I_kwDOJ0Z1Ps6AnK09
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|
Subsequent generation requests hang after successful generation request with `num_predict: 0`
|
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| 2024-02-27T22:54:24
| 2024-05-10T01:13:35
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NONE
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If you submit a generation request with `num_predict: 0`, the request will be handled successfully but all subsequent generation requests will hang indefinitely regardless of their `num_predict` values.
Below is an example of how to reproduce this behavior:
```
curl http://localhost:11434/api/generate -d '{
"model": "vicuna:13b-16k",
"template":"Hello world!",
"stream":false,
"num_predict":0
}'
```
Any requests following this will not return until the daemon has been restarted.
Also to note is that the daemon might be in an unresponsive state following this request, I've had to `kill` it each time it's happened to me so far, but haven't tested this against typical behavior for signals sent mid-generation or otherwise.
I think it also affects `raw` mode, and neither `num_keep` or `num_ctx` seem to be related.
|
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| 2,069,374,142
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PR_kwDOJ0Z1Ps5jatea
| 1,849
|
Accomodate split cuda lib dir
|
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| 2024-01-08T00:24:45
| 2024-02-06T00:01:17
| 2024-02-06T00:01:17
|
CONTRIBUTOR
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Makes it a little easier to compile when cuda lib dir is split up as in nixos.
|
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I_kwDOJ0Z1Ps6Wb9l3
| 6,786
|
Isn't it time to move onto Omni models?
|
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| 2024-09-13T05:34:46
| 2024-10-23T17:17:40
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NONE
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There is a model that I found today called
*LLaMa 3.1 8b Omni* and it is a speech to speech model with very low latency ensuring the best experience for local models.
But as for Ollama it doesn't support such models, although VLMs are there but having these Omni models on your local device is just 🤌🏻 too Good to be true.
HF reference for llama 3.1 8b Omni:
https://huggingface.co/ICTNLP/Llama-3.1-8B-Omni
Looking forward to @ollama team's implementation
Thanks🙏🏻
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llm: suppress large allocations for GGUF arrays
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This introduces a little array type for holding GGUF arrays that prevents the array from growing too large. It preserves the total size of the array, but limits the number of elements that are actually allocated.
GGUF arrays that are extremely large, such as tokens, etc, are generally uninteresting to users, and are not worth the memory overhead, and the time spent allocating and freeing them. They are necessary for inference, but not for inspection.
The size of these arrays is, however, important in Ollama, so it is preserved in a separate field on array.
|
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I_kwDOJ0Z1Ps50M1bL
| 836
|
Support setting parameters without a modelfile
|
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I know you can set temperature using modelfile or api, but it would be much more convenient to be able to do it in cli.
For example:
`ollama run llama-2 -temperture 0.1` when loading
and/or
`/set temperature 0.1` in runtime
Thanks for your consideration!
|
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I_kwDOJ0Z1Ps6Ktitm
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|
Running multiple models simultaneously, always using one card
|
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### What is the issue?
Running multiple models simultaneously, always using one card,but i have 4 cards and download 4model
When multiple users are using it at the same time, always clear the first card and then load other models instead of using my other idle cards
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.33
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PR_kwDOJ0Z1Ps5wRyOi
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Add new community integration (TypingMind)
|
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| 2024-05-23T04:31:39
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|
NONE
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Cheers!
|
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Serve static files
|
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Image Embeddings
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CONTRIBUTOR
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rough work
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I_kwDOJ0Z1Ps6RkuCv
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Add Gemma 2 2b base/ text/ pre-trained model to registry
|
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Apologies, if I am missing something but can you upload quants for [base 2b](https://huggingface.co/google/gemma-2-2b) as well?
All I see are instruct ones [here](https://ollama.com/library/gemma2/tags)
Thanks!
|
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Ensure `nvidia` and `nvidia_uvm` kernel modules are loaded in `install.sh` script and at startup
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Make sure the `nvidia` and `nvidia_uvm` kernel modules are loaded on install.
Nvidia has a daemon that takes care of that `nvidia-persistenced` for restarts, so add both to its config file as well
Lastly, make sure the kernel modules are loaded when re-running the install script, even if drivers are already installed.
Fixes https://github.com/ollama/ollama/issues/4563
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PR_kwDOJ0Z1Ps5kBthD
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Fix typo in arm mac arch script
|
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I_kwDOJ0Z1Ps6D_Mw-
| 3,401
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Document `OLLAMA_DEBUG` in `ollama serve` `-h` docs
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### What are you trying to do?
_No response_
### How should we solve this?
_No response_
### What is the impact of not solving this?
_No response_
### Anything else?
_No response_
|
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I_kwDOJ0Z1Ps56R0bu
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gpu issues
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warning: gpu support may not be enabled, check that you have installed GPU drivers: nvidia-smi command failed
i have installed all drivers for my gpu:1070ti
still nothing maybe i installed the wrong drivers can someone point me in the right direction
|
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PR_kwDOJ0Z1Ps5oQuDE
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|
update llama.cpp submodule commit to `87c91c07`
|
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I_kwDOJ0Z1Ps55kwiC
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macOS environment variable not working
|
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Hello - thanks for the great repository. I wanted to alert you to the fact that the OLLAMA_MODELS path appears to be having no impact. This is true for `pull`, `rull` and serving.
