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https://api.github.com/repos/ollama/ollama/issues/4091
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I_kwDOJ0Z1Ps6Hki1F
| 4,091
|
Unable to access ollama from other machine
|
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### What is the issue?
Hi Im new to AI or develop in general and I had a question:
When I try to access on the same machine then it work normally but when I Try to connect from other machine it doesn't allow me to do that how can I allow it to connect?
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.32
|
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PR_kwDOJ0Z1Ps54lYil
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|
doc: fixed spelling error
|
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| 2024-08-16T14:16:08
| 2024-09-04T13:42:33
| 2024-09-04T13:42:33
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CONTRIBUTOR
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|
Changed "dorrect" to "correct".
|
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I_kwDOJ0Z1Ps6O852N
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ggml_cuda_init: failed to initialize CUDA: system has unsupported display driver / cuda driver combination
|
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### What is the issue?
```bash
time=2024-07-09T13:56:46.484Z level=INFO source=sched.go:738 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa gpu=GPU-72b1bc75-c26b-1c04-f9cd-ff1942a73215 parallel=4 available=24748556288 required="6.2 GiB"
time=2024-07-09T13:56:46.484Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[23.0 GiB]" 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=2024-07-09T13:56:46.485Z level=INFO source=server.go:375 msg="starting llama server" cmd="/tmp/ollama368855320/runners/cuda_v11/ollama_llama_server --model /root/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 4 --port 39141"
time=2024-07-09T13:56:46.485Z level=INFO source=sched.go:474 msg="loaded runners" count=1
time=2024-07-09T13:56:46.485Z level=INFO source=server.go:563 msg="waiting for llama runner to start responding"
time=2024-07-09T13:56:46.486Z 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="139659088556032" timestamp=1720533406
INFO [main] system info | n_threads=32 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="139659088556032" timestamp=1720533406 total_threads=64
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="63" port="39141" tid="139659088556032" timestamp=1720533406
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /root/.ollama/models/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, ...
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
time=2024-07-09T13:56:46.737Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server loading model"
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: 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: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
ggml_cuda_init: failed to initialize CUDA: system has unsupported display driver / cuda driver combination
llm_load_tensors: ggml ctx size = 0.14 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: CPU buffer size = 4437.80 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 = 500000.0
llama_new_context_with_model: freq_scale = 1
ggml_cuda_host_malloc: failed to allocate 1024.00 MiB of pinned memory: system has unsupported display driver / cuda driver combination
llama_kv_cache_init: CPU 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
ggml_cuda_host_malloc: failed to allocate 2.02 MiB of pinned memory: system has unsupported display driver / cuda driver combination
llama_new_context_with_model: CPU output buffer size = 2.02 MiB
ggml_cuda_host_malloc: failed to allocate 560.01 MiB of pinned memory: system has unsupported display driver / cuda driver combination
llama_new_context_with_model: CUDA_Host compute buffer size = 560.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
INFO [main] model loaded | tid="139659088556032" timestamp=1720533410
time=2024-07-09T13:56:50.631Z level=INFO source=server.go:609 msg="llama runner started in 4.15 seconds"
[GIN] 2024/07/09 - 13:56:50 | 200 | 4.429415046s | 127.0.0.1 | POST "/api/chat"
[GIN] 2024/07/09 - 13:57:41 | 200 | 8.412510746s | 127.0.0.1 | POST "/api/chat"
```
```bash
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Fri_Jan__6_16:45:21_PST_2023
Cuda compilation tools, release 12.0, V12.0.140
Build cuda_12.0.r12.0/compiler.32267302_0
nvidia-smi
Tue Jul 9 13:52:27 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 On | 00000000:A2:00.0 Off | Off |
| 30% 34C P8 23W / 450W | 1MiB / 24564MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.2.1
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I_kwDOJ0Z1Ps6SHGVr
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[question] How to default to CPU?
|
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### What is the issue?
I created the FreeBSD port for ollama.
However, GPU isn't available and all 'ollama run' commands fail with the ollama server printing this:
```
time=2024-08-06T09:57:27.238-07:00 level=WARN source=sched.go:642 msg="gpu VRAM usage didn't recover within timeout" seconds=5.06509013 model=/home/yuri/.ollama/models/blobs/sha256-ff82381e2bea77d91c1b824c7afb83f6fb73e9f7de9dda631bcdbca564aa5435
```
It appears to default to GPU.
```ollama help run``` and ```ollama help start``` don't offer any options allowing to default to CPU.
How can I make ollama to default to CPU?
Thank you,
Yuri
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.4
|
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I_kwDOJ0Z1Ps54bADJ
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|
German umlaut missing with deepseek-llm
|
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Here are the responses for few models and deepseek-llm cannot output "ö" and "ü":
```
%ollama run orca2:13b "Please repeat: wäre, Tür, höchstens"
wäre, Tür, höchstens
Translation: would be, door, at most
%ollama run codellama:34b "Please repeat: wäre, Tür, höchstens"
Wäre, Tür, höchstens.
%ollama run deepseek-llm:67b-chat "Please repeat: wäre, Tür, höchstens"
To complete this task, I will first listen to the audio file provided and write down the German words that are spoken. Then,
I will repeat those words in a clear manner for you.
Step 1: Listen to the audio file and identify the German words being spoken. In this case, the words are "wäre", "Tr"
(door), and "hchstens" (at most).
Step 2: Repeat each word in a clear manner.
- wäre -> I would say this as "vare".
- Tr -> Pronounced like "tuer", which means door.
- hchstens -> This is pronounced like "hkhs-tens" and it translates to "at most."
```
Is this a problem of the model or with ollama ?
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PR_kwDOJ0Z1Ps5iBhyg
| 1,524
|
restore model load duration on generate response
|
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| 2023-12-14T17:01:29
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- set model load duration on generate and chat done response
- calculate createAt time when response created
resolves #1523
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I_kwDOJ0Z1Ps56uOdy
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|
Server doesn't listen on all available interfaces
|
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I think this might be a problem recently introduced in v0.1.17 but I'm not 100% sure.
`ollama serve` doesn't listen on `0.0.0.0` and therefore doesn't make itself available on all interfaces. This causes problems when trying to connect to it via an interface other than `localhost`.
A (hopefully temporary) workaround is using a utility like `socat`, e.g. to listen on all interfaces on port `8888` and relay traffic to port `11434`:
```
$ socat TCP-LISTEN:8888,reuseaddr,fork TCP:localhost:11434
```
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PR_kwDOJ0Z1Ps6BduJt
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Update type for ToolFunction to support new json serialization
|
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Need to update the `ToolFunction` type to support the tool passing from client libraries
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I_kwDOJ0Z1Ps551XNg
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Error Ollama + Langchain + Google Colab + ngrok
|
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| 2023-12-15T16:40:27
| 2024-03-11T18:47:23
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When I use the combination: Ollama + Langchain + Google Colab + ngrok. I get an error
(The models are downloaded, I can see them in Ollama list)
```
llm = Ollama(
model="run deepseek-coder:6.7b", base_url="https://e12b-35-231-226-171.ngrok.io/")
responce = llm.predict('What do you know about Falco?')
Output exceeds the [size limit](command:workbench.action.openSettings?%5B%22notebook.output.textLineLimit%22%5D). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?5f7f2031-a63a-42c0-ac20-ccc8d53de6b2)---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/requests/models.py:971](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/requests/models.py:971), in Response.json(self, **kwargs)
970 try:
--> 971 return complexjson.loads(self.text, **kwargs)
972 except JSONDecodeError as e:
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/__init__.py:514](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/__init__.py:514), in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, allow_nan, **kw)
510 if (cls is None and encoding is None and object_hook is None and
511 parse_int is None and parse_float is None and
512 parse_constant is None and object_pairs_hook is None
513 and not use_decimal and not allow_nan and not kw):
--> 514 return _default_decoder.decode(s)
515 if cls is None:
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/decoder.py:389](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/decoder.py:389), in JSONDecoder.decode(self, s, _w, _PY3)
388 if end != len(s):
--> 389 raise JSONDecodeError("Extra data", s, end, len(s))
390 return obj
JSONDecodeError: Extra data: line 1 column 5 - line 1 column 19 (char 4 - 18)
During handling of the above exception, another exception occurred:
...
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
--> 975 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
JSONDecodeError: Extra data: line 1 column 5 (char 4)
```
If I run from the terminal Ollama + Google Colab + ngrok, everything works with google colab and ngrok. Also, if I change the Python script to local base_url:
```
llm = Ollama(
model="run deepseek-coder:6.7b", base_url="http://localhost:11434")
responce = llm.predict('What do you know about Falco?')
```
everything works Ollama + Langchain.
Only the combination Ollama + Langchain + Google Colab + ngrok does not work
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I_kwDOJ0Z1Ps6AAR2U
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Gemma 7B produces gibberish output
|
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| 2024-02-21T19:48:59
| 2024-04-17T11:11:24
| 2024-02-23T01:26:34
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* Gemma 7B produces gibberish output
* 2B seem to be working well though
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PR_kwDOJ0Z1Ps5_9ksb
| 7,367
|
CI testing
|
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| 2024-10-25T22:26:53
| 2024-10-25T23:50:58
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Nothing to see here....
|
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I_kwDOJ0Z1Ps6H3I2E
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|
`OLLAMA_NUM_PARALLEL` and multi-modal models lead to `failed processing images` error
|
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| 2024-05-05T07:49:42
| 2024-05-05T07:49:43
| null |
MEMBER
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### What is the issue?
When processing multiple requests using multi-modal models such as `llava` or `moondream` generation freezes and an error is printed in the server logs: `failed processing images`
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
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| 2,264,327,111
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I_kwDOJ0Z1Ps6G9uPH
| 3,919
|
trying to use llama3 with ollama embeddings getting error model 'llama2' not found
|
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| 2024-04-25T19:18:14
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### What is the issue?
im using this code
```
from langchain_community.llms import Ollama
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Chroma
MODEL = 'llama3'
model = Ollama(model=MODEL)
embeddings = OllamaEmbeddings()
loader = PyPDFLoader('der-admi.pdf')
documents = loader.load_and_split()
documents
vectorstore = Chroma.from_documents(documents, embedding=embeddings)
```
and im getting the following error
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[6], [line 2](vscode-notebook-cell:?execution_count=6&line=2)
[1](vscode-notebook-cell:?execution_count=6&line=1) from langchain_community.vectorstores import Chroma
----> [2](vscode-notebook-cell:?execution_count=6&line=2) vectorstore = Chroma.from_documents(documents, embedding=embeddings)
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:778](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:778), in Chroma.from_documents(cls, documents, embedding, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
[776](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:776) texts = [doc.page_content for doc in documents]
[777](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:777) metadatas = [doc.metadata for doc in documents]
--> [778](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:778) return cls.from_texts(
[779](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:779) texts=texts,
[780](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:780) embedding=embedding,
[781](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:781) metadatas=metadatas,
[782](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:782) ids=ids,
[783](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:783) collection_name=collection_name,
[784](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:784) persist_directory=persist_directory,
[785](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:785) client_settings=client_settings,
[786](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:786) client=client,
[787](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:787) collection_metadata=collection_metadata,
[788](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:788) **kwargs,
[789](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:789) )
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:736](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:736), in Chroma.from_texts(cls, texts, embedding, metadatas, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
[728](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:728) from chromadb.utils.batch_utils import create_batches
[730](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:730) for batch in create_batches(
[731](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:731) api=chroma_collection._client,
[732](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:732) ids=ids,
[733](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:733) metadatas=metadatas,
[734](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:734) documents=texts,
[735](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:735) ):
--> [736](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:736) chroma_collection.add_texts(
[737](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:737) texts=batch[3] if batch[3] else [],
[738](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:738) metadatas=batch[2] if batch[2] else None,
[739](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:739) ids=batch[0],
[740](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:740) )
[741](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:741) else:
[742](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:742) chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:275](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:275), in Chroma.add_texts(self, texts, metadatas, ids, **kwargs)
[273](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:273) texts = list(texts)
[274](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:274) if self._embedding_function is not None:
--> [275](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:275) embeddings = self._embedding_function.embed_documents(texts)
[276](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:276) if metadatas:
[277](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:277) # fill metadatas with empty dicts if somebody
[278](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:278) # did not specify metadata for all texts
[279](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:279) length_diff = len(texts) - len(metadatas)
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:211](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:211), in OllamaEmbeddings.embed_documents(self, texts)
[202](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:202) """Embed documents using an Ollama deployed embedding model.
[203](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:203)
[204](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:204) Args:
(...)
[208](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:208) List of embeddings, one for each text.
[209](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:209) """
[210](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:210) instruction_pairs = [f"{self.embed_instruction}{text}" for text in texts]
--> [211](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:211) embeddings = self._embed(instruction_pairs)
[212](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:212) return embeddings
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199), in OllamaEmbeddings._embed(self, input)
[197](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:197) else:
[198](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:198) iter_ = input
--> [199](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199) return [self._process_emb_response(prompt) for prompt in iter_]
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199), in <listcomp>(.0)
[197](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:197) else:
[198](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:198) iter_ = input
--> [199](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:199) return [self._process_emb_response(prompt) for prompt in iter_]
File [~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:173](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:173), in OllamaEmbeddings._process_emb_response(self, input)
[170](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:170) raise ValueError(f"Error raised by inference endpoint: {e}")
[172](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:172) if res.status_code != 200:
--> [173](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:173) raise ValueError(
[174](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:174) "Error raised by inference API HTTP code: %s, %s"
[175](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:175) % (res.status_code, res.text)
[176](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:176) )
[177](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:177) try:
[178](https://file+.vscode-resource.vscode-cdn.net/Users/ruben/Desktop/Python/pdf/local-model/~/Desktop/Python/pdf/local-model/.venv/lib/python3.10/site-packages/langchain_community/embeddings/ollama.py:178) t = res.json()
ValueError: Error raised by inference API HTTP code: 404, {"error":"model 'llama2' not found, try pulling it first"}
```
I have installed llama3 and is running
```
ollama list
NAME ID SIZE MODIFIED
llama3:latest a6990ed6be41 4.7 GB 10 minutes ago
mistral:latest 61e88e884507 4.1 GB 13 hours ago
```
I don't understand why is asking for llama2 in the embeddings.
### OS
macOS
### GPU
AMD
### CPU
Intel
### Ollama version
ollama version is 0.1.32
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I_kwDOJ0Z1Ps56CJIU
| 1,585
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CUDA error 2 [...] out of memory when using mixtral:8x7b-instruct-v0.1-q3_K_M but not on bigger models
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] | null | 11
| 2023-12-18T20:16:26
| 2024-05-10T00:25:43
| 2024-05-10T00:25:42
|
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| null | null | null |
Hi, I'm opening this issue because I noticed a weird behavior running ollama on docker with GPU support and trying different mixtral 8x7B sizes: I can easily do inference on my GPU with models like mixtral:8x7b-instruct-v0.1-q4_K_M but I see a memory failure when running smaller models like mixtral:8x7b-instruct-v0.1-q3_K_M.
I'm on Ubuntu 23.10, my GPU is a NVIDIA 3090.
