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PR_kwDOJ0Z1Ps5x4gqa
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Support for tools requests in ollama API
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Ollama API tools/tool_calls support
The tool support would consist of three addons to the ollama core.
1. Adding "tools" to the chat request and "tool_calls" to the chat response
2. Extending the model template to support `.Tools` and `.Results` variables.
3. To support detection of the model responding with a tool call request.
4. Further work towards OpenAI function calling support
The examples will be based on the Mistral-7b-v0.3 model it now supports tool calls.
# (1) Ollama API extension
The target handler would be the `chat` endpoint.
First the request would be extended with a `"tool"` argument. This argument would be of `string` type as probably not all models will have the OpenAI `json` formatted tool description.
Next, the a new `"tool"` message role should be added to support the tool results to be added to the `"messages"`.
Finally, the chat response would need to have `"tool_calls"` argument to return the detected tool calls coming from the model response. This part will be addressed in the (3) Tool calls detection section of this Pull Request description.
# (2) Model template extension
The prompt must support a way to define the available tools which in the mistral case require to add `[AVAILABLE_TOOLS]...[/AVAILABLE_TOOLS]` section before the `[INST]...[/INST]` section.
For the tool call results to be accepted by the model in the next completion cycle, the results must be enclosed with `[TOOL_RESULTS]...[/TOOL_RESULTS]`.
Update mistral prompt template would look like this:
```
{{ if .Tools }}[AVAILABLE_TOOLS] {{ .Tools }} [/AVAILABLE_TOOLS] {{ end }}[INST] {{ if .System }}{{ .System }} {{ end }}{{ .Prompt }} [/INST]{{ if .Results }}[TOOL_RESULTS] {{ .Results }}[/TOOL_RESULTS]{{ end }} {{ .Response }}
```
For other models, `.Tools` and .`Results` variables would allow to specify different tokens enclosures or sequences to define available tools and to convert messages with "tool" role into the model supported prompt format.
## Hermes 2 Pro Mistral 7B model
For this model the available tools prompt section could look as follows:
```
{{ if .Tools }}<tools>{{.Tools}}</tools>{{ end}}
```
# (3) Tool calls detection
The model template extension is a straight forward one; however the tool call request parsing is the same.
In the case of the mistral model, the tool calls request starts with `[TOOL_CALLS]` token and is followed by a `json array` block. This would be the end of the story if the model would not occasionally hallucinate and add some text after the json array.
To mitigate this pitfall a "dumb" json array parser would be required. This parser would be combined with the start token `[TOOL_CALLS] [...]` to extract only that part of the response.
To keep ollama model independent, at the moment 2 types of tool call parsers could be added:
1. [start_token] content [end_token] parser/extractor
2. [start_token] json parser/extractor
Once the tool_calls have been detected and extracted, the content would be returned in the `"tool_calls"` response argument. As the format of the content will be highly dependent on the model safest would be to return it as a `string`.
The first one seems the most logical, however Mistral must have had their reasons to not include a end token.
The parser/extractor type as well as the start and stop tokens would be defined in the model parameters.
In case of Mistral, the parameters would look like this:
```
tool_calls_start [TOOL_CALLS]
tool_calls_format json
```
In case the model returns both start and end token it would look like this:
```
tool_calls_start [TOOL_CALLS]
tool_calls_end [/TOOL_CALLS]
tool_calls_format json
```
# (4) OpenAI function calling support
With the above addons to the ollama core and API, adding OpenAI function calling API support would mostly be straight forward.