```
$ echo $OLLAMA_MODELS
(prints appropriate directory)
$ ollama run <model>
(downloads to ~/.ollama/..)
$ OLLAMA_MODELS=<directory> run <model>
(downloads to ~/.ollama/...)
```
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I_kwDOJ0Z1Ps6Lmuoj
| 4,947
|
Cannot update ollama on windows through "restart to update"
|
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| 2024-06-09T11:52:04
| 2024-06-10T11:42:01
| 2024-06-10T11:42:01
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NONE
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### What is the issue?
cannot update ollama on windows through clicking "restart to update" at taskbar, it has no response
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.41
|
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I_kwDOJ0Z1Ps6Qt4nz
| 5,918
|
Llama3.1 70b-instruct-q4_1 buggy
|
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| 2024-07-24T15:54:38
| 2024-09-15T17:35:22
| 2024-09-15T17:18:31
|
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### What is the issue?
When I run the **70b-instruct-q4_1** version of Llama3.1 ollama gives a buggy reply:
My sample request:
> ➜ ollama-tests curl http://localhost:11434/api/chat -d '{
"model": "llama3.1:70b-instruct-q4_1",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Tell me about the top 3 commercial industrial computer vision products on the market."
}
], "stream": false
}'
The model's response
> {
"model": "llama3.1:70b-instruct-q4_1",
"created_at": "2024-07-24T15:45:23.026538Z",
"message": {
"role": "assistant",
"content": "assistant\nassistantassistantassistant"
},
"done_reason": "stop",
"done": true,
"total_duration": 56814340833,
"load_duration": 53209095791,
"prompt_eval_count": 37,
"prompt_eval_duration": 2107466000,
"eval_count": 6,
"eval_duration": 1493717000
}`
The same request generates a proper response using **llama3.1:8b-instruct-q8_0**.
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.2.8
|
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I_kwDOJ0Z1Ps5_PoTD
| 2,496
|
default num_thread incorrect on some large core count system (non-hyperthreading)
|
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| 2024-02-14T17:10:39
| 2024-08-05T22:20:08
| 2024-08-05T22:20:08
|
NONE
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I have tested Ollama on different machines yet, but no matter how many cores or RAM I have, it's only using 50% of the cores and just a very few GB of RAM.
For example now I'm running `ollama rum llama2:70b` on 16 core server with 32 GB of RAM, but while prompting only eight cores are used and just around 1 GB of RAM.
Is there something wrong? In the models descriptions are aleways warning you neet at least 8,16,32,... GB of RAM.
|
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I_kwDOJ0Z1Ps6GTSXr
| 3,754
|
ollama rm Support deletion through ID
|
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| 2024-04-19T14:48:40
| 2024-04-19T14:48:40
| null |
NONE
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Just like Docker RM supports deletion through ID, have you considered supporting model deletion through ID? If so, I am willing to research this area and submit a PR
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| 1,824,949,943
|
PR_kwDOJ0Z1Ps5Wk5I4
| 225
|
add stop conditions
|
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| 2023-07-27T18:28:50
| 2023-07-28T00:20:57
| 2023-07-28T00:20:56
|
CONTRIBUTOR
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resolves #140
resolves #217
|
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I_kwDOJ0Z1Ps56UsdK
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|
shell autocompletion
|
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NONE
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hi, I would like to add shell autocompletion for ollama to my linux distribution.
I dont know go but the cli parser seems to be using https://github.com/spf13/cobra, the same as github CLI `gh`.
The nix package generates the autocompletion like this:
https://github.com/teto/nixpkgs/blob/dbaa028d61848fda534a04ce21459b722f0bca81/pkgs/applications/version-management/gh/default.nix#L30
so I tried `ollama completion -s zsh` hoping it was not shown by `ollama --help` because hidden but that failed.
Is there a way to generate those files currently and if yes, how, if not, could you add such a subcommand please ? makes writing commands on the prompt easier.
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I_kwDOJ0Z1Ps6DtfGR
| 3,365
|
Unable to make cors work in docker container
|
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### What is the issue?
I have been trying to get cors to work inside a docker container with no luck so far. Attached is my docker-compose.yaml for starting ollama. I have tried many variations on the OLLAMA_HOST and OLLAMA_ORIGINS env vars to no luck. Does anyone know the magic combo to make it work?