My docker-compose.yml:
```
version: '3.7'
services:
ollama:
container_name: ollama_cat_dev
image: ollama/ollama:0.1.16
restart: unless-stopped
volumes:
- ./ollama:/root/.ollama
expose:
- 11434
environment:
- gpus=all
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
```
As said, when I try to use mixtral:8x7b-instruct-v0.1-q3_K_M I see "out of memory" issues and the inference is completely done on CPU. Here the log (attached because it was too long for github issues)
[ollama.log](https://github.com/jmorganca/ollama/files/13708188/ollama.log)
Here instead the log when I run a bigger model like mixtral:8x7b-instruct-v0.1-q4_K_M:
```
2023/12/18 20:09:56 llama.go:300: 23732 MB VRAM available, loading up to 22 GPU layers
2023/12/18 20:09:56 llama.go:436: starting llama runner
2023/12/18 20:09:56 llama.go:494: waiting for llama runner to start responding
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6
{"timestamp":1702930196,"level":"INFO","function":"main","line":2652,"message":"build info","build":441,"commit":"948ff13"}
{"timestamp":1702930196,"level":"INFO","function":"main","line":2655,"message":"system info","n_threads":16,"n_threads_batch":-1,"total_threads":32,"system_info":"AVX = 1 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | "}
llama_model_loader: loaded meta data with 24 key-value pairs and 995 tensors from /root/.ollama/models/blobs/sha256:59a66936ed54cfe28136f99a3ec5336f2b404bad0bc0f3a48123f87c677d8623 (version GGUF V3 (latest))
llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32000, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 10: blk.0.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 11: blk.0.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 12: blk.0.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 13: blk.0.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 14: blk.0.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 15: blk.0.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 16: blk.0.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 17: blk.0.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 18: blk.0.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 19: blk.0.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 20: blk.0.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 21: blk.0.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 22: blk.0.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 23: blk.0.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 24: blk.0.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 25: blk.0.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 26: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.0.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 29: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 30: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 31: blk.0.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 32: blk.1.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 33: blk.1.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 34: blk.1.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 35: blk.1.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 36: blk.1.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 37: blk.1.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 38: blk.1.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 39: blk.1.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 40: blk.1.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 41: blk.1.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 42: blk.1.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 43: blk.1.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 44: blk.1.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 45: blk.1.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 46: blk.1.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 47: blk.1.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 48: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 49: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 50: blk.1.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 51: blk.1.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 52: blk.1.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 53: blk.1.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 54: blk.1.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 55: blk.1.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 56: blk.1.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 57: blk.1.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 58: blk.1.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 59: blk.1.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 60: blk.1.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 61: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 62: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 63: blk.2.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 64: blk.2.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 65: blk.2.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 66: blk.2.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 67: blk.2.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 68: blk.2.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 69: blk.2.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 70: blk.2.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 71: blk.2.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 72: blk.2.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 73: blk.2.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 74: blk.2.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 75: blk.2.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 76: blk.2.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 77: blk.2.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 78: blk.2.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 79: blk.2.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 80: blk.2.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 81: blk.2.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 82: blk.2.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 83: blk.2.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 84: blk.2.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 85: blk.2.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 86: blk.2.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 87: blk.2.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 88: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 89: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 90: blk.2.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 91: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 92: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 93: blk.2.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 94: blk.3.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 95: blk.3.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 96: blk.3.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 97: blk.3.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 98: blk.3.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 99: blk.3.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 100: blk.3.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 101: blk.3.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 102: blk.3.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 103: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 104: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 105: blk.3.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 106: blk.3.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 107: blk.3.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 108: blk.3.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 109: blk.3.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 110: blk.3.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 111: blk.3.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 112: blk.3.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 113: blk.3.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 114: blk.3.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 115: blk.3.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 116: blk.3.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 117: blk.3.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 118: blk.3.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 119: blk.3.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 120: blk.3.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 121: blk.3.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 122: blk.3.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 123: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 124: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 125: blk.4.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 126: blk.4.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 127: blk.4.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 128: blk.4.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 129: blk.4.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 130: blk.4.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 131: blk.4.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 132: blk.4.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 133: blk.4.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 134: blk.4.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 135: blk.4.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 136: blk.4.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 137: blk.4.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 138: blk.4.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 139: blk.4.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 140: blk.4.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 141: blk.4.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 142: blk.4.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 143: blk.4.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 144: blk.4.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 145: blk.4.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 146: blk.4.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 147: blk.4.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 148: blk.4.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 149: blk.4.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 150: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 151: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 152: blk.4.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 153: blk.4.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 154: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 155: blk.4.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 156: blk.5.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 157: blk.5.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 158: blk.5.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 159: blk.5.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 160: blk.5.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 161: blk.5.ffn_gate.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 162: blk.5.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 163: blk.5.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 164: blk.5.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 165: blk.5.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 166: blk.5.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
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llama_model_loader: - tensor 964: blk.31.ffn_down.0.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 965: blk.31.ffn_up.0.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 966: blk.31.ffn_gate.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 967: blk.31.ffn_down.1.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 968: blk.31.ffn_up.1.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 969: blk.31.ffn_gate.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 970: blk.31.ffn_down.2.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 971: blk.31.ffn_up.2.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 972: blk.31.ffn_gate.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 973: blk.31.ffn_down.3.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 974: blk.31.ffn_up.3.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 975: blk.31.ffn_gate.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 976: blk.31.ffn_down.4.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 977: blk.31.ffn_up.4.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 978: blk.31.ffn_gate.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 979: blk.31.ffn_down.5.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 980: blk.31.ffn_up.5.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 981: blk.31.ffn_gate.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 982: blk.31.ffn_down.6.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 983: blk.31.ffn_up.6.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 984: blk.31.ffn_gate.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 985: blk.31.ffn_down.7.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 986: blk.31.ffn_up.7.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 987: blk.31.ffn_gate_inp.weight f16 [ 4096, 8, 1, 1 ]
llama_model_loader: - tensor 988: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 989: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 990: blk.31.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 991: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 992: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 993: blk.31.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 994: output_norm.weight f32 [ 4096, 1, 1, 1 ]
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.bos_token_id u32 = 1
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 22: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 23: 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 definition check successful ( 259/32000 ).
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: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 4
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: n_ff = 14336
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
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_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly 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_tensors: ggml ctx size = 0.39 MiB
llm_load_tensors: using CUDA for GPU acceleration
llm_load_tensors: mem required = 7999.20 MiB
llm_load_tensors: offloading 22 repeating layers to GPU
llm_load_tensors: offloaded 22/33 layers to GPU
llm_load_tensors: VRAM used: 17217.06 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 32768
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: VRAM kv self = 2816.00 MB
llama_new_context_with_model: KV self size = 4096.00 MiB, K (f16): 2048.00 MiB, V (f16): 2048.00 MiB
llama_build_graph: non-view tensors processed: 1124/1124
llama_new_context_with_model: compute buffer total size = 2167.35 MiB
llama_new_context_with_model: VRAM scratch buffer: 2164.04 MiB
llama_new_context_with_model: total VRAM used: 22197.10 MiB (model: 17217.06 MiB, context: 4980.04 MiB)
{"timestamp":1702930214,"level":"INFO","function":"main","line":3035,"message":"HTTP server listening","hostname":"127.0.0.1","port":59735}
{"timestamp":1702930214,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":51756,"status":200,"method":"HEAD","path":"/","params":{}}
2023/12/18 20:10:14 llama.go:508: llama runner started in 17.801696 seconds
2023/12/18 20:10:14 llama.go:577: loaded 0 images
{"timestamp":1702930227,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":51756,"status":200,"method":"POST","path":"/completion","params":{}}
{"timestamp":1702930227,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":46844,"status":200,"method":"POST","path":"/tokenize","params":{}}
[GIN] 2023/12/18 - 20:10:27 | 200 | 31.133913884s | 172.28.0.4 | POST "/api/generate"
{"timestamp":1702930227,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":46844,"status":200,"method":"HEAD","path":"/","params":{}}
2023/12/18 20:10:27 llama.go:577: loaded 0 images
{"timestamp":1702930255,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":46844,"status":200,"method":"POST","path":"/completion","params":{}}
{"timestamp":1702930255,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"127.0.0.1","remote_port":48110,"status":200,"method":"POST","path":"/tokenize","params":{}}
[GIN] 2023/12/18 - 20:10:55 | 200 | 27.681923952s | 172.28.0.4 | POST "/api/generate"
2023/12/18 20:15:57 llama.go:451: signal: killed
2023/12/18 20:15:57 llama.go:525: llama runner stopped successfully
```
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| 2,764,987,796
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PR_kwDOJ0Z1Ps6GhtLi
| 8,279
|
Improved offline installation experience (install.sh)
|
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| 2025-01-01T10:36:41
| 2025-01-01T10:38:22
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As the current install.sh script requires good internet connection, this improvement enables user to pre-download required files in other ways to install offline with the script.
Add 2 new functions:
`note`: Display a note text in yellow.
`download_and_extract`: Handle all the download operations. Shows the filename and URL when the download starts, which allows user to download the required files manually to install Ollama offline or under restricted network conditions.
Tested on Jetson Orin NX (JetPack 6.1 / Ubuntu 22.04)
**Online Installation:**
```
>>> Installing ollama to /usr/local
>>> Downloading Linux arm64 bundle
>>> Downloading ollama-linux-arm64.tgz from https://ollama.com/download/ollama-linux-arm64.tgz
NOTE: If you have trouble downloading, use Ctrl-C to terminate the script and manually download the file to the current directory, then re-run the script.
O=- # # # #
```
**Offline Installation (with file downloaded in the same dir):**
```
>>> Installing ollama to /usr/local
>>> Downloading Linux arm64 bundle
>>> Download skipped, using local existing file: ollama-linux-arm64.tgz
>>> Downloading JetPack 6 components
>>> Download skipped, using local existing file: ollama-linux-arm64-jetpack6.tgz
>>> Adding ollama user to render group...
>>> Adding ollama user to video group...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
>>> NVIDIA JetPack ready.
>>> The Ollama API is now available at 127.0.0.1:11434.
>>> Install complete. Run "ollama" from the command line.
```
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| 2,267,257,865
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PR_kwDOJ0Z1Ps5t64Hm
| 3,984
|
types/model: relax name length constraint from 2 to 1
|
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| 2024-04-27T23:45:51
| 2024-04-28T00:58:42
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|
CONTRIBUTOR
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I_kwDOJ0Z1Ps6A6AW9
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I hope Ollama can add an embeddings interface compatible with OpenAI API
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I hope Ollama can add an embeddings interface compatible with OpenAI API
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PR_kwDOJ0Z1Ps5zt1ac
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llm: architecture patch
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PR_kwDOJ0Z1Ps5lSZ3d
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update submodule to `1cfb5372cf5707c8ec6dde7c874f4a44a6c4c915`
|
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I_kwDOJ0Z1Ps6VUIwv
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New Command-r models output nonsense
|
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### What is the issue?
The new 4-bit quants of command-r (I dont have the VRAM for higher quants) output nonsense.
``` bash
≻ ollama run command-r
pulling manifest
pulling 8e0609b8f0fe... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 18 GB
pulling b3741b7b9ce5... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 77 B
pulling 922095537bc1... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 2.9 KB
pulling 945eaa8b1428... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 13 KB
pulling 36b9655abe6a... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 81 B
pulling 8e63f21e12fb... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 568 B
verifying sha256 digest
writing manifest
success
>>> Hello
general obligatoireasıası obligatoireasıasıası obligatoire obligatoireası obligatoireasıası obligatoireasıasıası obligatoireasıasıasıasıası obligatoire
obligatoire obligatoireasıasıası obligatoireasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıası
>>> What actions can you perform
ştıhind ministrowieası
SummaryasıştıştıhindhindhindasıhindhindasıasıştıştıhindasıasıasıhindştıştıştıştıasıhindasıhindasıasıasıasıhindasıasıştıştıasıhindasıasıasıasıasıasıasıasıasıasıaSummaryasıştıştıhindhindhindasıhindhindasıasıştıştıhindasıasıasıhindştıştıştıştıasıhindasıhindasıasıasıasıhindasıasıştıştıasıhindasıasıasasıasıasıasıasıasıasıasıasıasıasıasıştıhindştıhindştıhindasıhindasıasıası ministrowie Summaryasıasıhindhindştıştıhindasıasıasıasıasıasıasıasıasıası
```
Specifying version
``` bash
≻ ollama run command-r:35b-08-2024-q4_K_S
pulling manifest
pulling 3ed323d43be5... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 18 GB
pulling b3741b7b9ce5... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 77 B
pulling 922095537bc1... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 2.9 KB
pulling 945eaa8b1428... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 13 KB
pulling 36b9655abe6a... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 81 B
pulling 93af4240a02c... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████▏ 570 B
verifying sha256 digest
writing manifest
success
>>> Hello
generalası obligatoireasıası GENERası expulsionası obligatoireasıasıası
primarioasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıasıası
```
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.9
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I_kwDOJ0Z1Ps6Kux5X
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Garbage output running llama3 GGUF model
|
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| 2024-05-31T10:38:49
| 2024-07-09T07:04:34
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### What is the issue?
I downloaded https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF/blob/main/Meta-Llama-3-8B.Q2_K.gguf
Created a Modelfile using `ollama create example -f Modelfile`
and ran `ollama run example`
On asking the _question why is the sky blue?_ on the >>> prompt I am getting garbage (a series of numbers)
### OS
Windows
### GPU
Other
### CPU
Intel
### Ollama version
0.1.31
|
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|
PR_kwDOJ0Z1Ps5vJXEh
| 4,339
|
chore: update dependencies across the board
|
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| 2024-05-11T03:19:53
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- Update `github.com/gin-gonic/gin` from `v1.9.1` to `v1.10.0`
- Update `github.com/stretchr/testify` from `v1.8.4` to `v1.9.0`
- Add `github.com/bytedance/sonic/loader` and `github.com/cloudwego/*` as new indirect dependencies
- Update `github.com/bytedance/sonic` from `v1.9.1` to `v1.11.6` and remove old indirect dependencies
- Update `github.com/gabriel-vasile/mimetype` from `v1.4.2` to `v1.4.3`
- Update `github.com/go-playground/validator/v10` from `v10.14.0` to `v10.20.0`
- Update various indirect dependencies to newer versions, including `github.com/klauspost/cpuid/v2`, `github.com/leodido/go-urn`, `github.com/mattn/go-isatty`, `github.com/pelletier/go-toml/v2`, `github.com/ugorji/go/codec`, `golang.org/x/*` packages, and `google.golang.org/protobuf`
- Remove several outdated indirect dependencies
|
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PR_kwDOJ0Z1Ps5n1_lQ
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|
Update types.go
|
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| 2024-02-25T15:21:39
| 2024-02-25T18:41:26
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CONTRIBUTOR
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|
specfied -> specified
|
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|
PR_kwDOJ0Z1Ps5XC0de
| 260
|
override ggml-metal if the file is different
|
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| 2023-08-02T19:51:01
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I_kwDOJ0Z1Ps53664X
| 1,286
|
Change enviroment-variables as settings to command parameters
|
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| 2023-11-27T10:12:00
| 2024-02-20T01:18:58
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NONE
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## Changethe method of configure settings
Instead of enviroment variables uses internal settings, at least as user interface.
### Example
Instead of:
> OLLAMA_MODELS=/path/to/file; ollama run model
Run:
>ollama conf path_to_models /path/to/file
>ollama run model
Or:
>ollama run model -f /path/to/file
|
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PR_kwDOJ0Z1Ps5nJQKS
| 2,553
|
Harden AMD driver lookup logic
|
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| 2024-02-17T00:22:52
| 2024-02-17T01:23:15
| 2024-02-17T01:23:12
|
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It looks like the version file doesn't exist on older(?) drivers
Fixes #2502
|
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I_kwDOJ0Z1Ps6nBG-q
| 8,519
|
CLI: Managing models like (Docker) containers via ID
|
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| 2025-01-21T14:58:12
| 2025-01-21T14:58:30
| null |
NONE
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Could we please have
`ollama ps`
`ollama stop <ID>`
instead of
`ollama stop this-is-a-llm-with-a-pretty-long-name:1337b_instruzioni_v3.33_q5_K_S`
or at least tab autocompletion?
Sorry in advance: Maybe this is already a duplicate (did look for it!), as it is not really a highly inventive idea.
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I_kwDOJ0Z1Ps6mh_T5
| 8,463
|
AMD Radeon RX6700XT unable to take input
|
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| 2025-01-16T22:41:02
| 2025-01-18T10:09:36
| 2025-01-18T10:09:35
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### What is the issue?
When trying to use ollamas APIs, the llama server crashes when loading.
## Obligatory System Information
CPU: AMD Ryzen 9 7900
RAM: 64GB DDR5
OS: Fedora Linux 41 (Workstation Edition) x86_64
Ollama host: Docker
### docker-compose.yml
```yml
services:
webui:
image: ghcr.io/open-webui/open-webui:main
ports:
- 8003:8080/tcp
environment:
- OLLAMA_BASE_URL=http://ollama:11434
volumes:
- webui:/app/backend/data
depends_on:
- ollama
restart:
unless-stopped
ollama:
image: ollama/ollama:rocm
environment:
- HSA_OVERRIDE_GFX_VERSION="10.3.1"
- AMD_SERIALIZE_KERNEL=3
- OLLAMA_DEBUG=1
- HIP_VISIBLE_DEVICES=0
- OLLAMA_LLM_LIBRARY=rocm_v60102
ports:
- 11434:11434/tcp
volumes:
- ollama:/root/.ollama
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
restart:
unless-stopped
volumes:
ollama:
webui:
```
### Console output of error
https://pastebin.com/pTW3FMCp
Line 55 already hints at an error. The next troubling thing I could find was at line 166.
### rocminfo
```
❯ rocminfo
ROCk module is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.1
Runtime Ext Version: 1.6
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD Ryzen 9 7900 12-Core Processor
...[redacted because irrelevant]
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx1031
Uuid: GPU-XX
Marketing Name: AMD Radeon RX 6700 XT
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 16(0x10) KB
L2: 3072(0xc00) KB
L3: 98304(0x18000) KB
Chip ID: 29663(0x73df)
ASIC Revision: 0(0x0)
Cacheline Size: 128(0x80)
Max Clock Freq. (MHz): 2855
BDFID: 768
Internal Node ID: 1
Compute Unit: 40
SIMDs per CU: 2
Shader Engines: 2
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Coherent Host Access: FALSE
Memory Properties:
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 122
SDMA engine uCode:: 80
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 12566528(0xbfc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size: 12566528(0xbfc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Recommended Granule:0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1031
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
```
My user is in the `video` and `render` groups.
### `ls` output of relevant `/dev` entries
```
❯ ls -lag /dev/dri /dev/kfd /dev/dri/*
crw-rw----@ 226,1 root video 16 Jan 18:19 /dev/dri/card1
crw-rw-rw- 234,0 root render 16 Jan 15:46 /dev/kfd
crw-rw-rw- 226,128 root render 16 Jan 15:46 /dev/dri/renderD128
/dev/dri:
drwxr-xr-x - root root 16 Jan 15:45 ./
drwxr-xr-x - root root 16 Jan 23:20 ../
drwxr-xr-x - root root 16 Jan 15:46 by-path/
crw-rw----@ 226,1 root video 16 Jan 18:19 card1
crw-rw-rw- 226,128 root render 16 Jan 15:46 renderD128
/dev/dri/by-path:
drwxr-xr-x - root root 16 Jan 15:46 ./
drwxr-xr-x - root root 16 Jan 15:45 ../
lrwxrwxrwx@ 8 root root 16 Jan 15:46 pci-0000:03:00.0-card -> ../card1
lrwxrwxrwx 13 root root 16 Jan 15:46 pci-0000:03:00.0-render -> ../renderD128
```
I just pulled the docker image half an hour ago, so it should be the most up to date. Nevertheless, here is the hash of the image in use: `sha256:9874ece252bfd8404e2795066649953255abc29e7d6aeab1966d19fadf9f06c4`
If any more information is needed, I am happy to supply it. :)
Thank you a lot for your time and effort.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
_No response_
|
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| 2,264,807,387
|
I_kwDOJ0Z1Ps6G_jfb
| 3,927
|
function calling with autogen does not work
|
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| false
| null |
[] | null | 4
| 2024-04-26T02:10:55
| 2024-07-30T02:50:42
| 2024-07-26T00:50:33
|
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### What is the issue?