1. Add "tools" argument to the `chat.completation` endpoint and validate the correct format of the tools json value. This value would then be converted from json to a string and passed to Ollama.Chat api
2. Parse the tool_calls and convert it the OpenAI tool calls json format.
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I_kwDOJ0Z1Ps6cU0VH
| 7,413
|
Suno-AI Bark
|
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| 2024-10-30T02:29:28
| 2024-11-04T17:43:56
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I was hoping you guys can please add this model
https://github.com/suno-ai/bark
Bark is a transformer-based text-to-audio model created by [Suno](https://suno.ai/). Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use.
|
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PR_kwDOJ0Z1Ps50w1AR
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feat: Support Moore Threads GPU
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|
CONTRIBUTOR
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[Moore Threads](https://en.mthreads.com/), a cutting-edge GPU startup, introduces MUSA (Moore Threads Unified System Architecture) as its foundational technology. This pull request marks the initial integration of MTGPU support into Ollama, leveraging MUSA's capabilities to enhance LLM inference performance.
I also sent a [PR](https://github.com/ggerganov/llama.cpp/pull/8383) to [llama.cpp](https://github.com/ggerganov/llama.cpp) to integrate MTGPU.
Tested models are:
```bash
NAME ID SIZE MODIFIED
mistral:latest 2ae6f6dd7a3d 4.1 GB 22 hours ago
llama3:8b-instruct-fp16 c666fe422df7 16 GB 30 hours ago
qwen2:72b 14066dfa503f 41 GB 2 days ago
nomic-embed-text:latest 0a109f422b47 274 MB 2 days ago
llama3:latest 365c0bd3c000 4.7 GB 3 days ago
tinyllama:latest 2644915ede35 637 MB 3 days ago
```
Screenshot:
<img width="1392" alt="Screenshot 2024-10-29 at 09 30 46" src="https://github.com/user-attachments/assets/1203fe66-9692-4099-8b32-973ee37cb3b0">
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PR_kwDOJ0Z1Ps5oKvra
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Update faq.md: proxy support
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LLava:13B Model Outputting ############### After Period of Inactivity
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| 2025-01-07T23:57:50
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### What is the issue?
**Description:**
I encountered an issue with the Ollama LLava:13B model where the output was consistently `###############` after a period of inactivity. Restarting the `ollama.service` resolved the issue temporarily, but the root cause remains unclear.
---
**Environment:**
- **Model:** Ollama LLava:13B
- **System Specs:**
- **OS:** Debian (Google Cloud VM)
- **GPU:** NVIDIA L4
- **Driver/CUDA:** Latest drivers compatible with NVIDIA L4
- **Service:** Ollama.service
---
**Issue Details:**
1. I was testing the Ollama LLava:13B model without any issues for an extended session.
2. After stopping the model for a period of time, when attempting to restart inference, the output was consistently `###############`.
3. Restarting the `ollama.service` resolved the issue temporarily, and normal functionality was restored.
4. The root cause of the incorrect output (`###############`) is unknown and has not been encountered previously in continuous usage.
---
**Steps to Reproduce:**
1. Start Ollama LLava:13B model on a Google Cloud VM with an NVIDIA L4 GPU.
2. Perform inference operations successfully.
3. Allow a period of inactivity (length uncertain, may be related to session timeout or resource deallocation).
4. Resume inference, resulting in `###############` as the output.
5. Restart `ollama.service` to restore normal function.
---
**Expected Behavior:**
The model should resume normal operation after a period of inactivity, without needing to restart the service.
---
**Observed Behavior:**
After resuming inference post-inactivity, the model consistently output `###############` until the service was restarted.
---
**Additional Information:**
- Logs and model outputs prior to the issue were normal.
- This behavior suggests a potential issue with resource management, memory, or session state handling in the model.
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.3.10
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PR_kwDOJ0Z1Ps5loaiV
| 2,296
|
append image tags to user content
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| 2024-02-01T00:31:48
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CONTRIBUTOR
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summary of changes:
1. add `[img-x]` to prompt content when there are images. `x` corresponds to the image's id. for generate, this is just the image's index in the Images list. for chat, this is the image's index of among all images in the messages list
2. account for image embedding when trimming the context. image projections produce 768 tokens for clip models. check and add this number to the total tokens count
3. if the image tokens exceed the max token count, do not add images to the final images list and strip the image tag from the prompt content
|
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I_kwDOJ0Z1Ps6Z1uVw
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Counting tokens in text before embedding
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| 2024-10-11T10:14:55
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When creating a vector database, we use embedding models such as bge-m3. The problem is that if the size of the text sent for vectorization does not fit into the context window of the model, the data is simply lost! and the Ollama project does not have a SINGLE MODEL!!! that would simply calculate tokens in the text before sending! For example, the bge-m3 model uses the RoBERTa algorithm, it would be very convenient if there was at least one model available via API that would simply count the exact number of tokens in the text! Is it possible to add this? Without this, working with embedding is difficult.