### What did you expect to see?
hitting it from a browser directly works fine, from code works fine, using Ollama chats https://github.com/drazdra/ollama-chats hosted on another node runs into cors fun:
curling on the local box shows anything set as origin is blocked:
```bash
gman@ai:~/wrk/ai-runtime$ curl -i -H 'Origin:http://10.22.22.1' localhost:11434/
HTTP/1.1 403 Forbidden
Date: Wed, 27 Mar 2024 03:20:24 GMT
Content-Length: 0
gman@ai:~/wrk/ai-runtime$ curl localhost:11434
Ollama is running
```
### Steps to reproduce
docker-compose.yaml
```
version: '3.6'
services:
ollama:
container_name: ollama
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
image: ollama/ollama
#tty: true
restart: unless-stopped
environment:
- OLLAMA_ORIGINS="*"
- OLLAMA_HOST="0.0.0.0:11434"
ports:
- 11434:11434
volumes:
- ./ollama:/root/.ollama:cached
```
### Are there any recent changes that introduced the issue?
_No response_
### OS
Linux
### Architecture
amd64
### Platform
Docker
### Ollama version
0.1.29
### GPU
Nvidia
### GPU info
_No response_
### CPU
AMD
### Other software
_No response_
|
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PR_kwDOJ0Z1Ps5nqz9L
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|
update README to add Gemma 2B, 7B model in Model Library Table
|
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| 2024-02-22T16:36:57
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@pfrankov @jmorganca Kindly Review
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I_kwDOJ0Z1Ps6BJcnv
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|
System Monitor detected that internet is used by Ollama.
|
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What does it send/receive? I was using it through MacOS terminal, installed the app from the website.
|
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I_kwDOJ0Z1Ps59RXuD
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ollama is stuck when translate language
|
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ollama is stuck when i ask to translate language.
In my case, i put it in like '한글로' for prompt which means 'in Korean' after English output.
Sometimes ollama could translate perfectly and stable, but mostly ollama is stuck.
I think if there is a problem in text generation, It should be pause not is broken.
In this case, **i have to restart ollama**.
I guess it is about string format from streaming.
i am using docker image from docker hub.
using this command 'docker exec -it ollama ollama run mistral'
or api.
version: v0.1.21 (i have been tested from v0.1.18~)
model: mistral, mixtral latest, llama2 etc.
sorry for no logs.
But it is easy to cause problems when you put '한글로' in prompt.
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Added a minimalist React UI for Ollama models to ReadME.md
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I discussed it a few times in the discord, and a few people seem to be using it, so it would be good to add.
Demo video:
https://github.com/jmorganca/ollama/assets/35015261/d50f7036-cdf2-44ed-9bb0-fdbed6a4ec66
I'll be maintaining/improving it a lot over the coming weeks, and some contributors reached out to get involved.
It can now handle markdown, etc. And continues to improve beyond the initial demo.
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I_kwDOJ0Z1Ps5_pUu3
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Running on GPU
|
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| 2024-02-19T05:27:39
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Hello,
It seems, the response time of llama2:7b is slow on my linux machine. I am not sure if the code
is running on Nvidia card.
In a python code, how to ensure that Ollama models run on GPU?
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PR_kwDOJ0Z1Ps51nl1k
| 5,742
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Added code to use swap memory in linux
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I_kwDOJ0Z1Ps6TZ5Yl
| 6,420
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Is the speed of the Olama running model related to the CUDA version?
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### What is the issue?
I deployed qwen2:72B with the latest version of Ollama, but I found that the loading speed of Ollama models varies greatly under different nvidia driver version.
# driver:535.183.06 | cuda version:12.2
ollama version:0.3.4
Time of Loading Model:29s
# driver:515.105.01 | cuda version:11.7
ollama version:0.3.6
Time of Loading Model:659s
## GPU :A800
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
_No response_
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I_kwDOJ0Z1Ps55EOm5
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Make `notus` model available on `ollama`
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| 2023-12-07T16:53:09
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We just hosted our [Argilla community meetup](https://www.youtube.com/playlist?list=PLBmuFBJ5cjcbsr49KFoC4DQoo3ZWT7q_d) and got the question if we would be able to make [Notus](https://argilla.io/blog/notus7b/) available on ollama. What would it take, and how can we help with this?
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I_kwDOJ0Z1Ps56fG7W
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|
Possible to increase speed / efficiency of model?
|
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NONE
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I'm trying out the Dolphin-Mixture model and it's quite fun, but really slow. (my specs are 64gb 3200mj ram, i7 4.5gh cpu and a 1080 ti)
but still it takes some time to start answering and when it starts it writes at maybe 2-3 words a second. Is it possible to make improvements to this?
|
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PR_kwDOJ0Z1Ps5ukaTU
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Add support for the BSD platforms
|
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This PR adds the ability to compile and run Ollama on various BSD platforms, specifically DragonFly, FreeBSD, NetBSD and OpenBSD. A couple notes:
1. It includes support for Vulkan to offload work to the GPU.
2. It's only been tested on OpenBSD, but hopefully interested parties in this community can test the other platforms.
3. `ollama run` is broken on OpenBSD. The [readline](readline) package uses `syscall` which OpenBSD [has removed from libc and the kernel](https://marc.info/?l=openbsd-tech&m=169841790407370&w=2). Instead, the readline package should be updated to use `golang.org/x/sys/unix`, similar to how [ergochat/readline](https://github.com/ergochat/readline) does it. I didn't want to include that work in this PR.
4. The tweak in `gen_common.sh` is to remove a bash'ism so that the file can be sourced from `ksh`.
On OpenBSD I needed to add these packages:
```pkg_add cmake go vulkan-headers```
The other platforms will probably have similarly named packages to install.