```python
#!/usr/local/bin/python3.12
from typing import Literal
from pydantic import BaseModel, Field
from typing_extensions import Annotated
import autogen
from autogen.cache import Cache
# MODEL_NAME = "gpt-3.5-turbo"
# API_URL = "https://api.openai.com/v1/"
# API_KEY = "sk-XYZ"
MODEL_NAME = "llama3:8k"
API_URL = "http://10.4.4.207:11434/v1"
API_KEY = "ollama"
config_list = [
{
"model": MODEL_NAME,
"base_url": API_URL,
"api_key": API_KEY,
}
]
llm_config = {
"config_list": config_list,
"timeout": 120,
}
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
is_termination_msg=lambda x: x.get("content", "")
and x.get("content", "").rstrip().endswith("TERMINATE"),
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
)
chatbot = autogen.AssistantAgent(
name="chatbot",
system_message="For currency exchange tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.",
llm_config=llm_config,
)
CurrencySymbol = Literal["USD", "EUR"]
def exchange_rate(
base_currency: CurrencySymbol, quote_currency: CurrencySymbol
) -> float:
if base_currency == quote_currency:
return 1.0
elif base_currency == "USD" and quote_currency == "EUR":
return 1 / 1.1
elif base_currency == "EUR" and quote_currency == "USD":
return 1.1
else:
raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")
class Currency(BaseModel):
currency: Annotated[CurrencySymbol, Field(..., description="Currency symbol")]
amount: Annotated[float, Field(0, description="Amount of currency", ge=0)]
@user_proxy.register_for_execution()
@chatbot.register_for_llm(description="Currency exchange calculator.")
def currency_calculator(
base: Annotated[Currency, "Base currency: amount and currency symbol"],
quote_currency: Annotated[CurrencySymbol, "Quote currency symbol"] = "USD",
) -> Currency:
quote_amount = exchange_rate(base.currency, quote_currency) * base.amount
return Currency(amount=quote_amount, currency=quote_currency)
def main():
with Cache.disk() as cache:
user_proxy.initiate_chat(
chatbot,
message="How much is 112.23 Euros in US Dollars?",
summary_method="last_msg",
cache=cache,
)
if __name__ == "__main__":
main()
```
output using gpt-3.5-turbo:
```python3.12 autogen_tools_example.py
user_proxy (to chatbot):
How much is 112.23 Euros in US Dollars?
--------------------------------------------------------------------------------
chatbot (to user_proxy):
***** Suggested tool call (call_TdfMydJ9TeKBbz8QRE5ZHl2k): currency_calculator *****
Arguments:
{"base":{"currency":"EUR","amount":112.23},"quote_currency":"USD"}
************************************************************************************
--------------------------------------------------------------------------------
>>>>>>>> EXECUTING FUNCTION currency_calculator...
user_proxy (to chatbot):
user_proxy (to chatbot):
***** Response from calling tool (call_TdfMydJ9TeKBbz8QRE5ZHl2k) *****
{"currency":"USD","amount":123.45300000000002}
**********************************************************************
--------------------------------------------------------------------------------
chatbot (to user_proxy):
112.23 Euros is equivalent to 123.45 US Dollars.
--------------------------------------------------------------------------------
user_proxy (to chatbot):
--------------------------------------------------------------------------------
chatbot (to user_proxy):
TERMINATE
--------------------------------------------------------------------------------
```
output using llama3 running with ollama:
```
user_proxy (to chatbot):
How much is 112.23 Euros in US Dollars?
--------------------------------------------------------------------------------
chatbot (to user_proxy):
To convert 112.23 Euros to US Dollars, I can use the provided function:
According to the exchange rate, 1 EUR is approximately equal to 1.22 USD.
Converting 112.23 EUR, we get:
112.23 EUR * 1.22 USD/EUR = 136.88 USD
So, 112.23 Euros are equivalent to approximately 136.88 US Dollars.
Please let me know if I should continue with the next task or reply TERMINATE when the currency exchange is done.
--------------------------------------------------------------------------------
user_proxy (to chatbot):
--------------------------------------------------------------------------------
```
followed by a bunch of other empty messages from `user_proxy (to chatbot)` before exiting.
### OS
Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama/ollama:0.1.32
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PR_kwDOJ0Z1Ps6BEmu1
| 7,527
|
Fix minor inconsistency
|
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"type": "User",
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"site_admin": false
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closed
| false
| null |
[] | null | 1
| 2024-11-06T15:24:36
| 2024-11-08T17:36:17
| 2024-11-08T17:36:17
|
CONTRIBUTOR
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| 2,288,528,117
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PR_kwDOJ0Z1Ps5vCP33
| 4,300
|
Add LlamaScript to Community Projects
|
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Pull Request for
#4061
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I_kwDOJ0Z1Ps553MxZ
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Increasing slow response - CPU only on Linux Azure
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I'm using the following VM in azure:
Standard D8s v3 vCPUs 8, RAM 32 GiB
Have tried Mistral 7b and Orca-mini. I've also tried 4 bit versions.
Ollama is responding increasingly slowly. After the 4th simple query ("hi" or "what's the capital of ...") I'm waiting in excess of 60 seconds for it to begin to respond; the response gets slow with each repeat simple question. Once it has responded the each token streams in reasonably well. I've tried both Ubuntu and Suse.
Is the VM just not suitable? I'm trying to see how far I can get without a GPU.
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Add `OLLAMA_HOME` for setting `~/.ollama`
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| 2024-05-27T05:26:18
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CONTRIBUTOR
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Fixes https://github.com/ollama/ollama/issues/228
This PR adds the optional configuration for `OLLAMA_HOME` to prevent cluttering
the user's home directory.
`OLLAMA_HOME` is optional and uses the current behavior if not provided.
If `OLLAMA_MODELS` is not explicitly, the default value is `~/$OLLAMA_HOME/models`.
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I_kwDOJ0Z1Ps6MVX2D
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Windows - `go generate` failing on build_cpu
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### What is the issue?
I've been trying to get a Windows dev environment up and running following the [development](https://github.com/ollama/ollama/blob/main/docs/development.md) guide. I've attempted installing both MinGW-w64 and MSYS2, along with the latest Visual Studio build tools, but the existing Windows build script does not seem to work out-of-the-box. I've tried swapping paths, moving which cmake is actually getting used, setting the default generator through environment variables, and quite a bit more.
When running `go generate ./...` some variation of the following error is shown:
```
Building LCD CPU
generating config with: cmake -S ../llama.cpp -B ../build/windows/amd64/cpu -DCMAKE_POSITION_INDEPENDENT_CODE=on -A x64 -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off -DBUILD_SHARED_LIBS=on -DLLAMA_NATIVE=off -DLLAMA_SERVER_VERBOSE=off -DCMAKE_BUILD_TYPE=Release
cmake version 3.28.3-msvc11
CMake suite maintained and supported by Kitware (kitware.com/cmake).
CMake Error at CMakeLists.txt:2 (project):
Generator
Ninja
does not support platform specification, but platform
x64
was specified.
CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage
CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage
-- Configuring incomplete, errors occurred!
llm\generate\generate_windows.go:3: running "powershell": exit status 1
```
The crux of the issue seems to be the default generator (ninja). It does not accept the `-A` argument that the Visual Studio generators do.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.44
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I_kwDOJ0Z1Ps6Y0RgS
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ollama_models path not working any longer
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| 2024-10-03T11:42:01
| 2024-12-02T23:04:45
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NONE
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### What is the issue?
Last week I added ollama_models path to my env file in my Mac. Olama picked up the settings and saved the models to my path (external SSD). Now yesterday when I picked gemma 2 and got it downloaded it ignored the path and downloaded it to .ollama.
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.12
|
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I_kwDOJ0Z1Ps6ITF7h
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windows ollama 0.1.34 can not use GPU，with nvidia RTX 4060
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NONE
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### What is the issue?
```
C:\Users\zh_af>nvidia-smi
Thu May 9 08:53:43 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 537.70 Driver Version: 537.70 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4060 WDDM | 00000000:01:00.0 On | N/A |
| 31% 30C P8 N/A / 115W | 7776MiB / 8188MiB | 2% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 2824 C+G ...on\124.0.2478.80\msedgewebview2.exe N/A |
| 0 N/A N/A 4304 C+G ...__8wekyb3d8bbwe\Notepad\Notepad.exe N/A |
| 0 N/A N/A 8132 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
| 0 N/A N/A 9796 C+G ...5\extracted\runtime\WeChatAppEx.exe N/A |
| 0 N/A N/A 10424 C+G ...wekyb3d8bbwe\XboxGameBarWidgets.exe N/A |
| 0 N/A N/A 11688 C+G C:\Windows\System32\NahimicSvc64.exe N/A |
| 0 N/A N/A 12648 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 19188 C+G ...cal\Microsoft\OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 20240 C+G ...oogle\Chrome\Application\chrome.exe N/A |
| 0 N/A N/A 20948 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 21820 C+G ...x64__8wekyb3d8bbwe\WinStore.App.exe N/A |
| 0 N/A N/A 24376 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A |
| 0 N/A N/A 25396 C ...\cuda_v11.3\ollama_llama_server.exe N/A |
| 0 N/A N/A 26708 C+G ...8bbwe\SnippingTool\SnippingTool.exe N/A |
| 0 N/A N/A 27804 C+G ...\Docker\frontend\Docker Desktop.exe N/A |
| 0 N/A N/A 30560 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 30980 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 31312 C+G ...n\SunloginClient\SunloginClient.exe N/A |
| 0 N/A N/A 35540 C+G ...crosoft\Edge\Application\msedge.exe N/A |
| 0 N/A N/A 38564 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A |
| 0 N/A N/A 39748 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 41288 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
+---------------------------------------------------------------------------------------+
```
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.34
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|
Mistral not providing license information
|
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| 2023-12-11T06:30:58
| 2024-01-25T22:56:35
| 2024-01-25T22:32:20
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NONE
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It is maybe because they don't include a license.txt in their repository. However, they do specify that it is Apache 2.0
also here
It might be nice if licenses were added to the website as well, that users could filter by their licensing needs
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PR_kwDOJ0Z1Ps5uF_a6
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types/model: reduce Name.Filepath allocs from 5 to 2
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| 2024-04-30T05:14:34
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CONTRIBUTOR
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Is IPv6 supported?
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With the Ollama server running:
```bash
> curl -X POST --header 'Content-Type: application/json' "http://[::1]:11434/api/generate" -d '{
"model": "llama2:13b",
"prompt": "Your first prompt goes here"
}'
curl: (7) Failed to connect to ::1 port 11434 after 5 ms: Couldn't connect to server
```
I am wondering, is IPv6 supported with Ollama server?
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| 2,022,410,814
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I_kwDOJ0Z1Ps54i4o-
| 1,361
|
Add support for gpt4-x-alpaca
|
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Hi there,
this is an amazing model:
https://huggingface.co/chavinlo/gpt4-x-alpaca
Cheers.
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PR_kwDOJ0Z1Ps5653f9
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|
catch when model vocab size is set correctly
|
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This check catches if there are too many tokens in the tokenizer vs. the expected number of tokens specified in the `vocab_size` field of `config.json`. This typically happens if the `added_tokens` array in `tokenizer.json` ends up has too many tokens.
Right now this results in the back end barfing during inference instead of catching it during `ollama create`.
|
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PR_kwDOJ0Z1Ps5cv_e7
| 778
|
show request to server rather than local check
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| 2023-10-13T15:14:41
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The show command should send a request to the server, rather than making a direct call to the function locally.
resolces #776
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PR_kwDOJ0Z1Ps5iXLIp
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|
Updated syntax in client.py
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* Updated the syntax for `heartbeat()` in `client.py`.
* Functionality is maintained.
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PR_kwDOJ0Z1Ps5jnP__
| 1,874
|
Set corret CUDA minimum compute capability version
|
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If you attempt to run the current CUDA build on compute capability 5.2 cards, you'll hit the following failure:
cuBLAS error 15 at ggml-cuda.cu:7956: the requested functionality is not supported
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PR_kwDOJ0Z1Ps5a7XZk
| 567
|
update submodule
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I_kwDOJ0Z1Ps6HLiBw
| 4,013
|
API Endpoint for Listing Loaded Running Models
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| 2024-04-29T01:25:17
| 2024-05-14T00:17:37
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NONE
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It would be excellent to be able to interrogate the API to determine which models are running at any given time, rather than just seeing which checkpoints were pulled.
I use a variety of clients to interact with Ollama's API. I sometimes run models with a long `keep_alive` and assume others have similar use cases.
The only way I know of to identify a running model is through processes: `ps aux | grep -- '--model' | grep -v grep | grep -Po '(?<=--model\s).*' | cut -d ' ' -f1`. This will give you the full path to the model's blob. From there, you can compare that with the output of ollama show --modelfile (or the /api/show endpoint).
I checked the open issues and reddit and didn't see any similar RFIs or requests.
I wrote a [bash script](https://github.com/strikeoncmputrz/LLM_Scripts/blob/main/show_loaded_models.sh) (depends on jq) that implements this as POC.
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I_kwDOJ0Z1Ps6BAN94
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Ollama doesn't use Radeon RX 6600
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| 2024-03-01T22:57:13
| 2024-09-06T20:08:20
| 2024-03-12T07:27:35
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NONE
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I'm using Arch Linux with the latest updates installed and ollama installed from its AUR package. When I use the Smaug model, it uses my CPU considerably but my GPU not at all:
I put the output of `ollama serve` and ollama running Smaug into a file:
[ollama.txt](https://github.com/ollama/ollama/files/14466737/ollama.txt)
[smaug.txt](https://github.com/ollama/ollama/files/14466741/smaug.txt)
I've installed Cuda because I thought for a moment it is needed, but I don't think that's the reason it doesn't work.
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PR_kwDOJ0Z1Ps5kfBUs
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Save and load sessions
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| 2024-01-19T01:54:02
| 2024-02-12T20:10:33
| 2024-01-25T20:12:36
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CONTRIBUTOR
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This change allows users to interactively save a session from the REPL, and then load it back up again later.
It also adds a new `MESSAGE` command for Modelfiles so that users can build their own session which can be created with `ollama create`.
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Codellama70b runs, but Codellama70b-Instruct spins forever after downloading
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| 2024-01-31T04:44:58
| 2024-07-19T21:39:51
| 2024-07-19T21:39:51
|
NONE
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Wondering if this is a config issue or something else? IE are any of the additional model files that are downloaded alongside the 38gb main file, borked in any way?
Ollama is via WSL in windows.
`ollama run codellama:70b`
works and gives me code
`ollama run codellama:70b-instruct`
downloads but has the spinning dots thing and never progresses.
That is to say
Verifies
"removing any unused layers"
"success"
But then nothing. Can't prompt, it just sits here and spins.
Exit and restart, try it again. Already downloaded so it skips re-downloading... but spins again for infinite amount of time.
Since 70b vanilla runs, it can't be memory or GPUs?
I have 2x 4090 and 64gb RAM. Sure 128 would be better but as I said, 70b vanilla runs fine.
Thanks
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I_kwDOJ0Z1Ps6Pla-t
| 5,703
|
Mixtral truncates output after year
|
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| 2024-07-15T14:56:29
| 2024-07-15T15:02:22
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NONE
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### What is the issue?
Output from Mixtral stops after year of date.
I could recreate the issue within ollama that appears in vLLM as well: https://github.com/vllm-project/vllm/issues/2464
The model I used was: `[mixtral:8x7b-instruct-v0.1-q8_0](https://ollama.com/library/mixtral:8x7b-instruct-v0.1-q8_0)`
You should reproduce it with the mentioned prompt:
```
In order to write a concise single-paragraph summary, pay attention to the following text:
The Commonwealth Bank of Australia (CBA) reported strong financial results for the first half of fiscal year 2023, with a statutory net profit after tax of AUD 5.216 billion, up 10% from the same period last year. Cash net profit after tax stood at AUD 5.153 billion, a 9% increase. Operating performance also improved by 18% to AUD 7.820 billion. The bank's home and consumer lending gross lending reached AUD 77 billion, while business and corporate lending gross lending amounted to AUD 18 billion. CBA's net promoter scores (NPS) remained high, with the bank ranking first in the consumer, business, and institutional categories. The bank's liquid assets and deposit funding increased, and its weighted average maturity stood at 5.8 years. CBA's CET1 ratio was 11.4%, and it declared a dividend per share of AUD 2.10 (35 cents). However, the bank warned that forward-looking statements should be treated with caution due to current economic uncertainties and geopolitical risks.
Using only the text above, write a condensed and concise summary of key results (preferably as one paragraph):
```
The output stops after the year of date:
`The Commonwealth Bank of Australia (CBA) reported strong financial results for the first half of fiscal year 2`
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.2.5
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PR_kwDOJ0Z1Ps5weDLL
| 4,612
|
added new community integration (headless-ollama)
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| 2024-05-24T13:58:18
| 2024-06-09T01:51:16
| 2024-06-09T01:51:16
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CONTRIBUTOR
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ollama makes it wonderfully easy to build desktop apps that rely on local LLMs with its js and python libraries.
> however, the user's system needs to have ollama already installed for the desktop app to use the libraries and make calls to the LLMs. Making users install ollama client separately isn't good UX tbh. thus, "headless-ollama"
this repo has pre-run scripts which automatically utilises node runtime to check for the host OS and installs the ollama client and the models needed by the desktop app before the server starts.
this is really helpful while building desktop apps where you want everything to be local and self contained within the system.
|
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PR_kwDOJ0Z1Ps5z7dYK
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|
Document concurrent behavior and settings
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| 2024-06-28T20:16:56
| 2024-07-01T16:49:52
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Merge after #4218
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PR_kwDOJ0Z1Ps5imXZC
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|
Update README.md - Community Integrations - Obsidian Local GPT plugin
|
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| 2023-12-21T19:13:07
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Local GPT plugin for Obsidian mainly relies on Ollama provider
But it's also possible to use OpenAI-like local server.
I'd say that Local GPT plugin is enhanced version of [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama) in every way.
|
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I_kwDOJ0Z1Ps6Wol3w
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|
llama 3.1 8b params downloaded from huggingface, strange num_ctx behavior
|
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### What is the issue?
I downloaded llama3.1 8b quantized to 8 bits from huggingface. It appears to have a default context size of 132k. Looking at numerous sources on the internet it seemed reasonable that in order to utilize the model i should reduce the context size with Parameter num_ctx 32k.