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I_kwDOJ0Z1Ps6Tf67S
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error loading model "Xiaobu Embedding v2" :error="llama runner process has terminated: signal: segmentation fault (core dumped)"
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### What is the issue?
ollama-1 | time=2024-08-20T02:46:33.204Z level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=25 layers.offload=0 layers.split="" memory.available="[22.2 GiB]" memory.required.full="820.5 MiB" memory.required.partial="0 B" memory.required.kv="48.0 MiB" memory.required.allocations="[820.5 MiB]" memory.weights.total="625.2 MiB" memory.weights.repeating="584.0 MiB" memory.weights.nonrepeating="41.3 MiB" memory.graph.full="128.0 MiB" memory.graph.partial="128.0 MiB"
ollama-1 | time=2024-08-20T02:46:33.206Z level=INFO source=server.go:393 msg="starting llama server" cmd="/tmp/ollama1960294902/runners/cpu_avx2/ollama_llama_server --model /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 46451"
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=sched.go:445 msg="loaded runners" count=1
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:593 msg="waiting for llama runner to start responding"
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error"
ollama-1 | INFO [main] build info | build=1 commit="1e6f655" tid="127020122728320" timestamp=1724121993
ollama-1 | INFO [main] system info | n_threads=16 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="127020122728320" timestamp=1724121993 total_threads=32
ollama-1 | INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="46451" tid="127020122728320" timestamp=1724121993
ollama-1 | llama_model_loader: loaded meta data with 27 key-value pairs and 389 tensors from /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a (version GGUF V3 (latest))
ollama-1 | llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
ollama-1 | llama_model_loader: - kv 0: general.architecture str = bert
ollama-1 | llama_model_loader: - kv 1: general.type str = model
ollama-1 | llama_model_loader: - kv 2: general.name str = Xiaobu Embedding v2
ollama-1 | llama_model_loader: - kv 3: general.version str = v2
ollama-1 | llama_model_loader: - kv 4: general.basename str = xiaobu-embedding
ollama-1 | llama_model_loader: - kv 5: general.size_label str = 324M
ollama-1 | llama_model_loader: - kv 6: general.tags arr[str,1] = ["mteb"]
ollama-1 | llama_model_loader: - kv 7: bert.block_count u32 = 24
ollama-1 | llama_model_loader: - kv 8: bert.context_length u32 = 512
ollama-1 | llama_model_loader: - kv 9: bert.embedding_length u32 = 1024
ollama-1 | llama_model_loader: - kv 10: bert.feed_forward_length u32 = 4096
ollama-1 | llama_model_loader: - kv 11: bert.attention.head_count u32 = 16
ollama-1 | llama_model_loader: - kv 12: bert.attention.layer_norm_epsilon f32 = 0.000000
ollama-1 | llama_model_loader: - kv 13: general.file_type u32 = 1
ollama-1 | llama_model_loader: - kv 14: bert.attention.causal bool = false
ollama-1 | llama_model_loader: - kv 15: bert.pooling_type u32 = 1
ollama-1 | llama_model_loader: - kv 16: tokenizer.ggml.token_type_count u32 = 2
ollama-1 | llama_model_loader: - kv 17: tokenizer.ggml.model str = bert
ollama-1 | llama_model_loader: - kv 18: tokenizer.ggml.pre str = xiaobu
ollama-1 | llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
ollama-1 | llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
ollama-1 | llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 100