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PR_kwDOJ0Z1Ps6FwPej
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|
Update go.mod
|
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CONTRIBUTOR
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Fixed the CVE-2024-45337 severity on wiz scan for the docker image, changing the version of golang.org/x/crypto to the latest version should fix the issue
|
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I_kwDOJ0Z1Ps6XR06i
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|
"/show parameters" command causes crashes when running Qwen 2.5 models, on version 0.3.11
|
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### What is the issue?
This only happens after changing the parameters through /set parameter command.
Here's an example:
PS H:\ztmp> ollama run qwen2.5
>>> /show parameters
No parameters were specified for this model.
>>> /set parameter top_k 1
Set parameter 'top_k' to '1'
>>> /show parameters
error: couldn't get model
Error: something went wrong, please see the ollama server logs for details
PS H:\ztmp>
This is on windows 11.
Here's the error message from the ollama serve terminal tab:
2024/09/20 08:47:43 [Recovery] 2024/09/20 - 08:47:43 panic recovered:
assignment to entry in nil map
runtime/map_faststr.go:205 (0x7a93ba)
github.com/ollama/ollama/server/routes.go:807 (0x12cc57e)
github.com/ollama/ollama/server/routes.go:732 (0x12cb497)
github.com/gin-gonic/gin@v1.10.0/context.go:185 (0x1287cca)
github.com/ollama/ollama/server/routes.go:1076 (0x12d0c14)
github.com/gin-gonic/gin@v1.10.0/context.go:185 (0x1295d39)
github.com/gin-gonic/gin@v1.10.0/recovery.go:102 (0x1295d27)
github.com/gin-gonic/gin@v1.10.0/context.go:185 (0x1294e64)
github.com/gin-gonic/gin@v1.10.0/logger.go:249 (0x1294e4b)
github.com/gin-gonic/gin@v1.10.0/context.go:185 (0x1294291)
github.com/gin-gonic/gin@v1.10.0/gin.go:633 (0x1293d00)
github.com/gin-gonic/gin@v1.10.0/gin.go:589 (0x1293831)
net/http/server.go:2688 (0xafaecc)
net/http/server.go:3142 (0xafc6cd)
net/http/server.go:2044 (0xaf79c7)
runtime/asm_amd64.s:1695 (0x8026a0)
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.11
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PR_kwDOJ0Z1Ps5euED0
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Add ModelFusion community integration
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llm: reorder gguf tensors
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| 2024-07-12T22:38:11
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CONTRIBUTOR
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Crashing or gibberish output on 3x Radeon GPUs
|
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### What is the issue?
While running mixtral:8x7b-instruct-v0.1-q4_K_M on my physical machine with x3 7900 XTX I got this error:
```
[root@5dc6ecf27031 /]# ollama run mixtral:8x7b-instruct-v0.1-q4_K_M
Error: llama runner process has terminated: signal: segmentation fault (core dumped)
[root@5dc6ecf27031 /]#
```
Logs:
```
[GIN] 2024/07/11 - 13:22:44 | 200 | 16.23µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/07/11 - 13:22:44 | 200 | 7.724554ms | 127.0.0.1 | POST "/api/show"
time=2024-07-11T13:22:44.297Z level=INFO source=sched.go:754 msg="new model will fit in available VRAM, loading" model=/root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8 library=rocm parallel=4 required="33.2 GiB"
time=2024-07-11T13:22:44.298Z level=INFO source=memory.go:309 msg="offload to rocm" layers.requested=-1 layers.model=33 layers.offload=33 layers.split=11,11,11 memory.available="[24.0 GiB 24.0 GiB 24.0 GiB]" memory.required.full="33.2 GiB" memory.required.partial="33.2 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[11.3 GiB 11.3 GiB 10.6 GiB]" memory.weights.total="25.5 GiB" memory.weights.repeating="25.4 GiB" memory.weights.nonrepeating="102.6 MiB" memory.graph.full="1.3 GiB" memory.graph.partial="1.3 GiB"
time=2024-07-11T13:22:44.299Z level=INFO source=server.go:375 msg="starting llama server" cmd="/tmp/ollama1419561683/runners/rocm_v60101/ollama_llama_server --model /root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8 --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 4 --tensor-split 11,11,11 --tensor-split 11,11,11 --port 41695"
time=2024-07-11T13:22:44.299Z level=INFO source=sched.go:474 msg="loaded runners" count=1
time=2024-07-11T13:22:44.299Z level=INFO source=server.go:563 msg="waiting for llama runner to start responding"
time=2024-07-11T13:22:44.299Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="a8db2a9" tid="140134008951616" timestamp=1720704164
INFO [main] system info | n_threads=16 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | 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 = 0 | " tid="140134008951616" timestamp=1720704164 total_threads=32
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="41695" tid="140134008951616" timestamp=1720704164
llama_model_loader: loaded meta data with 26 key-value pairs and 995 tensors from /root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = mistralai
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.expert_count u32 = 8
llama_model_loader: - kv 10: llama.expert_used_count u32 = 2
llama_model_loader: - kv 11: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 12: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: general.file_type u32 = 15
llama_model_loader: - kv 14: tokenizer.ggml.model str = llama