However when i utilize num_ctx to reduce the context size to 32k from 132k, ollama generates a much larger model. 15GB instead of 10GB for the default parameters. What could be wrong here. Reducing the context size should have perhaps reduced the size of the model generated. Is there something wrong that i have done?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
_No response_
|
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PR_kwDOJ0Z1Ps51jus4
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OpenAI: update message processing
|
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| 2024-07-16T20:25:11
| 2024-07-19T18:19:21
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6D6kv8
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|
Loading the model on VM from attached volumes is extremely slow
|
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| 2024-06-01T22:39:50
| 2024-06-01T22:39:46
|
NONE
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### What is the issue?
When pulling the model and running it the first time everything works fine.
However, after deallocating the VM and starting it again (attaching a permanent disk with Ollama models downloaded) it takes more than 20 minutes to load any large model.
It seems it's loading it to the CPU first with a speed of 100 MB per second.
It doesn't happen when I download a new model with "ollama pull" && "ollama run", only with models that were attached.
### What did you expect to see?
Same loading time as after downloading the model
### Steps to reproduce
- Install Ollama on VM Ubuntu 22.04
- ollama pull llama2:70b
- ollama run llama2:70b _--> loads fast_
- restart VM (deallocate)
- ollama run llama2:70b --> _takes 20x longer to start_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Linux
### Architecture
x86
### Platform
_No response_
### Ollama version
0.1.28
### GPU
Nvidia
### GPU info
Nvidia A100
### CPU
_No response_
### Other software
_No response_
|
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I_kwDOJ0Z1Ps6ej7S7
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|
Moondream v2 (CPU) crashes with images (post predict EOF error) on 0.4.1
|
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| 2024-11-14T22:31:06
| 2024-11-14T22:42:45
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NONE
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### What is the issue?
Moondream v2 seems to run into an issue with images on CPU with 0.4.1,
resulting in `Error: POST predict: Post "http://127.0.0.1:33685/completion": EOF`.
Does not seem to affect GPU.
```
~ $ ollama run moondream:v2 "please describe this image /home/kwi/demo-2.png" --verbose
Added image '/home/kwi/demo-2.png'
Error: POST predict: Post "http://127.0.0.1:34833/completion": EOF
~ $ ollama run moondream:v2 hi
Hi
$ ollama --version
ollama version is 0.4.1
```
Only seems to happen with images, not text. The model loads and runs fine for text chat, but consistently crashes with EOF when attempting to process any image.
journalctl:
`level=DEBUG source=server.go:423 msg="llama runner terminated" error="signal: aborted"`
What i tried so far.
- Differen images
- Different CPUS (Ryzen 5 6600H, Ryzen 7 5700X, i5-3320M)
- Other vision models (minicpm-v and llama seem to work fine)
- Different quants
- CLI and through API
I had an older laptop around with an outdated ollama 0.3.1 on it, that still worked fine. Upgrading to 0.4.1 caused the exact same error to appear as well.
### OS
Linux
### GPU
_No response_
### CPU
Intel, AMD
### Ollama version
0.4.1
|
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PR_kwDOJ0Z1Ps5jpcGg
| 1,885
|
Update submodule to `6efb8eb30e7025b168f3fda3ff83b9b386428ad6`
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| 2024-01-10T06:19:01
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I_kwDOJ0Z1Ps6Bpjef
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ollama pull qwen:1.8b error：Error: Head "https://registry.ollama.ai/v2/library/qwen/blobs/sha256:1296b084ed6bc4c6eaee99255d73e9c715d38e0087b6467fd1c498b908180614": unexpected EOF
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ollama pull qwen:1.8b error：
Error: Head "https://registry.ollama.ai/v2/library/qwen/blobs/sha256:1296b084ed6bc4c6eaee99255d73e9c715d38e0087b6467fd1c498b908180614": unexpected EOF
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I_kwDOJ0Z1Ps50gBfl
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Better Doc / Explanation and Examples of Template Syntax
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Really like ollama for is simple setup and usage with both a CLI and API.
The only thing that has tripped me up is getting the modelfile template correct for an imported model. It could be my inexperience but the documentation seems very sparse. I have been unable to get the JackalopeAI (on HuggingFace) to run after numeration attempts. The system goes into a loop of asking it's questions and proceeding to answer them.
It would be helpful if all the full grammar of the modelfile was documented including the special symbols used by the template.
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PR_kwDOJ0Z1Ps5se07_
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Install Ollama on OSTree systems
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CONTRIBUTOR
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There's a large plethora of OSTree OSes in the Fedora family:
Silverblue, Kinoite, CoreOS, IoT, Onyx, Sericea, Vauxite
In the CentOS Stream family:
Automotive Stream Distribution, CoreOS
In the Red Hat family:
Red Hat In-Vehicle Operating System, Red Hat Enterprise Linux CoreOS, RHEL for Edge
Then there's the Universal Blue family:
Things like podman-machine and podman-desktop on Windows and macOS use Fedora CoreOS as the host OS so there is that also.
The list goes on and on.
These OSes are ideal for containerized AI LLMs like Ollama.
I eventually got this working with podman, which is probably the best route to use on these OSes (or an rpm when someone packages it).
This change gets the install script working.
/usr/bin and /usr/share aren't writable on these systems, but /usr/local/bin and /usr/local/share are.
So this change ensures if /usr/local/bin is used during installation, that we also use /usr/local/share, if /usr/local/share exists. And then everything seems to work fine on these OSes for non-containerized installs.
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PR_kwDOJ0Z1Ps6Evv7T
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|
readme: include IBM Granite models
|
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I_kwDOJ0Z1Ps6CMSVF
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|
OpenAI API and templates
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Hi,
Does the new OpenAI API compatible endpoint `/v1/chat/completions` uses the default templates defined in the Modefile ?
Thank you
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I_kwDOJ0Z1Ps6bu-ws
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|
add termux compile instructions to web page
|
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pkg ugrade -y golang clang cmake libandroid-execinfo gzip git
git clone https://github.com/ollama/ollama ollama
cd ollama
go generate ./...
go build .
cp ollama ~/../usr/bin
this used to work to 0.3.13 then .14 the err came but i believe u change well , err will be gone , cmd working again ..
greets ..
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server: fix Transport override
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| 2024-11-25T22:48:36
| 2024-11-25T23:08:36
| 2024-11-25T23:08:34
|
CONTRIBUTOR
|
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This changes makeRequest to update the http client Transport if and only if testMakeRequestDialContext is set. This is to avoid overriding the default Transport when testMakeRequestDialContext is nil, which broke existing behavior, included proxies, timeouts, and other behaviors.
Fixes #7829
Fixes #7788
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| 2,651,692,740
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I_kwDOJ0Z1Ps6eDZ7E
| 7,627
|
support multiple lora adapters
|
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llama.cpp support multiple adapters, see https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md
why ollama support only one adapter?
https://github.com/ollama/ollama/blob/65973ceb6417c2e2796fa59bd3225bc7bd79b403/llm/server.go#L203-L206
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I_kwDOJ0Z1Ps5z6idQ
| 797
|
Support GPU on older NVIDIA GPU and CUDA drivers
|
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| 2024-02-26T11:53:38
| 2023-11-28T21:26:44
|
NONE
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I am testing using ollama on linux and docker, and its not using the GPU at all.
it appears that ollma is not using the CUDA image.
I resolved the issue by replacing the base image.
https://github.com/jmorganca/ollama/blob/92578798bb1abcedd6bc99479d804f32d9ee2f6c/Dockerfile#L17-L23
change ubuntu:22.04 to nvidia/cuda:11.8.0-devel-ubuntu22.04
and then it works
Perhaps we can build a GPU image and push it to the community, using the "gpu" tag for differentiation.
|
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I_kwDOJ0Z1Ps6lqA2Y
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ollama not working
|
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| 2025-01-10T04:27:15
| 2025-01-10T23:57:31
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NONE
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### What is the issue?
I installed ollama in ubuntu via curl command but not working when using ollama. So I check ollama version.>ollama -v ollama version is 0.0.0 Warning: client version is 0.5.0
### OS
Linux
### GPU
_No response_
### CPU
AMD
### Ollama version
_No response_
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| 2,418,160,984
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I_kwDOJ0Z1Ps6QIjVY
| 5,787
|
ollama run deepseek-coder-v2 creates gibberish output
|
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| 2024-07-19T06:40:47
| 2024-09-17T01:39:45
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NONE
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### What is the issue?
Hi,
I am trying to run the 16b ollama deepseek-coder-v2, which leads to a "gibberish" output.
Strangely enough it works after a fresh download, but then after trying to run it in Aider it doesnt.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.2.7
|
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I_kwDOJ0Z1Ps53otq6
| 1,247
|
Better validation for model names in `ollama create` and `ollama cp`
|
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| 2023-11-22T21:49:44
| 2023-11-29T20:54:30
| 2023-11-29T20:54:30
|
MEMBER
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Today creating `ollama create mymodel:my:tag` will work
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PR_kwDOJ0Z1Ps5Z0rrI
| 490
|
Add OLLAMA_HOME environment variable support.
|
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| 2023-11-03T16:57:16
| 2023-10-25T22:34:23
|
CONTRIBUTOR
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## Problem
I'd like to run Ollama on my Linux server, but I have a small home directory disk. As a result, rather than changing the home directory to my mass storage pool, I propose adding the environment variable ```OLLAMA_HOME``` to set the top-level filepath for Ollama.
## Change
Switch out os.UserHomeDir with a wrapper in a new `util` package.
`util.UserHomeDir` attempts to fetch the OLLAMA_HOME environment variable, and falls back otherwise.
Add documentation under `faq.md`.
## Tests
Tested manually. I'd be happy to add automated tests for this if existing infrastructure exists.
|
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I_kwDOJ0Z1Ps6OPzT5
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Updates to Phi-3 mini 4k/128k
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Microsoft updated both checkpoints:
[https://huggingface.co/microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
[https://huggingface.co/microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
> Release Notes
> This is an update over the original instruction-tuned Phi-3-mini release based on valuable customer feedback. The model used additional post-training data leading to substantial gains on long-context understanding, instruction following, and structure output. We also improve multi-turn conversation quality, explicitly support <|system|> tag, and significantly improve reasoning capability. We believe most use cases will benefit from this release, but we encourage users to test in their particular AI applications. We appreciate the enthusiastic adoption of the Phi-3 model family, and continue to welcome all feedback from the community.
### Benchmarks
| Benchmarks | Original | June 2024 Update |
|---------------------------|----------|------------------|
| Instruction Extra Hard | 5.7 | 5.9 |
| Instruction Hard | 5.0 | 5.2 |
| JSON Structure Output | 1.9 | 60.1 |
| XML Structure Output | 47.8 | 52.9 |
| GPQA | 25.9 | 29.7 |
| MMLU | 68.1 | 69.7 |
| **Average** | **25.7** | **37.3** |
### RULER: a retrieval-based benchmark for long context understanding
| Model | 4K | 8K | 16K | 32K | 64K | 128K | Average |
|-------------------|------|------|------|------|------|------|---------|
| Original | 86.7 | 78.1 | 75.6 | 70.3 | 58.9 | 43.3 | 68.8 |
| June 2024 Update | 92.4 | 91.1 | 90.8 | 87.9 | 79.8 | 65.6 | 84.6 |
### RepoQA: a benchmark for long context code understanding
| Model | Python | C++ | Rust | Java | TypeScript | Average |
|------------------|--------|------|------|------|------------|---------|
| Original | 27 | 29 | 40 | 33 | 33 | 32.4 |
| June 2024 Update | 85 | 63 | 72 | 93 | 72 | 77 |
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PR_kwDOJ0Z1Ps6Cpn-0
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|
ppc64le: corrected ioctls
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NONE
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As described in #796 `ollama run` won't work on ppc64le out of the box, as the ioctl `TCSETS` is invalid.
This PR changes the ioctl to `TCSETSF` while also moving it away from "magic numbers".
According to man pages:
```
TCSETSF
Equivalent to tcsetattr(fd, TCSAFLUSH, argp).
Allow the output buffer to drain, discard pending input,
and set the current serial port settings.
```
I've tested this change on an x64 CPU as well, and saw no issues/regressions.
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add version
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6kxPjW
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Ollama hangs without timeout, Ollama model is consuming full CPU or GPU
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### What is the issue?
Doing Ollama connections with langchain the API hangs, depending on the input.
No information is loggend on the Ollama serve side with debug option 3.
I have attached a test case (Python program, test image, and README) to reproduce it.
[ollama_hang.tgz](https://github.com/user-attachments/files/18281727/ollama_hang.tgz)
Changing the prompt, may change the situation (see [c't Forum](https://www.heise.de/forum/heise-online/Kommentare/Wie-eine-lokale-KI-die-Fotosammlung-auf-dem-NAS-verschlagworten-kann/Haengt-sich-bei-jedem-2-3-Bild-auf/thread-7564685/#posting_43932391))
### OS
Linux, macOS
### GPU
Intel, Apple
### CPU
AMD, Apple
### Ollama version
0.5.4
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upgrade openchat
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| 2024-01-10T10:40:27
| 2024-01-11T16:52:21
| 2024-01-11T00:09:38
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hello a new release of openchat was released :
https://huggingface.co/openchat/openchat-3.5-0106#benchmarks
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I_kwDOJ0Z1Ps6VyLzz
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Reflection 70B has significant issue with the weights
|
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| 2024-09-09T05:43:27
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The whole drama is described here:
https://x.com/shinboson/status/1832933753837982024
sorry for recommending the model, I was unaware of that and easily got into the hype
it's still possible that's just technical issue, but I'm suspicious:
https://x.com/rohanpaul_ai/status/1833094994929897862/photo/1
|
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I_kwDOJ0Z1Ps6myfzz
| 8,497
|
Repository for tyllama/kevin?
|
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| 2025-01-20T05:51:59
| 2025-01-20T07:49:47
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NONE
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The ollama library has the tyllama/kevin model: https://ollama.com/tyllama/kevin The description says that it can be installed from the repository, with the ability to remember previous dialogs and learn from them. But I didn't find that repository. Does it still exist? Removed, renamed?
| null |
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PR_kwDOJ0Z1Ps5vUcKA
| 4,412
|
Document older win10 terminal problems
|
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| 2024-05-13T22:10:03
| 2024-07-05T15:18:25
| 2024-07-05T15:18:22
|
COLLABORATOR
|
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We haven't found a workaround, so for now recommend updating.
Fixes #3916
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I_kwDOJ0Z1Ps6LdGiK
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|
Error removing model
|
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| 2024-06-07T06:11:34
| 2024-06-10T18:40:04
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MEMBER
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### What is the issue?
```
ollama run wizardcoder:34b-python
ollama rm wizardcoder:34b-python
Error: remove /usr/share/ollama/.ollama/models/blobs/sha256-a168bedb9a09640289c5174690a6221adae48b75dc431a219923f052ef20d0af: no such file or directory
```
### OS
Linux
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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I_kwDOJ0Z1Ps6StQrZ
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|
Feature request : get probability distribution
|
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| 2024-08-12T15:42:02
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I have a prompt and then i get an answer. In some part of answer is a JSON, something like this:
```
{
res:"yes"
}
```
or this:
```
{
res:"no"
}
```
I want to know the probability of token "yes" and "no". and use these probabilities in some algorithm.
|
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| 2,149,346,646
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I_kwDOJ0Z1Ps6AHG1W
| 2,681
|
ollama 运行 orca-迷你模型完成后的问题
|
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closed
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[] | null | 1
| 2024-02-22T15:35:33
| 2024-03-03T22:41:54
| 2024-02-22T16:03:46
|
NONE
|
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Error: Unable to load dynamic library: Unable to load dynamic server library: �Ҳ���ָ����ģ�顣
|
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I_kwDOJ0Z1Ps58C9nX
| 1,997
|
:back: Some kind of regression while running on some LlamaIndex versions (Kaggle & Killercoda)
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| 2024-01-15T02:52:36
| 2024-11-18T21:08:50
| 2024-05-10T01:03:01
|
NONE
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# :grey_question: About
While working on a `ollama` tutorial on Kaggle, since a few days, I faced a regression while working with LlamaIndex.
Here is the output I could get on any model (worked everytime)
... vs now (the code is now broken, and it fails consistetly):
# :information_source:
- :heavy_check_mark: Everything works perfectly well on my laptop
:thinking: Looks like something changed that causes this "regression" while playing around in some cases :thought_balloon:
# :tickets: Potentially related issues
- https://github.com/jmorganca/ollama/issues/1478
- https://github.com/jmorganca/ollama/issues/1641
- https://github.com/jmorganca/ollama/issues/1550
- https://github.com/jmorganca/ollama/pull/1146
## :scroll: Detailed stacktrace
```
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/httpcore/_exceptions.py:10, in map_exceptions(map)
9 try:
---> 10 yield
11 except Exception as exc: # noqa: PIE786
File /opt/conda/lib/python3.10/site-packages/httpcore/_backends/sync.py:206, in SyncBackend.connect_tcp(self, host, port, timeout, local_address, socket_options)
205 with map_exceptions(exc_map):
--> 206 sock = socket.create_connection(
207 address,
208 timeout,
209 source_address=source_address,
210 )
211 for option in socket_options:
File /opt/conda/lib/python3.10/socket.py:845, in create_connection(address, timeout, source_address)
844 try:
--> 845 raise err
846 finally:
847 # Break explicitly a reference cycle
File /opt/conda/lib/python3.10/socket.py:833, in create_connection(address, timeout, source_address)
832 sock.bind(source_address)
--> 833 sock.connect(sa)
834 # Break explicitly a reference cycle
OSError: [Errno 99] Cannot assign requested address
The above exception was the direct cause of the following exception:
ConnectError Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/httpx/_transports/default.py:67, in map_httpcore_exceptions()
66 try:
---> 67 yield
68 except Exception as exc:
File /opt/conda/lib/python3.10/site-packages/httpx/_transports/default.py:231, in HTTPTransport.handle_request(self, request)
230 with map_httpcore_exceptions():
--> 231 resp = self._pool.handle_request(req)
233 assert isinstance(resp.stream, typing.Iterable)
File /opt/conda/lib/python3.10/site-packages/httpcore/_sync/connection_pool.py:268, in ConnectionPool.handle_request(self, request)
267 self.response_closed(status)
--> 268 raise exc
269 else:
File /opt/conda/lib/python3.10/site-packages/httpcore/_sync/connection_pool.py:251, in ConnectionPool.handle_request(self, request)
250 try:
--> 251 response = connection.handle_request(request)
252 except ConnectionNotAvailable:
253 # The ConnectionNotAvailable exception is a special case, that
254 # indicates we need to retry the request on a new connection.