ollama-1 | llama_model_loader: - kv 22: tokenizer.ggml.seperator_token_id u32 = 102
ollama-1 | llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 0
ollama-1 | llama_model_loader: - kv 24: tokenizer.ggml.cls_token_id u32 = 101
ollama-1 | llama_model_loader: - kv 25: tokenizer.ggml.mask_token_id u32 = 103
ollama-1 | llama_model_loader: - kv 26: general.quantization_version u32 = 2
ollama-1 | llama_model_loader: - type f32: 244 tensors
ollama-1 | llama_model_loader: - type f16: 145 tensors
ollama-1 | llm_load_vocab: special tokens cache size = 5
ollama-1 | llm_load_vocab: token to piece cache size = 0.0769 MB
ollama-1 | llm_load_print_meta: format = GGUF V3 (latest)
ollama-1 | llm_load_print_meta: arch = bert
ollama-1 | llm_load_print_meta: vocab type = WPM
ollama-1 | llm_load_print_meta: n_vocab = 21128
ollama-1 | llm_load_print_meta: n_merges = 0
ollama-1 | llm_load_print_meta: vocab_only = 0
ollama-1 | llm_load_print_meta: n_ctx_train = 512
ollama-1 | llm_load_print_meta: n_embd = 1024
ollama-1 | llm_load_print_meta: n_layer = 24
ollama-1 | llm_load_print_meta: n_head = 16
ollama-1 | llm_load_print_meta: n_head_kv = 16
ollama-1 | llm_load_print_meta: n_rot = 64
ollama-1 | llm_load_print_meta: n_swa = 0
ollama-1 | llm_load_print_meta: n_embd_head_k = 64
ollama-1 | llm_load_print_meta: n_embd_head_v = 64
ollama-1 | llm_load_print_meta: n_gqa = 1
ollama-1 | llm_load_print_meta: n_embd_k_gqa = 1024
ollama-1 | llm_load_print_meta: n_embd_v_gqa = 1024
ollama-1 | llm_load_print_meta: f_norm_eps = 1.0e-12
ollama-1 | llm_load_print_meta: f_norm_rms_eps = 0.0e+00
ollama-1 | llm_load_print_meta: f_clamp_kqv = 0.0e+00
ollama-1 | llm_load_print_meta: f_max_alibi_bias = 0.0e+00
ollama-1 | llm_load_print_meta: f_logit_scale = 0.0e+00
ollama-1 | llm_load_print_meta: n_ff = 4096
ollama-1 | llm_load_print_meta: n_expert = 0
ollama-1 | llm_load_print_meta: n_expert_used = 0
ollama-1 | llm_load_print_meta: causal attn = 0
ollama-1 | llm_load_print_meta: pooling type = 1
ollama-1 | llm_load_print_meta: rope type = 2
ollama-1 | llm_load_print_meta: rope scaling = linear
ollama-1 | llm_load_print_meta: freq_base_train = 10000.0
ollama-1 | llm_load_print_meta: freq_scale_train = 1
ollama-1 | llm_load_print_meta: n_ctx_orig_yarn = 512
ollama-1 | llm_load_print_meta: rope_finetuned = unknown
ollama-1 | llm_load_print_meta: ssm_d_conv = 0
ollama-1 | llm_load_print_meta: ssm_d_inner = 0
ollama-1 | llm_load_print_meta: ssm_d_state = 0
ollama-1 | llm_load_print_meta: ssm_dt_rank = 0
ollama-1 | llm_load_print_meta: model type = 335M
ollama-1 | llm_load_print_meta: model ftype = F16
ollama-1 | llm_load_print_meta: model params = 324.47 M
ollama-1 | llm_load_print_meta: model size = 620.50 MiB (16.04 BPW)
ollama-1 | llm_load_print_meta: general.name = Xiaobu Embedding v2
ollama-1 | llm_load_print_meta: UNK token = 100 '[UNK]'
ollama-1 | llm_load_print_meta: SEP token = 102 '[SEP]'
ollama-1 | llm_load_print_meta: PAD token = 0 '[PAD]'
ollama-1 | llm_load_print_meta: CLS token = 101 '[CLS]'
ollama-1 | llm_load_print_meta: MASK token = 103 '[MASK]'
ollama-1 | llm_load_print_meta: LF token = 0 '[PAD]'
ollama-1 | llm_load_print_meta: max token length = 48
ollama-1 | llm_load_tensors: ggml ctx size = 0.16 MiB
ollama-1 | llm_load_tensors: CPU buffer size = 620.50 MiB
ollama-1 | time=2024-08-20T02:46:33.458Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server loading model"
ollama-1 | llama_new_context_with_model: n_ctx = 8192