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,58980] = ["▁ t", "i n", "e r", "▁ a", "h e...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type f16: 32 tensors
llama_model_loader: - type q8_0: 64 tensors
llama_model_loader: - type q4_K: 833 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 259
llm_load_vocab: token to piece cache size = 0.1637 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 8x7B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 46.70 B
llm_load_print_meta: model size = 24.62 GiB (4.53 BPW)
llm_load_print_meta: general.name = mistralai
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: max token length = 48
time=2024-07-11T13:22:44.549Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server loading model"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 3 ROCm devices:
Device 0: Radeon RX 7900 XTX, compute capability 11.0, VMM: no
Device 1: Radeon RX 7900 XTX, compute capability 11.0, VMM: no
Device 2: Radeon RX 7900 XTX, compute capability 11.0, VMM: no
llm_load_tensors: ggml ctx size = 1.53 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: ROCm0 buffer size = 8608.53 MiB
llm_load_tensors: ROCm1 buffer size = 8608.53 MiB
llm_load_tensors: ROCm2 buffer size = 7928.49 MiB
llm_load_tensors: ROCm_Host buffer size = 70.31 MiB
time=2024-07-11T13:23:03.566Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server not responding"
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
time=2024-07-11T13:23:04.460Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server loading model"
llama_kv_cache_init: ROCm0 KV buffer size = 352.00 MiB
llama_kv_cache_init: ROCm1 KV buffer size = 352.00 MiB
llama_kv_cache_init: ROCm2 KV buffer size = 320.00 MiB
llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB
llama_new_context_with_model: ROCm_Host output buffer size = 0.55 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model: ROCm0 compute buffer size = 640.01 MiB
llama_new_context_with_model: ROCm1 compute buffer size = 640.01 MiB
llama_new_context_with_model: ROCm2 compute buffer size = 640.02 MiB
llama_new_context_with_model: ROCm_Host compute buffer size = 72.02 MiB
llama_new_context_with_model: graph nodes = 1510
llama_new_context_with_model: graph splits = 4
time=2024-07-11T13:23:06.864Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server error"
[GIN] 2024/07/11 - 13:23:07 | 500 | 22.834580361s | 127.0.0.1 | POST "/api/chat"
time=2024-07-11T13:23:07.115Z level=ERROR source=sched.go:480 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault (core dumped) "
time=2024-07-11T13:23:12.116Z level=WARN source=sched.go:671 msg="gpu VRAM usage didn't recover within timeout" seconds=5.001085328 model=/root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8
time=2024-07-11T13:23:12.366Z level=WARN source=sched.go:671 msg="gpu VRAM usage didn't recover within timeout" seconds=5.251122065 model=/root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8
time=2024-07-11T13:23:12.616Z level=WARN source=sched.go:671 msg="gpu VRAM usage didn't recover within timeout" seconds=5.500799906 model=/root/.ollama/models/blobs/sha256-3a17f7cde150070bbc815645693fb93c311cc42e7deaf198364acadcf08458f8
```
I am running this docker version
docker run -d --restart unless-stopped --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11442:11434 --name dvz3 ollama/ollama:0.2.1-rocm
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.2.1-rocm
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I_kwDOJ0Z1Ps56hn9h
| 1,707
|
[Bug] Calling embedding endpoint within short time will receive `None`
|
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| 2023-12-25T11:44:12
| 2024-05-06T23:44:40
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I'm currently using `llama_index` to make LLM do a document QA,
but I notice the embedding endpoint often output:
```json
{"embedding":null}
```
when I do `condense_question` chat mode which calls LLM to rephrase question (and match context) before actually answer.
Like:
```python
llm = Ollama(model="dolphin2.2-mistral:7b-q4_K_M")
ollama_embedding = OllamaEmbedding(
model_name="dolphin2.2-mistral:7b-q4_K_M",
ollama_additional_kwargs={"mirostat": 0},
)
service_context = ServiceContext.from_defaults(llm=llm, embed_model=ollama_embedding)
chat_engine = index.as_chat_engine(service_context=service_context,chat_mode='condense_plus_context')
resp= chat_engine.chat('abc')
print(resp)
# Often happens when LLM is called second time
resp= chat_engine.chat('cde')
print(resp)
```
I guess it is probably due to ollama or llama.cpp needs to handle different behavior in the same time?
Because if I modify `llama_index` to check for `null` and retry, the above code will work:
```python
## In llama_index.embeddings.OllamaEmbedding
while True:
response = requests.post(
url=f"{self.base_url}/api/embeddings",
headers={"Content-Type": "application/json"},
json=ollama_request_body,
)
response.encoding = "utf-8"
if response.status_code != 200:
optional_detail = response.json().get("error")
raise ValueError(
f"Ollama call failed with status code {response.status_code}."
f" Details: {optional_detail}"
)
if response.json()["embedding"]:
break
```
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[Model request]GOT-OCR2.0
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Error: llama runner process has terminated: error loading model: unable to allocate CUDA0 buffer (4x L40S, 384GB system RAM, Deepseek-R1)
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### What is the issue?