(...)
258 # might end up as an HTTP/2 connection, but which actually ends
259 # up as HTTP/1.1.
File /opt/conda/lib/python3.10/site-packages/httpcore/_sync/connection.py:99, in HTTPConnection.handle_request(self, request)
98 self._connect_failed = True
---> 99 raise exc
100 elif not self._connection.is_available():
File /opt/conda/lib/python3.10/site-packages/httpcore/_sync/connection.py:76, in HTTPConnection.handle_request(self, request)
75 try:
---> 76 stream = self._connect(request)
78 ssl_object = stream.get_extra_info("ssl_object")
File /opt/conda/lib/python3.10/site-packages/httpcore/_sync/connection.py:124, in HTTPConnection._connect(self, request)
123 with Trace("connect_tcp", logger, request, kwargs) as trace:
--> 124 stream = self._network_backend.connect_tcp(**kwargs)
125 trace.return_value = stream
File /opt/conda/lib/python3.10/site-packages/httpcore/_backends/sync.py:205, in SyncBackend.connect_tcp(self, host, port, timeout, local_address, socket_options)
200 exc_map: ExceptionMapping = {
201 socket.timeout: ConnectTimeout,
202 OSError: ConnectError,
203 }
--> 205 with map_exceptions(exc_map):
206 sock = socket.create_connection(
207 address,
208 timeout,
209 source_address=source_address,
210 )
File /opt/conda/lib/python3.10/contextlib.py:153, in _GeneratorContextManager.__exit__(self, typ, value, traceback)
152 try:
--> 153 self.gen.throw(typ, value, traceback)
154 except StopIteration as exc:
155 # Suppress StopIteration *unless* it's the same exception that
156 # was passed to throw(). This prevents a StopIteration
157 # raised inside the "with" statement from being suppressed.
File /opt/conda/lib/python3.10/site-packages/httpcore/_exceptions.py:14, in map_exceptions(map)
13 if isinstance(exc, from_exc):
---> 14 raise to_exc(exc) from exc
15 raise
ConnectError: [Errno 99] Cannot assign requested address
The above exception was the direct cause of the following exception:
ConnectError Traceback (most recent call last)
Cell In[13], line 5
2 from llama_index.llms import Ollama
4 llm = Ollama(model=OLLAMA_MODEL)
----> 5 response = llm.complete("""Who is Grigori Perelman and why is he so important in mathematics?
6 (Answer with markdown sections, markdown with be the GitHub flavor.)""")
7 print(response)
File /opt/conda/lib/python3.10/site-packages/llama_index/llms/base.py:226, in llm_completion_callback.<locals>.wrap.<locals>.wrapped_llm_predict(_self, *args, **kwargs)
216 with wrapper_logic(_self) as callback_manager:
217 event_id = callback_manager.on_event_start(
218 CBEventType.LLM,
219 payload={
(...)
223 },
224 )
--> 226 f_return_val = f(_self, *args, **kwargs)
227 if isinstance(f_return_val, Generator):
228 # intercept the generator and add a callback to the end
229 def wrapped_gen() -> CompletionResponseGen:
File /opt/conda/lib/python3.10/site-packages/llama_index/llms/ollama.py:180, in Ollama.complete(self, prompt, formatted, **kwargs)
171 payload = {
172 self.prompt_key: prompt,
173 "model": self.model,
(...)
176 **kwargs,
177 }
179 with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
--> 180 response = client.post(
181 url=f"{self.base_url}/api/generate",
182 json=payload,
183 )
184 response.raise_for_status()
185 raw = response.json()
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:1146, in Client.post(self, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)
1125 def post(
1126 self,
1127 url: URLTypes,
(...)
1139 extensions: typing.Optional[RequestExtensions] = None,
1140 ) -> Response:
1141 """
1142 Send a `POST` request.
1143
1144 **Parameters**: See `httpx.request`.
1145 """
-> 1146 return self.request(
1147 "POST",
1148 url,
1149 content=content,
1150 data=data,
1151 files=files,
1152 json=json,
1153 params=params,
1154 headers=headers,
1155 cookies=cookies,
1156 auth=auth,
1157 follow_redirects=follow_redirects,
1158 timeout=timeout,
1159 extensions=extensions,
1160 )
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:828, in Client.request(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)
813 warnings.warn(message, DeprecationWarning)
815 request = self.build_request(
816 method=method,
817 url=url,
(...)
826 extensions=extensions,
827 )
--> 828 return self.send(request, auth=auth, follow_redirects=follow_redirects)
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:915, in Client.send(self, request, stream, auth, follow_redirects)
907 follow_redirects = (
908 self.follow_redirects
909 if isinstance(follow_redirects, UseClientDefault)
910 else follow_redirects
911 )
913 auth = self._build_request_auth(request, auth)
--> 915 response = self._send_handling_auth(
916 request,
917 auth=auth,
918 follow_redirects=follow_redirects,
919 history=[],
920 )
921 try:
922 if not stream:
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:943, in Client._send_handling_auth(self, request, auth, follow_redirects, history)
940 request = next(auth_flow)
942 while True:
--> 943 response = self._send_handling_redirects(
944 request,
945 follow_redirects=follow_redirects,
946 history=history,
947 )
948 try:
949 try:
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:980, in Client._send_handling_redirects(self, request, follow_redirects, history)
977 for hook in self._event_hooks["request"]:
978 hook(request)
--> 980 response = self._send_single_request(request)
981 try:
982 for hook in self._event_hooks["response"]:
File /opt/conda/lib/python3.10/site-packages/httpx/_client.py:1016, in Client._send_single_request(self, request)
1011 raise RuntimeError(
1012 "Attempted to send an async request with a sync Client instance."
1013 )
1015 with request_context(request=request):
-> 1016 response = transport.handle_request(request)
1018 assert isinstance(response.stream, SyncByteStream)
1020 response.request = request
File /opt/conda/lib/python3.10/site-packages/httpx/_transports/default.py:230, in HTTPTransport.handle_request(self, request)
216 assert isinstance(request.stream, SyncByteStream)
218 req = httpcore.Request(
219 method=request.method,
220 url=httpcore.URL(
(...)
228 extensions=request.extensions,
229 )
--> 230 with map_httpcore_exceptions():
231 resp = self._pool.handle_request(req)
233 assert isinstance(resp.stream, typing.Iterable)
File /opt/conda/lib/python3.10/contextlib.py:153, in _GeneratorContextManager.__exit__(self, typ, value, traceback)
151 value = typ()
152 try:
--> 153 self.gen.throw(typ, value, traceback)
154 except StopIteration as exc:
155 # Suppress StopIteration *unless* it's the same exception that
156 # was passed to throw(). This prevents a StopIteration
157 # raised inside the "with" statement from being suppressed.
158 return exc is not value
File /opt/conda/lib/python3.10/site-packages/httpx/_transports/default.py:84, in map_httpcore_exceptions()
81 raise
83 message = str(exc)
---> 84 raise mapped_exc(message) from exc
ConnectError: [Errno 99] Cannot assign requested address
```
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| 1,978,789,553
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PR_kwDOJ0Z1Ps5eq1mp
| 1,015
|
Update api.md
|
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| 2023-11-06T10:24:18
| 2023-11-29T21:21:58
| 2023-11-29T21:21:58
|
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Fixed documentation, responds one token for streamed results
|
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PR_kwDOJ0Z1Ps50nkSy
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llm: remove ambiguous log message when placing an upper limit on predictions
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PR_kwDOJ0Z1Ps5ncl29
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API doc formatting updates
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| 2024-02-20T21:40:43
| 2024-05-07T17:49:02
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CONTRIBUTOR
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- in preparation for rendering on ollama.com
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I_kwDOJ0Z1Ps6XackV
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No ollama model can recognize the referenced information.
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### What is the issue?
Scene One
By calling a public cloud-based LLM model through an AI Agent, two documents exceeding 2000 words each are uploaded, and the input question is: Analyze the differences between the two documents. In this manner, the model can normally analyze the differences between the two documents.
Scene Two
If the locally deployed LLM model of olalma0.3.3 is called (multiple different models have been tried), with the same documents and the same question, the model indicates that it cannot find the documents for comparison.
If the document content is reduced to around 1000 words, the model can then compare normally.
The model's maxContext and maxResponse have been adjusted from small to large, with no effect.
Scene Three: When uploading a document exceeding 2000 words and asking the ollama local model to summarize the content, the same issue arises. However, if the document is reduced to around 1000 words, the ollama local model can analyze it normally.
Despite trying multiple ollama models and adjusting maxContext and maxResponse from 2000 to 30000, the problem persists.
the log message as followed,
(base) [root@gpu ~]# journalctl -u ollama -r
-- Logs begin at 一 2024-09-02 03:24:01 CST, end at 六 2024-09-21 21:59:04 CST. --
9月 21 21:59:04 gpu ollama[48923]: [GIN] 2024/09/21 - 21:59:04 | 200 | 11.452294657s | 172.16.1.219 | POST "/v1/chat/completions"
9月 21 21:59:01 gpu ollama[48923]: time=2024-09-21T21:59:01.646+08:00 level=INFO source=server.go:623 msg="llama runner started in 6.78 seconds"
9月 21 21:59:01 gpu ollama[48923]: INFO [main] model loaded | tid="140514816995328" timestamp=1726927141
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: graph splits = 2
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: graph nodes = 1850
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: CUDA_Host compute buffer size = 41.01 MiB
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: CUDA0 compute buffer size = 578.00 MiB
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: CUDA_Host output buffer size = 3.98 MiB
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: KV self size = 2944.00 MiB, K (f16): 1472.00 MiB, V (f16): 1472.00 MiB
9月 21 21:59:00 gpu ollama[48923]: llama_kv_cache_init: CUDA0 KV buffer size = 2944.00 MiB
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: freq_scale = 1
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: freq_base = 10000.0
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: flash_attn = 0
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: n_ubatch = 512
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: n_batch = 512
9月 21 21:59:00 gpu ollama[48923]: llama_new_context_with_model: n_ctx = 8192
9月 21 21:58:58 gpu ollama[48923]: time=2024-09-21T21:58:58.185+08:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm serve
9月 21 21:58:58 gpu ollama[48923]: llm_load_tensors: CUDA0 buffer size = 14898.60 MiB
9月 21 21:58:58 gpu ollama[48923]: llm_load_tensors: CPU buffer size = 922.85 MiB
9月 21 21:58:58 gpu ollama[48923]: llm_load_tensors: offloaded 47/47 layers to GPU
9月 21 21:58:58 gpu ollama[48923]: llm_load_tensors: offloading non-repeating layers to GPU
9月 21 21:58:58 gpu ollama[48923]: llm_load_tensors: offloading 46 repeating layers to GPU
9月 21 21:58:56 gpu ollama[48923]: time=2024-09-21T21:58:56.580+08:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm serve
9月 21 21:58:56 gpu ollama[48923]: llm_load_tensors: ggml ctx size = 0.45 MiB
9月 21 21:58:55 gpu ollama[48923]: Device 0: NVIDIA A30, compute capability 8.0, VMM: yes
9月 21 21:58:55 gpu ollama[48923]: ggml_cuda_init: found 1 CUDA devices:
9月 21 21:58:55 gpu ollama[48923]: ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
9月 21 21:58:55 gpu ollama[48923]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: max token length = 93
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: EOT token = 107 '<end_of_turn>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: LF token = 227 '<0x0A>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: PAD token = 0 '<pad>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: UNK token = 3 '<unk>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: EOS token = 1 '<eos>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: BOS token = 2 '<bos>'
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: general.name = gemma-2-27b-it
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: model size = 14.55 GiB (4.59 BPW)
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: model params = 27.23 B
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: model ftype = Q4_0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: model type = 27B
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: ssm_dt_rank = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: ssm_d_state = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: ssm_d_inner = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: ssm_d_conv = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: rope_finetuned = unknown
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_ctx_orig_yarn = 8192
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: freq_scale_train = 1
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: freq_base_train = 10000.0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: rope scaling = linear
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: rope type = 2
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: pooling type = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: causal attn = 1
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_expert_used = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_expert = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_ff = 36864
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: f_logit_scale = 0.0e+00
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: f_norm_rms_eps = 1.0e-06
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: f_norm_eps = 0.0e+00
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_embd_v_gqa = 2048
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_embd_k_gqa = 2048
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_gqa = 2
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_embd_head_v = 128
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_embd_head_k = 128
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_swa = 4096
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_rot = 128
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_head_kv = 16
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_head = 32
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_layer = 46
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_embd = 4608
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_ctx_train = 8192
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: vocab_only = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_merges = 0
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: n_vocab = 256000
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: vocab type = SPM
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: arch = gemma2
9月 21 21:58:55 gpu ollama[48923]: llm_load_print_meta: format = GGUF V3 (latest)
9月 21 21:58:55 gpu ollama[48923]: llm_load_vocab: token to piece cache size = 1.6014 MB
9月 21 21:58:55 gpu ollama[48923]: llm_load_vocab: special tokens cache size = 108
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - type q6_K: 1 tensors
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - type q4_0: 322 tensors
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - type f32: 185 tensors
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 28: general.quantization_version u32 = 2
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 27: tokenizer.ggml.add_space_prefix bool = false
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 26: tokenizer.chat_template str = {{ bos_token }}{% if messages[0]['rol
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 25: tokenizer.ggml.add_eos_token bool = false
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 24: tokenizer.ggml.add_bos_token bool = true
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 0
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 22: tokenizer.ggml.unknown_token_id u32 = 3
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 1
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 2
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 19: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 18: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000
9月 21 21:58:55 gpu ollama[48923]: time=2024-09-21T21:58:55.122+08:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm serve
9月 21 21:58:55 gpu ollama[48923]: llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>",
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 16: tokenizer.ggml.pre str = default
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 15: tokenizer.ggml.model str = llama
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 14: gemma2.attention.sliding_window u32 = 4096
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 13: gemma2.final_logit_softcapping f32 = 30.000000
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 12: gemma2.attn_logit_softcapping f32 = 50.000000
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 11: general.file_type u32 = 2
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 10: gemma2.attention.value_length u32 = 128
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 9: gemma2.attention.key_length u32 = 128
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 8: gemma2.attention.layer_norm_rms_epsilon f32 = 0.000001
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 7: gemma2.attention.head_count_kv u32 = 16
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 6: gemma2.attention.head_count u32 = 32
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 5: gemma2.feed_forward_length u32 = 36864
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 4: gemma2.block_count u32 = 46
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 3: gemma2.embedding_length u32 = 4608
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 2: gemma2.context_length u32 = 8192
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 1: general.name str = gemma-2-27b-it
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: - kv 0: general.architecture str = gemma2
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
9月 21 21:58:54 gpu ollama[48923]: llama_model_loader: loaded meta data with 29 key-value pairs and 508 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-d
9月 21 21:58:54 gpu ollama[48923]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="63" port="34781" tid="140514816995328" timestamp=1726927
9月 21 21:58:54 gpu ollama[48923]: INFO [main] system info | n_threads=32 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VB
9月 21 21:58:54 gpu ollama[48923]: INFO [main] build info | build=1 commit="6eeaeba" tid="140514816995328" timestamp=1726927134
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.869+08:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm serve
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.864+08:00 level=INFO source=server.go:584 msg="waiting for llama runner to start responding"
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.863+08:00 level=INFO source=sched.go:445 msg="loaded runners" count=1
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.863+08:00 level=INFO source=server.go:384 msg="starting llama server" cmd="/tmp/ollama242898797/runners/
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.862+08:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=47 laye
9月 21 21:58:54 gpu ollama[48923]: time=2024-09-21T21:58:54.861+08:00 level=INFO source=sched.go:710 msg="new model will fit in available VRAM in single GPU, loading
### upgrate to 0.3.11 , the same issue with log as followed
9月 21 22:39:08 gpu ollama[54696]: [GIN] 2024/09/21 - 22:39:08 | 200 | 10.260377093s | 172.16.1.219 | POST "/v1/chat/completions"
9月 21 22:39:05 gpu ollama[54696]: time=2024-09-21T22:39:05.203+08:00 level=INFO source=server.go:626 msg="llama runner started in 5.67 seconds"
9月 21 22:39:05 gpu ollama[54696]: INFO [main] model loaded | tid="140149569400832" timestamp=1726929545
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: graph splits = 2
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: graph nodes = 1850
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: CUDA_Host compute buffer size = 41.01 MiB
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: CUDA0 compute buffer size = 578.00 MiB
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: CUDA_Host output buffer size = 3.98 MiB
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: KV self size = 2944.00 MiB, K (f16): 1472.00 MiB, V (f16): 1472.00 MiB
9月 21 22:39:04 gpu ollama[54696]: llama_kv_cache_init: CUDA0 KV buffer size = 2944.00 MiB
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: freq_scale = 1
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: freq_base = 10000.0
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: flash_attn = 0
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: n_ubatch = 512
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: n_batch = 512
9月 21 22:39:04 gpu ollama[54696]: llama_new_context_with_model: n_ctx = 8192
9月 21 22:39:02 gpu ollama[54696]: time=2024-09-21T22:39:02.405+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm serve
9月 21 22:39:02 gpu ollama[54696]: llm_load_tensors: CUDA0 buffer size = 14898.60 MiB
9月 21 22:39:02 gpu ollama[54696]: llm_load_tensors: CPU buffer size = 922.85 MiB
9月 21 22:39:02 gpu ollama[54696]: llm_load_tensors: offloaded 47/47 layers to GPU
9月 21 22:39:02 gpu ollama[54696]: llm_load_tensors: offloading non-repeating layers to GPU
9月 21 22:39:02 gpu ollama[54696]: llm_load_tensors: offloading 46 repeating layers to GPU
9月 21 22:39:01 gpu ollama[54696]: time=2024-09-21T22:39:01.250+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm serve
9月 21 22:39:00 gpu ollama[54696]: llm_load_tensors: ggml ctx size = 0.45 MiB
9月 21 22:39:00 gpu ollama[54696]: Device 0: NVIDIA A30, compute capability 8.0, VMM: yes
9月 21 22:39:00 gpu ollama[54696]: ggml_cuda_init: found 1 CUDA devices:
9月 21 22:39:00 gpu ollama[54696]: ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
9月 21 22:39:00 gpu ollama[54696]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: max token length = 93
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: EOT token = 107 '<end_of_turn>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: LF token = 227 '<0x0A>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: PAD token = 0 '<pad>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: UNK token = 3 '<unk>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: EOS token = 1 '<eos>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: BOS token = 2 '<bos>'
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: general.name = gemma-2-27b-it
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: model size = 14.55 GiB (4.59 BPW)
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: model params = 27.23 B
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: model ftype = Q4_0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: model type = 27B
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: ssm_dt_b_c_rms = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: ssm_dt_rank = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: ssm_d_state = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: ssm_d_inner = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: ssm_d_conv = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: rope_finetuned = unknown
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_ctx_orig_yarn = 8192
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: freq_scale_train = 1
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: freq_base_train = 10000.0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: rope scaling = linear
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: rope type = 2
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: pooling type = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: causal attn = 1
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_expert_used = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_expert = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_ff = 36864
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: f_logit_scale = 0.0e+00
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: f_norm_rms_eps = 1.0e-06
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: f_norm_eps = 0.0e+00
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_embd_v_gqa = 2048
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_embd_k_gqa = 2048
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_gqa = 2
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_embd_head_v = 128
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_embd_head_k = 128
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_swa = 4096
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_rot = 128
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_head_kv = 16
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_head = 32
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_layer = 46
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_embd = 4608
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_ctx_train = 8192
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: vocab_only = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_merges = 0
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: n_vocab = 256000
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: vocab type = SPM
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: arch = gemma2
9月 21 22:39:00 gpu ollama[54696]: llm_load_print_meta: format = GGUF V3 (latest)
9月 21 22:39:00 gpu ollama[54696]: llm_load_vocab: token to piece cache size = 1.6014 MB
9月 21 22:39:00 gpu ollama[54696]: llm_load_vocab: special tokens cache size = 108
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - type q6_K: 1 tensors
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - type q4_0: 322 tensors
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - type f32: 185 tensors
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 28: general.quantization_version u32 = 2
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 27: tokenizer.ggml.add_space_prefix bool = false
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 26: tokenizer.chat_template str = {{ bos_token }}{% if messages[0]['rol
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 25: tokenizer.ggml.add_eos_token bool = false
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 24: tokenizer.ggml.add_bos_token bool = true
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 0
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 22: tokenizer.ggml.unknown_token_id u32 = 3
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 1
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 2
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 19: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 18: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>",
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.792+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm serve
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 16: tokenizer.ggml.pre str = default
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 15: tokenizer.ggml.model str = llama
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 14: gemma2.attention.sliding_window u32 = 4096
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 13: gemma2.final_logit_softcapping f32 = 30.000000
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 12: gemma2.attn_logit_softcapping f32 = 50.000000
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 11: general.file_type u32 = 2
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 10: gemma2.attention.value_length u32 = 128
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 9: gemma2.attention.key_length u32 = 128
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 8: gemma2.attention.layer_norm_rms_epsilon f32 = 0.000001
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 7: gemma2.attention.head_count_kv u32 = 16
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 6: gemma2.attention.head_count u32 = 32
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 5: gemma2.feed_forward_length u32 = 36864
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 4: gemma2.block_count u32 = 46
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 3: gemma2.embedding_length u32 = 4608
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 2: gemma2.context_length u32 = 8192
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 1: general.name str = gemma-2-27b-it
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: - kv 0: general.architecture str = gemma2
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
9月 21 22:38:59 gpu ollama[54696]: llama_model_loader: loaded meta data with 29 key-value pairs and 508 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-d
9月 21 22:38:59 gpu ollama[54696]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="63" port="43383" tid="140149569400832" timestamp=1726929
9月 21 22:38:59 gpu ollama[54696]: INFO [main] system info | n_threads=32 n_threads_batch=32 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VB
9月 21 22:38:59 gpu ollama[54696]: INFO [main] build info | build=10 commit="9225b05" tid="140149569400832" timestamp=1726929539
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.537+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm serve
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.536+08:00 level=INFO source=server.go:587 msg="waiting for llama runner to start responding"
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.536+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.534+08:00 level=INFO source=server.go:388 msg="starting llama server" cmd="/tmp/ollama2642747161/runners
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.516+08:00 level=INFO source=memory.go:326 msg="offload to cuda" layers.requested=-1 layers.model=47 laye
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.514+08:00 level=INFO source=server.go:103 msg="system memory" total="125.4 GiB" free="111.5 GiB" free_sw
9月 21 22:38:59 gpu ollama[54696]: time=2024-09-21T22:38:59.514+08:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loadin
9月 21 22:38:51 gpu ollama[54696]: time=2024-09-21T22:38:51.422+08:00 level=INFO source=types.go:107 msg="inference compute" id=GPU-1a5993d8-1f60-3ecd-b80f-55ca9f1e
9月 21 22:38:51 gpu ollama[54696]: time=2024-09-21T22:38:51.422+08:00 level=INFO source=types.go:107 msg="inference compute" id=GPU-ac079011-c45b-de29-f2e2-71b2e5d2
9月 21 22:38:51 gpu ollama[54696]: time=2024-09-21T22:38:51.422+08:00 level=INFO source=types.go:107 msg="inference compute" id=GPU-6b83f2f6-dc65-7feb-5e02-0cd00879
9月 21 22:38:51 gpu ollama[54696]: time=2024-09-21T22:38:51.421+08:00 level=INFO source=types.go:107 msg="inference compute" id=GPU-ad4cba93-ee35-2ea2-dba7-7b5772a0
9月 21 22:38:50 gpu ollama[54696]: time=2024-09-21T22:38:50.097+08:00 level=INFO source=gpu.go:199 msg="looking for compatible GPUs"
9月 21 22:38:50 gpu ollama[54696]: time=2024-09-21T22:38:50.097+08:00 level=INFO source=common.go:49 msg="Dynamic LLM libraries" runners="[cpu cpu_avx cpu_avx2 cuda
9月 21 22:38:34 gpu ollama[54696]: time=2024-09-21T22:38:34.553+08:00 level=INFO source=common.go:135 msg="extracting embedded files" dir=/tmp/ollama2642747161/runn
9月 21 22:38:34 gpu ollama[54696]: time=2024-09-21T22:38:34.552+08:00 level=INFO source=routes.go:1200 msg="Listening on [::]:11434 (version 0.3.11)"
9月 21 22:38:34 gpu ollama[54696]: time=2024-09-21T22:38:34.551+08:00 level=INFO source=images.go:760 msg="total unused blobs removed: 0"
9月 21 22:38:34 gpu ollama[54696]: time=2024-09-21T22:38:34.549+08:00 level=INFO source=images.go:753 msg="total blobs: 44"
9月 21 22:38:34 gpu ollama[54696]: 2024/09/21 22:38:34 routes.go:1153: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HS
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.3
|
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| 2,013,342,661
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PR_kwDOJ0Z1Ps5gfvnh
| 1,294
|
Allow setting parameters in the REPL
|
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[] | null | 0
| 2023-11-28T00:02:40
| 2023-11-29T17:56:43
| 2023-11-29T17:56:42
|
CONTRIBUTOR
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This change adds a new `/set parameter` command inside the repl so that you can change parameters without having to recreate a modelfile.
I have changed the `/show parameters` command to also reflect any parameters that have been set, however I haven't yet changed `/show modelfile` which should spit out a new modelfile which reflects the changes. That can come in a followup PR. Also not included in this PR are `/set template` and `/set system` which will come in a different PR.
|
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I_kwDOJ0Z1Ps6BxroT
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Ollama not using GPU, falling back to CPU
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| 2024-04-29T22:43:52
| 2024-04-12T22:18:53
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NONE
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I'm running Ollama via a docker container on Debian. For a llama2 model, my CPU utilization is at 100% while GPU remains at 0%.
Here is my output from `docker logs ollama`:
```
time=2024-03-09T14:52:42.622Z level=INFO source=images.go:800 msg="total blobs: 0"
time=2024-03-09T14:52:42.623Z level=INFO source=images.go:807 msg="total unused blobs removed: 0"
time=2024-03-09T14:52:42.623Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.28)"
time=2024-03-09T14:52:42.623Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-03-09T14:52:46.425Z level=INFO source=payload_common.go:150 msg="Dynamic LLM libraries [cpu_avx rocm_v60000 cpu_avx2 cuda_v11 cpu]"
time=2024-03-09T14:52:46.425Z level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-03-09T14:52:46.425Z level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-03-09T14:52:46.426Z level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14]"
time=2024-03-09T14:52:46.434Z level=INFO source=gpu.go:249 msg="Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14: nvml vram init failure: 999"
time=2024-03-09T14:52:46.434Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T14:52:46.434Z level=INFO source=routes.go:1042 msg="no GPU detected"
time=2024-03-09T15:12:39.692Z level=INFO source=images.go:800 msg="total blobs: 6"
time=2024-03-09T15:12:39.694Z level=INFO source=images.go:807 msg="total unused blobs removed: 6"
time=2024-03-09T15:12:39.695Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.28)"
time=2024-03-09T15:12:39.695Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-03-09T15:12:43.522Z level=INFO source=payload_common.go:150 msg="Dynamic LLM libraries [rocm_v60000 cpu cpu_avx cpu_avx2 cuda_v11]"
time=2024-03-09T15:12:43.523Z level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-03-09T15:12:43.523Z level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-03-09T15:12:43.525Z level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14]"
time=2024-03-09T15:12:43.535Z level=INFO source=gpu.go:249 msg="Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14: nvml vram init failure: 999"
time=2024-03-09T15:12:43.535Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:12:43.535Z level=INFO source=routes.go:1042 msg="no GPU detected"
time=2024-03-09T15:25:32.983Z level=INFO source=images.go:800 msg="total blobs: 0"
time=2024-03-09T15:25:32.984Z level=INFO source=images.go:807 msg="total unused blobs removed: 0"
time=2024-03-09T15:25:32.984Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.28)"
time=2024-03-09T15:25:32.985Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-03-09T15:25:36.686Z level=INFO source=payload_common.go:150 msg="Dynamic LLM libraries [cpu cpu_avx rocm_v60000 cpu_avx2 cuda_v11]"
time=2024-03-09T15:25:36.686Z level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-03-09T15:25:36.686Z level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-03-09T15:25:36.688Z level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14]"
time=2024-03-09T15:25:36.698Z level=INFO source=gpu.go:249 msg="Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14: nvml vram init failure: 999"
time=2024-03-09T15:25:36.698Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:25:36.698Z level=INFO source=routes.go:1042 msg="no GPU detected"
time=2024-03-09T15:28:43.196Z level=INFO source=images.go:800 msg="total blobs: 0"
time=2024-03-09T15:28:43.198Z level=INFO source=images.go:807 msg="total unused blobs removed: 0"
time=2024-03-09T15:28:43.198Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.28)"
time=2024-03-09T15:28:43.199Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-03-09T15:28:46.997Z level=INFO source=payload_common.go:150 msg="Dynamic LLM libraries [cpu cpu_avx rocm_v60000 cuda_v11 cpu_avx2]"
time=2024-03-09T15:28:46.997Z level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-03-09T15:28:46.998Z level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-03-09T15:28:46.999Z level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14]"
time=2024-03-09T15:28:47.010Z level=INFO source=gpu.go:249 msg="Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14: nvml vram init failure: 999"
time=2024-03-09T15:28:47.010Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:28:47.010Z level=INFO source=routes.go:1042 msg="no GPU detected"
time=2024-03-09T15:33:09.444Z level=INFO source=images.go:800 msg="total blobs: 0"
time=2024-03-09T15:33:09.444Z level=INFO source=images.go:807 msg="total unused blobs removed: 0"
time=2024-03-09T15:33:09.445Z level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.28)"
time=2024-03-09T15:33:09.445Z level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..."
time=2024-03-09T15:33:13.264Z level=INFO source=payload_common.go:150 msg="Dynamic LLM libraries [cuda_v11 cpu_avx cpu rocm_v60000 cpu_avx2]"
time=2024-03-09T15:33:13.264Z level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-03-09T15:33:13.264Z level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-03-09T15:33:13.278Z level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14]"
time=2024-03-09T15:33:13.287Z level=INFO source=gpu.go:249 msg="Unable to load CUDA management library /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.550.54.14: nvml vram init failure: 999"
time=2024-03-09T15:33:13.287Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:33:13.287Z level=INFO source=routes.go:1042 msg="no GPU detected"
...
...
time=2024-03-09T15:36:53.196Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:36:53.196Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-09T15:36:53.196Z level=INFO source=llm.go:77 msg="GPU not available, falling back to CPU"
loading library /root/.ollama/assets/0.1.28/cpu_avx2/libext_server.so
time=2024-03-09T15:36:53.200Z level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: /root/.ollama/assets/0.1.28/cpu_avx2/libext_server.so"
time=2024-03-09T15:36:53.200Z level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
...
...
llama_kv_cache_init: CPU 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: CPU input buffer size = 13.02 MiB
llama_new_context_with_model: CPU compute buffer size = 160.00 MiB
```
I can confirm that I have NVIDIA drivers installed, and also the latest version of nvidia-container-toolkit
```
root@docker-debian:/root/docker# nvidia-ctk --version
NVIDIA Container Toolkit CLI version 1.14.6
```
`nvidia-smi` output:
```
root@docker-debian:/root/docker# sudo docker run --rm --runtime=nvidia --gpus all \
--device /dev/nvidia0:/dev/nvidia0 \
--device /dev/nvidia1:/dev/nvidia1 \
--device /dev/nvidiactl \
--device /dev/nvidia-modeset \
--device /dev/nvidia-uvm \
debian nvidia-smi
Sat Mar 9 15:53:14 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 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 Tesla M40 24GB Off | 00000000:02:00.0 Off | Off |
| N/A 38C P8 16W / 250W | 0MiB / 24576MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce GTX 1660 ... Off | 00000000:03:00.0 Off | N/A |
| 51% 42C P8 12W / 125W | 0MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
I'm very new to this and learning! Hope some one can point me in the right direction
|
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| 1,815,125,416
|
I_kwDOJ0Z1Ps5sMJ2o
| 155
|
Where are the models pulled to?
|
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| 2023-07-21T04:15:37
| 2024-07-27T10:25:17
| 2023-08-23T17:47:41
|
NONE
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It downloaded 7 gigs of stuff and i can't seem to find where it went. I want to download it. Any ideas?
|
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PR_kwDOJ0Z1Ps52QcxE
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OpenAI: Add Usage to `v1/embeddings`
|
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PR_kwDOJ0Z1Ps5ihEHz
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|
Use cuda base image for final docker image
|
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This is necessary so cuda works at all.
|
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PR_kwDOJ0Z1Ps5igboL
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|
Add Cache option #1573
|
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| 2023-12-22T22:16:20
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CONTRIBUTOR
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This PR, adds the API option "cache", that allows the llama.cpp server to cache our prompt Eval and the response.
This speed-up follow-up calls immensely for some models, if you use it over the API, with the same prompt (or even partial ones), it will speed up subsequent calls, since it skips the evaluation of the prompt.
Also, this PR includes commands /set cache and /set nocache to give users the ability to enable prompt caching in the official CLI.
* Add a new entry "cache" to the options object that is passed to the worker
* Add commands /set cache and /set nocache to allow this in the repl cli
* Update docs
This is a partial fix for, Enable prompt cache #1573, we might need to patch llama.cpp at some point to allow us full flexibility.
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I_kwDOJ0Z1Ps58sJ9u
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|
Crash upon loading any model with the ROCm GPU
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Stacktrace:
```
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
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: n_ctx_train = 4096
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
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: n_ff = 11008
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 13B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 8.36 B
llm_load_print_meta: model size = 4.41 GiB (4.53 BPW)
llm_load_print_meta: general.name = LLaMA v2
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_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: using ROCm for GPU acceleration
llm_load_tensors: system memory used = 70.45 MiB
llm_load_tensors: VRAM used = 4446.30 MiB
llm_load_tensors: offloading 40 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 41/41 layers to GPU
...................................................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: VRAM kv self = 1280.00 MB
llama_new_context_with_model: KV self size = 1280.00 MiB, K (f16): 640.00 MiB, V (f16): 640.00 MiB
llama_build_graph: non-view tensors processed: 844/844
llama_new_context_with_model: compute buffer total size = 159.19 MiB
llama_new_context_with_model: VRAM scratch buffer: 156.00 MiB
llama_new_context_with_model: total VRAM used: 5882.31 MiB (model: 4446.30 MiB, context: 1436.00 MiB)
SIGSEGV: segmentation violation
PC=0x780302b2b380 m=18 sigcode=128
signal arrived during cgo execution
goroutine 67 [syscall]:
runtime.cgocall(0x9b3a90, 0xc000318808)
/usr/lib/go/src/runtime/cgocall.go:157 +0x4b fp=0xc0003187e0 sp=0xc0003187a8 pc=0x409b0b
github.com/jmorganca/ollama/llm._Cfunc_dyn_llama_server_init({0x78029c001620, 0x780309434970, 0x7803094350c0, 0x780309435150, 0x780309435300, 0x780309435480, 0x7803094359b0, 0x780309435990, 0x780309435a40, 0x780309435f20, ...}, ...)