I am aware that 4 x L40S only has 192GB of VRAM, but the machine also has 384GB of system RAM. The error seems to indicate that 45108 MiB of RAM is being allocated with cudaMalloc and this is failing. This is very close to the GPU limit (46068 MiB). On my home setup (2x P40, admittedly not trying such a huge model) the GPU never gets close to its VRAM limit (typically only 22GB out of 24GB).
```
Jan 26 17:48:20 ip-172-31-3-6 ollama[1418]: llm_load_print_meta: rope_yarn_log_mul = 0.1000
Jan 26 17:50:35 ip-172-31-3-6 ollama[1418]: ggml_backend_cuda_buffer_type_alloc_buffer: allocating 45108.64 MiB on device 0: cudaMalloc failed: out of memory
Jan 26 17:50:35 ip-172-31-3-6 ollama[1418]: llama_model_load: error loading model: unable to allocate CUDA0 buffer
Jan 26 17:50:35 ip-172-31-3-6 ollama[1418]: llama_load_model_from_file: failed to load model
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: panic: unable to load model: /usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: goroutine 34 [running]:
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: github.com/ollama/ollama/llama/runner.(*Server).loadModel(0xc0001a0000, {0x1b, 0x0, 0x0, 0x0, {0xc000194090, 0x4, 0x4}, 0xc00018a060, 0x0}, ...)
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: github.com/ollama/ollama/llama/runner/runner.go:852 +0x3ad
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: created by github.com/ollama/ollama/llama/runner.Execute in goroutine 1
Jan 26 17:51:23 ip-172-31-3-6 ollama[1418]: github.com/ollama/ollama/llama/runner/runner.go:970 +0xd0d
Jan 26 17:51:24 ip-172-31-3-6 ollama[1418]: time=2025-01-26T17:51:24.077Z level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server error"
Jan 26 17:51:24 ip-172-31-3-6 ollama[1418]: time=2025-01-26T17:51:24.578Z level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: error loading model: unable to allocate CUDA0 buffer\nllama_load_model_from_file: failed to load model"
Jan 26 17:51:24 ip-172-31-3-6 ollama[1418]: [GIN] 2025/01/26 - 17:51:24 | 500 | 3m6s | 127.0.0.1 | POST "/api/generate"
Jan 26 17:51:30 ip-172-31-3-6 ollama[1418]: time=2025-01-26T17:51:30.190Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.6116838510000004 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1>
Jan 26 17:51:30 ip-172-31-3-6 ollama[1418]: time=2025-01-26T17:51:30.947Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=6.369278328 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
Jan 26 17:51:31 ip-172-31-3-6 ollama[1418]: time=2025-01-26T17:51:31.707Z level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=7.128851916 model=/usr/share/ollama/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9
Jan 26 17:51:40 ip-172-31-3-6 ollama[1418]: [GIN] 2025/01/26 - 17:51:40 | 200 | 65.972µs | 127.0.0.1 | GET "/api/version"
ubuntu@ip-172-31-3-6:~$ ollama --version
ollama version is 0.5.7
ubuntu@ip-172-31-3-6:~$ free
total used free shared buff/cache available
Mem: 390837004 4152012 385439624 3248 1245368 383763992
Swap: 0 0 0
ubuntu@ip-172-31-3-6:~$ nvidia-smi
Sun Jan 26 17:48:08 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.144.03 Driver Version: 550.144.03 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA L40S On | 00000000:38:00.0 Off | 0 |
| N/A 22C P8 22W / 350W | 4MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA L40S On | 00000000:3A:00.0 Off | 0 |
| N/A 22C P8 21W / 350W | 4MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA L40S On | 00000000:3C:00.0 Off | 0 |
| N/A 22C P8 44W / 350W | 4MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 3 NVIDIA L40S On | 00000000:3E:00.0 Off | 0 |
| N/A 23C P8 22W / 350W | 4MiB / 46068MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
```
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.5.7
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Show supported types list in help message for `ollama create`
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This PR adds supported types list to the help message for `ollama create`
```console
$ go run main.go create -h
Create a model from a Modelfile
Usage:
ollama create MODEL [flags]
Flags:
-f, --file string Name of the Modelfile (default "Modelfile")
-h, --help help for create
-q, --quantize string Quantize model to this level (e.g. q4_0)
Supported types:
BF16, F16, F32, IQ1_M, IQ1_S, IQ2_M
IQ2_S, IQ2_XS, IQ2_XXS, IQ3_S, IQ3_XS, IQ3_XXS
IQ4_NL, IQ4_XS, Q2_K, Q2_K_S, Q3_K_L, Q3_K_M
Q3_K_S, Q4_0, Q4_1, Q4_1_F16, Q4_K_M, Q4_K_S
Q5_0, Q5_1, Q5_K_M, Q5_K_S, Q6_K, Q8_0
Environment Variables:
OLLAMA_HOST IP Address for the ollama server (default 127.0.0.1:11434)
```
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|
remove unused openssh key types
|
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PR_kwDOJ0Z1Ps5_94Uy
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Fix deepseek deseret regex
|
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| 2024-10-25T23:37:50
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On windows compiled with gcc the c++ regex library failed to handle the characters
Without any changes, loading the model in the Go server crashes with
```
llama_model_load: error loading model: error loading model vocabulary: wstring_convert::from_bytes
```
The patch for unicode.cpp gets past the wide character conversion problem, but then hits
```
Regex error: Invalid range in bracket expression.