_cgo_gotypes.go:284 +0x45 fp=0xc000318808 sp=0xc0003187e0 pc=0x7c25a5
github.com/jmorganca/ollama/llm.newDynExtServer.func7(0xae3c43?, 0x6c?)
/home/kainoa/Git/ollama-clean/llm/dyn_ext_server.go:142 +0xef fp=0xc0003188f8 sp=0xc000318808 pc=0x7c3a0f
github.com/jmorganca/ollama/llm.newDynExtServer({0xc000618000, 0x2e}, {0xc0001c48c0, _}, {_, _, _}, {0x0, 0x0, 0x0}, ...)
/home/kainoa/Git/ollama-clean/llm/dyn_ext_server.go:142 +0xa32 fp=0xc000318b88 sp=0xc0003188f8 pc=0x7c3752
github.com/jmorganca/ollama/llm.newLlmServer({{_, _, _}, {_, _}, {_, _}}, {_, _}, {0x0, ...}, ...)
/home/kainoa/Git/ollama-clean/llm/llm.go:147 +0x36a fp=0xc000318d48 sp=0xc000318b88 pc=0x7bff6a
github.com/jmorganca/ollama/llm.New({0x0?, 0x1000100000100?}, {0xc0001c48c0, _}, {_, _, _}, {0x0, 0x0, 0x0}, ...)
/home/kainoa/Git/ollama-clean/llm/llm.go:122 +0x6f9 fp=0xc000318fb8 sp=0xc000318d48 pc=0x7bf999
github.com/jmorganca/ollama/server.load(0xc000002f00?, 0xc000002f00, {{0x0, 0x800, 0x200, 0x1, 0xffffffffffffffff, 0x0, 0x0, 0x1, ...}, ...}, ...)
/home/kainoa/Git/ollama-clean/server/routes.go:83 +0x3a5 fp=0xc000319138 sp=0xc000318fb8 pc=0x98fde5
github.com/jmorganca/ollama/server.ChatHandler(0xc0002fc100)
/home/kainoa/Git/ollama-clean/server/routes.go:1071 +0x828 fp=0xc000319748 sp=0xc000319138 pc=0x99a728
github.com/gin-gonic/gin.(*Context).Next(...)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/jmorganca/ollama/server.(*Server).GenerateRoutes.func1(0xc0002fc100)
/home/kainoa/Git/ollama-clean/server/routes.go:883 +0x68 fp=0xc000319780 sp=0xc000319748 pc=0x999268
github.com/gin-gonic/gin.(*Context).Next(...)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.CustomRecoveryWithWriter.func1(0xc0002fc100)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/recovery.go:102 +0x7a fp=0xc0003197d0 sp=0xc000319780 pc=0x974afa
github.com/gin-gonic/gin.(*Context).Next(...)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.LoggerWithConfig.func1(0xc0002fc100)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/logger.go:240 +0xde fp=0xc000319980 sp=0xc0003197d0 pc=0x973c9e
github.com/gin-gonic/gin.(*Context).Next(...)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.(*Engine).handleHTTPRequest(0xc0000e9a00, 0xc0002fc100)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:620 +0x65b fp=0xc000319b08 sp=0xc000319980 pc=0x972d5b
github.com/gin-gonic/gin.(*Engine).ServeHTTP(0xc0000e9a00, {0x1258e00?, 0xc0001c61c0}, 0xc0002fc500)
/home/kainoa/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:576 +0x1dd fp=0xc000319b48 sp=0xc000319b08 pc=0x97251d
net/http.serverHandler.ServeHTTP({0x1257120?}, {0x1258e00?, 0xc0001c61c0?}, 0x6?)
/usr/lib/go/src/net/http/server.go:2938 +0x8e fp=0xc000319b78 sp=0xc000319b48 pc=0x6ce14e
net/http.(*conn).serve(0xc0001bae10, {0x125a468, 0xc0004a6720})
/usr/lib/go/src/net/http/server.go:2009 +0x5f4 fp=0xc000319fb8 sp=0xc000319b78 pc=0x6ca034
net/http.(*Server).Serve.func3()
/usr/lib/go/src/net/http/server.go:3086 +0x28 fp=0xc000319fe0 sp=0xc000319fb8 pc=0x6ce968
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000319fe8 sp=0xc000319fe0 pc=0x46e081
created by net/http.(*Server).Serve in goroutine 1
/usr/lib/go/src/net/http/server.go:3086 +0x5cb
goroutine 1 [IO wait]:
runtime.gopark(0x480890?, 0xc0003ab848?, 0x98?, 0xb8?, 0x4f687d?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc00011b828 sp=0xc00011b808 pc=0x43e60e
runtime.netpollblock(0x46c0f2?, 0x4092a6?, 0x0?)
/usr/lib/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc00011b860 sp=0xc00011b828 pc=0x4370b7
internal/poll.runtime_pollWait(0x78036acc4e80, 0x72)
/usr/lib/go/src/runtime/netpoll.go:343 +0x85 fp=0xc00011b880 sp=0xc00011b860 pc=0x4688a5
internal/poll.(*pollDesc).wait(0xc000484080?, 0x4?, 0x0)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00011b8a8 sp=0xc00011b880 pc=0x4ef4c7
internal/poll.(*pollDesc).waitRead(...)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Accept(0xc000484080)
/usr/lib/go/src/internal/poll/fd_unix.go:611 +0x2ac fp=0xc00011b950 sp=0xc00011b8a8 pc=0x4f49ac
net.(*netFD).accept(0xc000484080)
/usr/lib/go/src/net/fd_unix.go:172 +0x29 fp=0xc00011ba08 sp=0xc00011b950 pc=0x56b569
net.(*TCPListener).accept(0xc0004595c0)
/usr/lib/go/src/net/tcpsock_posix.go:152 +0x1e fp=0xc00011ba30 sp=0xc00011ba08 pc=0x58039e
net.(*TCPListener).Accept(0xc0004595c0)
/usr/lib/go/src/net/tcpsock.go:315 +0x30 fp=0xc00011ba60 sp=0xc00011ba30 pc=0x57f550
net/http.(*onceCloseListener).Accept(0xc0001bae10?)
<autogenerated>:1 +0x24 fp=0xc00011ba78 sp=0xc00011ba60 pc=0x6f0ee4
net/http.(*Server).Serve(0xc000396ff0, {0x1258bf0, 0xc0004595c0})
/usr/lib/go/src/net/http/server.go:3056 +0x364 fp=0xc00011bba8 sp=0xc00011ba78 pc=0x6ce5a4
github.com/jmorganca/ollama/server.Serve({0x1258bf0, 0xc0004595c0})
/home/kainoa/Git/ollama-clean/server/routes.go:970 +0x494 fp=0xc00011bc98 sp=0xc00011bba8 pc=0x999754
github.com/jmorganca/ollama/cmd.RunServer(0xc000482300?, {0x169c7a0?, 0x4?, 0xacbac1?})
/home/kainoa/Git/ollama-clean/cmd/cmd.go:690 +0x199 fp=0xc00011bd30 sp=0xc00011bc98 pc=0x9abb39
github.com/spf13/cobra.(*Command).execute(0xc000417800, {0x169c7a0, 0x0, 0x0})
/home/kainoa/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:940 +0x87c fp=0xc00011be68 sp=0xc00011bd30 pc=0x763c9c
github.com/spf13/cobra.(*Command).ExecuteC(0xc000416c00)
/home/kainoa/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:1068 +0x3a5 fp=0xc00011bf20 sp=0xc00011be68 pc=0x7644c5
github.com/spf13/cobra.(*Command).Execute(...)
/home/kainoa/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:992
github.com/spf13/cobra.(*Command).ExecuteContext(...)
/home/kainoa/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:985
main.main()
/home/kainoa/Git/ollama-clean/main.go:11 +0x4d fp=0xc00011bf40 sp=0xc00011bf20 pc=0x9b2bad
runtime.main()
/usr/lib/go/src/runtime/proc.go:267 +0x2bb fp=0xc00011bfe0 sp=0xc00011bf40 pc=0x43e1bb
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00011bfe8 sp=0xc00011bfe0 pc=0x46e081
goroutine 2 [force gc (idle)]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000070fa8 sp=0xc000070f88 pc=0x43e60e
runtime.goparkunlock(...)
/usr/lib/go/src/runtime/proc.go:404
runtime.forcegchelper()
/usr/lib/go/src/runtime/proc.go:322 +0xb3 fp=0xc000070fe0 sp=0xc000070fa8 pc=0x43e493
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000070fe8 sp=0xc000070fe0 pc=0x46e081
created by runtime.init.6 in goroutine 1
/usr/lib/go/src/runtime/proc.go:310 +0x1a
goroutine 3 [GC sweep wait]:
runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000071778 sp=0xc000071758 pc=0x43e60e
runtime.goparkunlock(...)
/usr/lib/go/src/runtime/proc.go:404
runtime.bgsweep(0x0?)
/usr/lib/go/src/runtime/mgcsweep.go:321 +0xdf fp=0xc0000717c8 sp=0xc000071778 pc=0x42a57f
runtime.gcenable.func1()
/usr/lib/go/src/runtime/mgc.go:200 +0x25 fp=0xc0000717e0 sp=0xc0000717c8 pc=0x41f6c5
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000717e8 sp=0xc0000717e0 pc=0x46e081
created by runtime.gcenable in goroutine 1
/usr/lib/go/src/runtime/mgc.go:200 +0x66
goroutine 4 [GC scavenge wait]:
runtime.gopark(0x104a1f?, 0xede89?, 0x0?, 0x0?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000071f70 sp=0xc000071f50 pc=0x43e60e
runtime.goparkunlock(...)
/usr/lib/go/src/runtime/proc.go:404
runtime.(*scavengerState).park(0x166cb20)
/usr/lib/go/src/runtime/mgcscavenge.go:425 +0x49 fp=0xc000071fa0 sp=0xc000071f70 pc=0x427de9
runtime.bgscavenge(0x0?)
/usr/lib/go/src/runtime/mgcscavenge.go:658 +0x59 fp=0xc000071fc8 sp=0xc000071fa0 pc=0x428399
runtime.gcenable.func2()
/usr/lib/go/src/runtime/mgc.go:201 +0x25 fp=0xc000071fe0 sp=0xc000071fc8 pc=0x41f665
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000071fe8 sp=0xc000071fe0 pc=0x46e081
created by runtime.gcenable in goroutine 1
/usr/lib/go/src/runtime/mgc.go:201 +0xa5
goroutine 5 [finalizer wait]:
runtime.gopark(0x198?, 0xac4a80?, 0x1?, 0xf7?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000070620 sp=0xc000070600 pc=0x43e60e
runtime.runfinq()
/usr/lib/go/src/runtime/mfinal.go:193 +0x107 fp=0xc0000707e0 sp=0xc000070620 pc=0x41e6e7
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000707e8 sp=0xc0000707e0 pc=0x46e081
created by runtime.createfing in goroutine 1
/usr/lib/go/src/runtime/mfinal.go:163 +0x3d
goroutine 6 [select, locked to thread]:
runtime.gopark(0xc0000727a8?, 0x2?, 0xa9?, 0xe8?, 0xc0000727a4?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000072638 sp=0xc000072618 pc=0x43e60e
runtime.selectgo(0xc0000727a8, 0xc0000727a0, 0x0?, 0x0, 0x0?, 0x1)
/usr/lib/go/src/runtime/select.go:327 +0x725 fp=0xc000072758 sp=0xc000072638 pc=0x44e165
runtime.ensureSigM.func1()
/usr/lib/go/src/runtime/signal_unix.go:1014 +0x19f fp=0xc0000727e0 sp=0xc000072758 pc=0x46519f
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000727e8 sp=0xc0000727e0 pc=0x46e081
created by runtime.ensureSigM in goroutine 1
/usr/lib/go/src/runtime/signal_unix.go:997 +0xc8
goroutine 18 [syscall]:
runtime.notetsleepg(0x0?, 0x0?)
/usr/lib/go/src/runtime/lock_futex.go:236 +0x29 fp=0xc00006c7a0 sp=0xc00006c768 pc=0x411209
os/signal.signal_recv()
/usr/lib/go/src/runtime/sigqueue.go:152 +0x29 fp=0xc00006c7c0 sp=0xc00006c7a0 pc=0x46aa49
os/signal.loop()
/usr/lib/go/src/os/signal/signal_unix.go:23 +0x13 fp=0xc00006c7e0 sp=0xc00006c7c0 pc=0x6f3913
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00006c7e8 sp=0xc00006c7e0 pc=0x46e081
created by os/signal.Notify.func1.1 in goroutine 1
/usr/lib/go/src/os/signal/signal.go:151 +0x1f
goroutine 7 [chan receive]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000072f18 sp=0xc000072ef8 pc=0x43e60e
runtime.chanrecv(0xc0004ac540, 0x0, 0x1)
/usr/lib/go/src/runtime/chan.go:583 +0x3cd fp=0xc000072f90 sp=0xc000072f18 pc=0x40beed
runtime.chanrecv1(0x0?, 0x0?)
/usr/lib/go/src/runtime/chan.go:442 +0x12 fp=0xc000072fb8 sp=0xc000072f90 pc=0x40baf2
github.com/jmorganca/ollama/server.Serve.func1()
/home/kainoa/Git/ollama-clean/server/routes.go:952 +0x25 fp=0xc000072fe0 sp=0xc000072fb8 pc=0x9997e5
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000072fe8 sp=0xc000072fe0 pc=0x46e081
created by github.com/jmorganca/ollama/server.Serve in goroutine 1
/home/kainoa/Git/ollama-clean/server/routes.go:951 +0x407
goroutine 62 [IO wait]:
runtime.gopark(0x75?, 0xb?, 0x0?, 0x0?, 0xa?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc00011f8f8 sp=0xc00011f8d8 pc=0x43e60e
runtime.netpollblock(0x47e9f8?, 0x4092a6?, 0x0?)
/usr/lib/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc00011f930 sp=0xc00011f8f8 pc=0x4370b7
internal/poll.runtime_pollWait(0x78036acc4d88, 0x72)
/usr/lib/go/src/runtime/netpoll.go:343 +0x85 fp=0xc00011f950 sp=0xc00011f930 pc=0x4688a5
internal/poll.(*pollDesc).wait(0xc000040080?, 0xc000428000?, 0x0)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00011f978 sp=0xc00011f950 pc=0x4ef4c7
internal/poll.(*pollDesc).waitRead(...)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc000040080, {0xc000428000, 0x1000, 0x1000})
/usr/lib/go/src/internal/poll/fd_unix.go:164 +0x27a fp=0xc00011fa10 sp=0xc00011f978 pc=0x4f07ba
net.(*netFD).Read(0xc000040080, {0xc000428000?, 0x4ef985?, 0x0?})
/usr/lib/go/src/net/fd_posix.go:55 +0x25 fp=0xc00011fa58 sp=0xc00011fa10 pc=0x569545
net.(*conn).Read(0xc000074038, {0xc000428000?, 0x0?, 0xc0000b0518?})
/usr/lib/go/src/net/net.go:179 +0x45 fp=0xc00011faa0 sp=0xc00011fa58 pc=0x577805
net.(*TCPConn).Read(0xc0000b0510?, {0xc000428000?, 0x0?, 0xc00011fac0?})
<autogenerated>:1 +0x25 fp=0xc00011fad0 sp=0xc00011faa0 pc=0x589705
net/http.(*connReader).Read(0xc0000b0510, {0xc000428000, 0x1000, 0x1000})
/usr/lib/go/src/net/http/server.go:791 +0x14b fp=0xc00011fb20 sp=0xc00011fad0 pc=0x6c42eb
bufio.(*Reader).fill(0xc0004ac000)
/usr/lib/go/src/bufio/bufio.go:113 +0x103 fp=0xc00011fb58 sp=0xc00011fb20 pc=0x653ea3
bufio.(*Reader).Peek(0xc0004ac000, 0x4)
/usr/lib/go/src/bufio/bufio.go:151 +0x53 fp=0xc00011fb78 sp=0xc00011fb58 pc=0x653fd3
net/http.(*conn).serve(0xc0000fc240, {0x125a468, 0xc0004a6720})
/usr/lib/go/src/net/http/server.go:2044 +0x75c fp=0xc00011ffb8 sp=0xc00011fb78 pc=0x6ca19c
net/http.(*Server).Serve.func3()
/usr/lib/go/src/net/http/server.go:3086 +0x28 fp=0xc00011ffe0 sp=0xc00011ffb8 pc=0x6ce968
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00011ffe8 sp=0xc00011ffe0 pc=0x46e081
created by net/http.(*Server).Serve in goroutine 1
/usr/lib/go/src/net/http/server.go:3086 +0x5cb
goroutine 12 [GC worker (idle)]:
runtime.gopark(0x0?, 0x0?, 0xe0?, 0x2e?, 0xc0004c2fd0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc0004c2f50 sp=0xc0004c2f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0004c2fe0 sp=0xc0004c2f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0004c2fe8 sp=0xc0004c2fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 34 [GC worker (idle)]:
runtime.gopark(0xa09ea49875?, 0x3?, 0x84?, 0x3?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc0004be750 sp=0xc0004be730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0004be7e0 sp=0xc0004be750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0004be7e8 sp=0xc0004be7e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 13 [GC worker (idle)]:
runtime.gopark(0xa09ea48fd3?, 0x1?, 0x72?, 0x10?, 0xc0000737d0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000073750 sp=0xc000073730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0000737e0 sp=0xc000073750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0000737e8 sp=0xc0000737e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 14 [GC worker (idle)]:
runtime.gopark(0xa09ea45121?, 0x3?, 0x96?, 0x5?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc0004c3750 sp=0xc0004c3730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0004c37e0 sp=0xc0004c3750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0004c37e8 sp=0xc0004c37e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 50 [GC worker (idle)]:
runtime.gopark(0xa09ea49267?, 0x1?, 0x4f?, 0xb6?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000586750 sp=0xc000586730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005867e0 sp=0xc000586750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005867e8 sp=0xc0005867e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 51 [GC worker (idle)]:
runtime.gopark(0xa09ea44f4b?, 0x1?, 0xc3?, 0xc5?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000586f50 sp=0xc000586f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000586fe0 sp=0xc000586f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000586fe8 sp=0xc000586fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 52 [GC worker (idle)]:
runtime.gopark(0xa09ea48ec5?, 0x1?, 0x40?, 0x34?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000587750 sp=0xc000587730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005877e0 sp=0xc000587750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005877e8 sp=0xc0005877e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 53 [GC worker (idle)]:
runtime.gopark(0xa09ea490ff?, 0x1?, 0x9e?, 0x11?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000587f50 sp=0xc000587f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000587fe0 sp=0xc000587f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000587fe8 sp=0xc000587fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 54 [GC worker (idle)]:
runtime.gopark(0xa09ea46909?, 0x1?, 0xb7?, 0x51?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000588750 sp=0xc000588730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005887e0 sp=0xc000588750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005887e8 sp=0xc0005887e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 55 [GC worker (idle)]:
runtime.gopark(0xa09ea450d1?, 0x3?, 0x57?, 0x4f?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000588f50 sp=0xc000588f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000588fe0 sp=0xc000588f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000588fe8 sp=0xc000588fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 56 [GC worker (idle)]:
runtime.gopark(0xa09ea45009?, 0x3?, 0x6a?, 0x4?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000589750 sp=0xc000589730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005897e0 sp=0xc000589750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005897e8 sp=0xc0005897e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 57 [GC worker (idle)]:
runtime.gopark(0xa09ea49177?, 0x3?, 0x6?, 0x1d?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000589f50 sp=0xc000589f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000589fe0 sp=0xc000589f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000589fe8 sp=0xc000589fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 58 [GC worker (idle)]:
runtime.gopark(0x169e4e0?, 0x1?, 0xaa?, 0x2d?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000582750 sp=0xc000582730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005827e0 sp=0xc000582750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005827e8 sp=0xc0005827e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 59 [GC worker (idle)]:
runtime.gopark(0xa09ea49159?, 0x3?, 0xc4?, 0x13?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000582f50 sp=0xc000582f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000582fe0 sp=0xc000582f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000582fe8 sp=0xc000582fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 60 [GC worker (idle)]:
runtime.gopark(0xa09ea43c3b?, 0x3?, 0xf5?, 0xc4?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000583750 sp=0xc000583730 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc0005837e0 sp=0xc000583750 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc0005837e8 sp=0xc0005837e0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 61 [GC worker (idle)]:
runtime.gopark(0xa09ea46279?, 0xc00058a160?, 0x1a?, 0x14?, 0x0?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc000583f50 sp=0xc000583f30 pc=0x43e60e
runtime.gcBgMarkWorker()
/usr/lib/go/src/runtime/mgc.go:1295 +0xe5 fp=0xc000583fe0 sp=0xc000583f50 pc=0x421245
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000583fe8 sp=0xc000583fe0 pc=0x46e081
created by runtime.gcBgMarkStartWorkers in goroutine 11
/usr/lib/go/src/runtime/mgc.go:1219 +0x1c
goroutine 16 [IO wait]:
runtime.gopark(0x41e?, 0xb?, 0x0?, 0x0?, 0xc?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc0005918f8 sp=0xc0005918d8 pc=0x43e60e
runtime.netpollblock(0x47e9f8?, 0x4092a6?, 0x0?)