```
Switching to the `\U<8hexchars>` syntax to capture the range for Deseret resolves the regex problem.
Fixes #7311
|
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PR_kwDOJ0Z1Ps5vVSj2
| 4,416
|
don't abort when an invalid model name is used in /save
|
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| 2024-05-14T01:42:17
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| 2024-05-14T01:48:28
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Fixes #3852
|
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I_kwDOJ0Z1Ps6AfwTg
| 2,774
|
What is the different between /api/generate and /api/chat?
|
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I mean if I give them the same prompt and input, the answers will be the same. Right? Then why they are two different API?
Or is chat auto context? I mean when using /api/chat, the answer will automatically include the previous conversation? And the /api/generate only answer for the present?
|
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PR_kwDOJ0Z1Ps6GLO5h
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Remove redundant image_url format check in fromChatRequest
|
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| 2024-12-24T15:22:37
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Improves the `fromChatRequest` function by removing redundant checks for Ollama's direct string format and improving error messages for better debugging.
Summary of changes:
- Removed the redundant check for `image_url` in Ollama format.
- Clarified and enhanced error messages in the conversion logic.
|
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PR_kwDOJ0Z1Ps6JDQsP
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|
readme: add AI Toolkit for VSCode to community integrations
|
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| 2025-01-27T08:34:26
| 2025-01-30T13:12:11
| 2025-01-27T08:36:23
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CONTRIBUTOR
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Add [AI Toolkit for VSCode](https://marketplace.visualstudio.com/items?itemName=ms-windows-ai-studio.windows-ai-studio) to community integrations.
AI Toolkit for VSCode ollama support: https://aka.ms/ai-tooklit/ollama-docs
|
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PR_kwDOJ0Z1Ps5o0Jnk
| 2,949
|
add api.NewClient
|
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| 2024-03-06T09:31:58
| 2024-05-04T08:35:08
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fix #2948
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| 2,024,279,782
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I_kwDOJ0Z1Ps54qA7m
| 1,374
|
Out of memory error on model that previously worked fine after update to version 0.1.13
|
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| 2023-12-04T16:40:23
| 2024-01-08T21:42:02
| 2024-01-08T21:42:02
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NONE
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I configured a model to run entirely in VRAM using the following Modelfile:
```
FROM deepseek-coder:33b-instruct-q5_K_S
PARAMETER num_gpu 65
PARAMETER num_ctx 2048
```
I had no issues with running this, it would use about 22GB of my 4090's 24GB VRAM without issue. It would generate responses very quickly which was very helpful for getting quick answers to short coding queries.
However, yesterday I updated Ollama (to 0.1.13), and now I cannot run the same model. I get an out of memory error, despite the model not needing more than 22.5GB (according to the logs below).
I run Ollama on a headless linux server, so there are no other applications using the GPU.
Was there an update that changes how much VRAM Ollama allocates to make it need more than before? Is there a way to configure Ollama so that it behaves the same way as before?
EDIT: Reverting back to ollama version 0.1.11 resolves the issue for now.
Error:
```
Dec 04 16:28:20 osm-server ollama[528776]: llm_load_tensors: offloaded 65/65 layers to GPU
Dec 04 16:28:20 osm-server ollama[528776]: llm_load_tensors: VRAM used: 21741.89 MiB
Dec 04 16:28:23 osm-server ollama[528776]: ....................................................................................................
Dec 04 16:28:23 osm-server ollama[528776]: llama_new_context_with_model: n_ctx = 2048
Dec 04 16:28:23 osm-server ollama[528776]: llama_new_context_with_model: freq_base = 100000.0
Dec 04 16:28:23 osm-server ollama[528776]: llama_new_context_with_model: freq_scale = 0.25
Dec 04 16:28:24 osm-server ollama[528776]: llama_kv_cache_init: offloading v cache to GPU
Dec 04 16:28:24 osm-server ollama[528776]: llama_kv_cache_init: offloading k cache to GPU
Dec 04 16:28:24 osm-server ollama[528776]: llama_kv_cache_init: VRAM kv self = 496.00 MiB
Dec 04 16:28:24 osm-server ollama[528776]: llama_new_context_with_model: kv self size = 496.00 MiB
Dec 04 16:28:24 osm-server ollama[528776]: llama_build_graph: non-view tensors processed: 1430/1430
Dec 04 16:28:24 osm-server ollama[528776]: llama_new_context_with_model: compute buffer total size = 273.07 MiB
Dec 04 16:28:24 osm-server ollama[528776]: llama_new_context_with_model: VRAM scratch buffer: 270.00 MiB
Dec 04 16:28:24 osm-server ollama[528776]: llama_new_context_with_model: total VRAM used: 22507.89 MiB (model: 21741.89 MiB, context: 766.00 MiB)
Dec 04 16:28:24 osm-server ollama[600735]: {"timestamp":1701707304,"level":"INFO","function":"main","line":2917,"message":"HTTP server listening","hostname":"127.0.0.1","port":57264}
Dec 04 16:28:24 osm-server ollama[600735]: {"timestamp":1701707304,"level":"INFO","function":"log_server_request","line":2478,"message":"request","remote_addr":"127.0.0.1","remote_port":46990,"status":200,"method":"HEAD","path":"/","params":{}}
Dec 04 16:28:24 osm-server ollama[528776]: 2023/12/04 16:28:24 llama.go:493: llama runner started in 4.401485 seconds
Dec 04 16:28:24 osm-server ollama[528776]: CUDA error 2 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:5884: out of memory
Dec 04 16:28:24 osm-server ollama[528776]: current device: 0
Dec 04 16:28:25 osm-server ollama[528776]: 2023/12/04 16:28:25 llama.go:436: 2 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:5884: out of memory
Dec 04 16:28:25 osm-server ollama[528776]: current device: 0
Dec 04 16:28:25 osm-server ollama[528776]: 2023/12/04 16:28:25 llama.go:510: llama runner stopped successfully
Dec 04 16:28:25 osm-server ollama[528776]: [GIN] 2023/12/04 - 16:28:25 | 200 | 6.468638351s | 127.0.0.1 | POST "/api/generate"
```
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I_kwDOJ0Z1Ps51Mhg0
| 938
|
CLI windows size detection not accurate on WSL
|
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| 2023-10-27T21:33:22
| 2024-02-20T00:56:13
| 2024-02-20T00:56:13
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CONTRIBUTOR
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When backspacing characters from a long input on wsl a "float extra character" may not be deleted.