/usr/lib/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc000591930 sp=0xc0005918f8 pc=0x4370b7
internal/poll.runtime_pollWait(0x78036acc4b98, 0x72)
/usr/lib/go/src/runtime/netpoll.go:343 +0x85 fp=0xc000591950 sp=0xc000591930 pc=0x4688a5
internal/poll.(*pollDesc).wait(0xc000436080?, 0xc000312000?, 0x0)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc000591978 sp=0xc000591950 pc=0x4ef4c7
internal/poll.(*pollDesc).waitRead(...)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc000436080, {0xc000312000, 0x1000, 0x1000})
/usr/lib/go/src/internal/poll/fd_unix.go:164 +0x27a fp=0xc000591a10 sp=0xc000591978 pc=0x4f07ba
net.(*netFD).Read(0xc000436080, {0xc000312000?, 0x4ef985?, 0x0?})
/usr/lib/go/src/net/fd_posix.go:55 +0x25 fp=0xc000591a58 sp=0xc000591a10 pc=0x569545
net.(*conn).Read(0xc00025c148, {0xc000312000?, 0x0?, 0xc000395aa8?})
/usr/lib/go/src/net/net.go:179 +0x45 fp=0xc000591aa0 sp=0xc000591a58 pc=0x577805
net.(*TCPConn).Read(0xc000395aa0?, {0xc000312000?, 0x0?, 0xc00031dac0?})
<autogenerated>:1 +0x25 fp=0xc000591ad0 sp=0xc000591aa0 pc=0x589705
net/http.(*connReader).Read(0xc000395aa0, {0xc000312000, 0x1000, 0x1000})
/usr/lib/go/src/net/http/server.go:791 +0x14b fp=0xc000591b20 sp=0xc000591ad0 pc=0x6c42eb
bufio.(*Reader).fill(0xc0001a73e0)
/usr/lib/go/src/bufio/bufio.go:113 +0x103 fp=0xc000591b58 sp=0xc000591b20 pc=0x653ea3
bufio.(*Reader).Peek(0xc0001a73e0, 0x4)
/usr/lib/go/src/bufio/bufio.go:151 +0x53 fp=0xc000591b78 sp=0xc000591b58 pc=0x653fd3
net/http.(*conn).serve(0xc0001ba990, {0x125a468, 0xc0004a6720})
/usr/lib/go/src/net/http/server.go:2044 +0x75c fp=0xc000591fb8 sp=0xc000591b78 pc=0x6ca19c
net/http.(*Server).Serve.func3()
/usr/lib/go/src/net/http/server.go:3086 +0x28 fp=0xc000591fe0 sp=0xc000591fb8 pc=0x6ce968
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc000591fe8 sp=0xc000591fe0 pc=0x46e081
created by net/http.(*Server).Serve in goroutine 1
/usr/lib/go/src/net/http/server.go:3086 +0x5cb
goroutine 64 [IO wait]:
runtime.gopark(0x41e?, 0xb?, 0x0?, 0x0?, 0xb?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc00058d8f8 sp=0xc00058d8d8 pc=0x43e60e
runtime.netpollblock(0x47e9f8?, 0x4092a6?, 0x0?)
/usr/lib/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc00058d930 sp=0xc00058d8f8 pc=0x4370b7
internal/poll.runtime_pollWait(0x78036acc4c90, 0x72)
/usr/lib/go/src/runtime/netpoll.go:343 +0x85 fp=0xc00058d950 sp=0xc00058d930 pc=0x4688a5
internal/poll.(*pollDesc).wait(0xc000040200?, 0xc0002fa000?, 0x0)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00058d978 sp=0xc00058d950 pc=0x4ef4c7
internal/poll.(*pollDesc).waitRead(...)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc000040200, {0xc0002fa000, 0x1000, 0x1000})
/usr/lib/go/src/internal/poll/fd_unix.go:164 +0x27a fp=0xc00058da10 sp=0xc00058d978 pc=0x4f07ba
net.(*netFD).Read(0xc000040200, {0xc0002fa000?, 0x4ef985?, 0x0?})
/usr/lib/go/src/net/fd_posix.go:55 +0x25 fp=0xc00058da58 sp=0xc00058da10 pc=0x569545
net.(*conn).Read(0xc000074040, {0xc0002fa000?, 0x0?, 0xc0001d8218?})
/usr/lib/go/src/net/net.go:179 +0x45 fp=0xc00058daa0 sp=0xc00058da58 pc=0x577805
net.(*TCPConn).Read(0xc0001d8210?, {0xc0002fa000?, 0x0?, 0xc0003a7ac0?})
<autogenerated>:1 +0x25 fp=0xc00058dad0 sp=0xc00058daa0 pc=0x589705
net/http.(*connReader).Read(0xc0001d8210, {0xc0002fa000, 0x1000, 0x1000})
/usr/lib/go/src/net/http/server.go:791 +0x14b fp=0xc00058db20 sp=0xc00058dad0 pc=0x6c42eb
bufio.(*Reader).fill(0xc00009a180)
/usr/lib/go/src/bufio/bufio.go:113 +0x103 fp=0xc00058db58 sp=0xc00058db20 pc=0x653ea3
bufio.(*Reader).Peek(0xc00009a180, 0x4)
/usr/lib/go/src/bufio/bufio.go:151 +0x53 fp=0xc00058db78 sp=0xc00058db58 pc=0x653fd3
net/http.(*conn).serve(0xc0000fc3f0, {0x125a468, 0xc0004a6720})
/usr/lib/go/src/net/http/server.go:2044 +0x75c fp=0xc00058dfb8 sp=0xc00058db78 pc=0x6ca19c
net/http.(*Server).Serve.func3()
/usr/lib/go/src/net/http/server.go:3086 +0x28 fp=0xc00058dfe0 sp=0xc00058dfb8 pc=0x6ce968
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00058dfe8 sp=0xc00058dfe0 pc=0x46e081
created by net/http.(*Server).Serve in goroutine 1
/usr/lib/go/src/net/http/server.go:3086 +0x5cb
goroutine 68 [IO wait]:
runtime.gopark(0x100000000?, 0xb?, 0x0?, 0x0?, 0xd?)
/usr/lib/go/src/runtime/proc.go:398 +0xce fp=0xc00006e5a0 sp=0xc00006e580 pc=0x43e60e
runtime.netpollblock(0x47e9f8?, 0x4092a6?, 0x0?)
/usr/lib/go/src/runtime/netpoll.go:564 +0xf7 fp=0xc00006e5d8 sp=0xc00006e5a0 pc=0x4370b7
internal/poll.runtime_pollWait(0x78036acc4aa0, 0x72)
/usr/lib/go/src/runtime/netpoll.go:343 +0x85 fp=0xc00006e5f8 sp=0xc00006e5d8 pc=0x4688a5
internal/poll.(*pollDesc).wait(0xc000436180?, 0xc000438551?, 0x0)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00006e620 sp=0xc00006e5f8 pc=0x4ef4c7
internal/poll.(*pollDesc).waitRead(...)
/usr/lib/go/src/internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc000436180, {0xc000438551, 0x1, 0x1})
/usr/lib/go/src/internal/poll/fd_unix.go:164 +0x27a fp=0xc00006e6b8 sp=0xc00006e620 pc=0x4f07ba
net.(*netFD).Read(0xc000436180, {0xc000438551?, 0xc00006e740?, 0x46a750?})
/usr/lib/go/src/net/fd_posix.go:55 +0x25 fp=0xc00006e700 sp=0xc00006e6b8 pc=0x569545
net.(*conn).Read(0xc00025c1f0, {0xc000438551?, 0x1?, 0xc0002ea730?})
/usr/lib/go/src/net/net.go:179 +0x45 fp=0xc00006e748 sp=0xc00006e700 pc=0x577805
net.(*TCPConn).Read(0xc000395aa0?, {0xc000438551?, 0xc0002ea730?, 0x0?})
<autogenerated>:1 +0x25 fp=0xc00006e778 sp=0xc00006e748 pc=0x589705
net/http.(*connReader).backgroundRead(0xc000438540)
/usr/lib/go/src/net/http/server.go:683 +0x37 fp=0xc00006e7c8 sp=0xc00006e778 pc=0x6c3eb7
net/http.(*connReader).startBackgroundRead.func2()
/usr/lib/go/src/net/http/server.go:679 +0x25 fp=0xc00006e7e0 sp=0xc00006e7c8 pc=0x6c3de5
runtime.goexit()
/usr/lib/go/src/runtime/asm_amd64.s:1650 +0x1 fp=0xc00006e7e8 sp=0xc00006e7e0 pc=0x46e081
created by net/http.(*connReader).startBackgroundRead in goroutine 67
/usr/lib/go/src/net/http/server.go:679 +0xba
rax 0x0
rbx 0x7800341b33c0
rcx 0x7802d8d00200
rdx 0x348
rdi 0x7802d8d00200
rsi 0x78003423a650
rbp 0x780310bfe910
rsp 0x780310bfe6e0
r8 0x90
r9 0x4
r10 0x3
r11 0x78029c9aa400
r12 0x17
r13 0x78029c9aa400
r14 0x78003efd1500
r15 0x78003efd16b8
rip 0x780302b2b380
rflags 0x10246
cs 0x33
fs 0x0
gs 0x0
```
Version: 4c54f0ddeb997cfefe4716e5631b270112975aab (built with ` CLBlast_DIR=/usr/lib/cmake/CLBlast ROCM_PATH=/opt/rocm go generate ./... && go build .`)
|
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https://api.github.com/repos/ollama/ollama/issues/556
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https://github.com/ollama/ollama/pull/556
| 1,905,373,816
|
PR_kwDOJ0Z1Ps5azgGR
| 556
|
pack in cuda libs
|
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[] | null | 0
| 2023-09-20T16:42:58
| 2023-09-20T22:02:38
| 2023-09-20T22:02:37
|
CONTRIBUTOR
|
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|
This change packs CUDA libs into the llama runner and tells the runner to use those libs.
Here is the example generate in my case.
```
go generate ./...
```
|
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I_kwDOJ0Z1Ps6n9Wgc
| 8,653
|
Latest pre-built Ollama binaries (cuda 12.x) do not come with "oob" support for 5.x architecture
|
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NONE
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### "oob" support for 5.x architecture is missing on prebuilt binaries
Hello,
I ended up needing some more power so I threw a spare Quadro M5000 into my AI rig only to find it was not being utilsed at all. I did the usual checks and the card has compute capability 5.2 (confirmed compatible in the support matrix [here](https://github.com/ollama/ollama/blob/main/docs/gpu.md)).
As initial troubleshooting steps (bouncing ideas from other issues posted here) I tried:
- Manually passing through the UUID of all GPU's via `CUDA_VISIBLE_DEVICES` gvar. ollama would acknowledge this in the logs but would never use the card anway. There was no log message complaining about compute capability or mention of dropping the card.
- Setting `OLLAMA_SCHED_SPREAD` gvar to `true`
I found that the ollama install script also grabbed **cuda 11.x by default**, but during the installation the GPU's I had installed were a pair of 20GB RTX4000 "Ada" generation + Aspeed AST2500 IPMI/VGA. I also had the 565 driver installed before setting everything else up, it quotes that its built with cuda 12.6, I don't quite understand why the installer would grab the 11.x toolkit.
During a dive through the repo to see what I could find, I noticed the make config file for cuda_v12 ([here](https://github.com/ollama/ollama/blob/main/make/Makefile.cuda_v12)) does not include 5.0/5.2 support by default but it could. I also found that in the current release notes for cuda 12.8 - Maxwell, Pascal and Volta architectures will be "frozen" (depricated?) in future releases of the cuda toolkit [here](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#deprecated-architectures).
I managed to build everything fine with the cuda 12.8 toolkit and confirmed ollama works as I expected, here are the build flags I used:
`make cuda_v12 CUDA_ARCHITECTURES="50;52;60;61;62;70;72;75;80;86;87;89;90;90a" -j 32`
I know that there is still heaps of Maxwell (5.2) cards still floating around in systems, people on a budget will definitely try use them with the hype of the recent model releases as they are capable of running them locally to an extent. I believe either the docs need an update or binaries should be compiled with support built in until theres an official notice or documentation change to avoid confusion.
Apologies if I am wrong, I thought I would post this here before opening a pull request incase there was anything already in motion related to this.
Cheers,
RK
### OS
Linux - Ubuntu 22.04
### GPU
Nvidia - RTX A4000 "Ada" x2, RTX 4070 & Quadro M5000
### CPU
2x AMD EPYC 7371
### Ollama version
0.5.7 (latest install script)
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PR_kwDOJ0Z1Ps5h0Vos
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retry on concurrent request failure
|
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| 2023-12-12T17:10:54
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|
CONTRIBUTOR
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- remove parallel
- retry concurrent requests on failure
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I_kwDOJ0Z1Ps6Xp9Cz
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Why Is n_ctx in log Always Four Times the num_ctx Value in ModelFIle When Building qwen2.5-coder-7b-instruct-q5_k_m.gguf?
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| 2024-09-24T05:26:34
| 2024-09-24T07:12:01
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### What is the issue?
When I built qwen2.5-coder-7b-instruct-q5_k_m.gguf using the modelfile and set PARAMETER num_ctx 4096, the log output showed llama_new_context_with_model: n_ctx = 16384. After setting num_ctx to different values, I noticed that n_ctx is always 4 times the value of num_ctx. Why is this happening?
The log:
The ModelFile:
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.3.3
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| 2,362,279,633
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I_kwDOJ0Z1Ps6MzYbR
| 5,140
|
Chat template not yet supported for Deepseek-Coder-V2 lite
|
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| 2024-06-19T12:37:36
| 2024-06-19T18:46:11
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### What is the issue?
Whenever i try to chat with the llm through open-webui and ollama, i get this in the logs of ollama:
`ERROR [validate_model_chat_template] The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses | tid="22401915281408" timestamp=1718800491`
and the output in open-webui is just crap, nothing else. The answer has nothing to do with what i have requested.
### OS
Linux, Docker
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.44
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A question on memory
|
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[] | null | 2
| 2023-10-28T18:01:38
| 2023-10-28T20:43:34
| 2023-10-28T20:43:34
|
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Hello this is a question - with regards to the memory spec on running the OSS LLM - see below:
_Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models._
Is the reference to Memory requirement for GPU or the Main Memory (CPU) ? or a combination of GPU Memory and CPU Memory ?
Appreciate a clarity on this.
Thanks in advance.
|
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