Reproduce by going to a newline and backspacing from the last character.
<img width="717" alt="Screenshot 2023-10-27 at 17 30 48" src="https://github.com/jmorganca/ollama/assets/5853428/9e18a84b-3071-4b67-aaba-43f2e2d4c1f4">
<img width="716" alt="Screenshot 2023-10-27 at 17 31 12" src="https://github.com/jmorganca/ollama/assets/5853428/029e7b25-d49a-435e-b320-773a22d81369">
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PR_kwDOJ0Z1Ps5rcjJg
| 3,458
|
fix model needLoad always be true
|
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[] | null | 1
| 2024-04-02T14:26:53
| 2024-04-03T06:05:55
| 2024-04-03T06:05:54
|
CONTRIBUTOR
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loaded.Options.Runner.NumGPU was change to 33 or other value by
``
func New(model string, adapters, projectors []string, opts *api.Options)
``
so `!reflect.DeepEqual(loaded.Options.Runner, opts.Runner)` will always be true, because` opts.Runner.NumGPU` which default to -1
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I_kwDOJ0Z1Ps5zHZCc
| 729
|
Unable to pull models behind the proxy
|
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| 2023-10-07T11:38:39
| 2024-10-01T11:45:58
| 2023-10-10T16:59:07
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Dear Maintainers,
Thank you very much for creating this project!
I need to set up ollama on Linux behind a proxy, and when pulling I get an error:
```download.go:166: couldn't download blob: Get "https:///...../ollama/docker/registry/v2/blobs/...": tls: first record does not look like a TLS handshake```
I have tried these methods and they also did not work for me:
- https://github.com/jmorganca/ollama/issues/703#issuecomment-1747857562
- https://github.com/jmorganca/ollama/issues/676#issuecomment-1744722380
- https://github.com/jmorganca/ollama/issues/697
Could you please add a way to configure a proxy for Ollama? That will enable a lot of users that must use a proxy.
Thank you very much in advance.
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PR_kwDOJ0Z1Ps5x2EeC
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cmd: initial functionality for `ollama show <model>`
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| 2024-06-08T00:44:51
| 2024-06-15T03:03:48
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I_kwDOJ0Z1Ps6KF8rJ
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Repeating answers in an instance.
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| 2024-05-25T07:58:33
| 2024-05-29T22:52:59
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NONE
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### What is the issue?
Hi, i'm using llama2 models, and when i asked the Ai to explain something, it does responds and explain it, but when it reached the end, it instead print out certain line,
```
[INST: <</INST]
```
then repeats another answer regarding the current questions.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama 0.1.38
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I_kwDOJ0Z1Ps6SbYxp
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Pull Command Parsing Not Working
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### What is the issue?
When running ollama on Windows, attempt to run 'ollama pull llama3.1' results in 'ollama pull llama3.1
pulling manifest
Error: Incorrect function.'
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama --version ollama version is 0.3.4
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for glm4-9b
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| 2024-08-19T05:54:49
| 2024-08-19T05:54:49
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NONE
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Do you have any plans to support the tool-calling function of glm4-9b?
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PR_kwDOJ0Z1Ps5umAi_
| 4,194
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docs: delete log `HEAD`
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| 2024-05-06T05:28:47
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CONTRIBUTOR
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I_kwDOJ0Z1Ps57N7tI
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Code view on codellama vs phi and dolphin-phi
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| 2024-03-13T23:53:54
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Dolphin phi and (probably phi code indent):
Phi, indents but has no code view:
Can someone probably do something to improve these models as they are the only models that run very fast on smaller GPUs.
Or perhaps, maybe someone would train phi-code:instruct.
Thanks.
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