ollama-1 | llama_new_context_with_model: n_batch = 512
ollama-1 | llama_new_context_with_model: n_ubatch = 512
ollama-1 | llama_new_context_with_model: flash_attn = 0
ollama-1 | llama_new_context_with_model: freq_base = 10000.0
ollama-1 | llama_new_context_with_model: freq_scale = 1
ollama-1 | llama_kv_cache_init: CPU KV buffer size = 768.00 MiB
ollama-1 | llama_new_context_with_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB
ollama-1 | llama_new_context_with_model: CPU output buffer size = 0.00 MiB
ollama-1 | llama_new_context_with_model: CPU compute buffer size = 25.01 MiB
ollama-1 | llama_new_context_with_model: graph nodes = 851
ollama-1 | llama_new_context_with_model: graph splits = 1
ollama-1 | time=2024-08-20T02:46:34.161Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server not responding"
ollama-1 | time=2024-08-20T02:46:35.314Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error"
ollama-1 | [GIN] 2024/08/20 - 02:46:36 | 500 | 3.116673729s | 172.17.0.1 | POST "/v1/embeddings"
ollama-1 | time=2024-08-20T02:46:36.317Z level=ERROR source=sched.go:451 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault (core dumped)"
### OS
Docker
### GPU
Other
### CPU
Intel
### Ollama version
0.3.5
|
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| 2,418,427,797
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I_kwDOJ0Z1Ps6QJkeV
| 5,792
|
List command optional flag to display license
|
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open
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| 2024-07-19T09:08:15
| 2024-07-19T09:08:15
| null |
NONE
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It could be useful, in corporate environment, for security and compliance, to be able to display with list command, using an optional flag, also type of license, for example:
ollama list -l
display usual information, but also related license name for each model
| null |
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| 1,942,651,885
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PR_kwDOJ0Z1Ps5cxlan
| 784
|
check for newer updates
|
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| 2023-10-13T21:13:04
| 2023-10-13T21:29:47
| 2023-10-13T21:29:46
|
CONTRIBUTOR
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Check if there are newer version of ollama updates available. This prevents multiple updates in a row.
|
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I_kwDOJ0Z1Ps59qTAJ
| 2,278
|
Add Code Llama 70B model
|
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| 2024-01-30T16:12:31
| 2024-06-16T13:35:57
| 2024-01-30T19:20:35
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NONE
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Code Llama 70B now available
--
"We just released new versions of Code Llama, our LLM for code generation. Code Llama 70B consists of two new 70B parameter base models and one additional instruction fine-tuned model — CodeLlama-70B-Instruct, which achieves the strongest HumanEval performance of any Llama model we’ve released to date. CodeLlama-70B, CodeLlama-70B-Python and CodeLlama-70B-Instruct are all available now under the same license as Llama 2 and previous Code Llama models to support both research and commercial innovation.
Code Llama 70B now available
We just released new versions of [Code Llama, our LLM for code generation](https://content.atmeta.com/n/MjY3LVBWQi05NDEAAAGQ-hqn6RRHXTr9A_sGCB8j1pjEBzeFiLec_IBvLeOIVdMk_HvX3ZdvMWg6MdwGy9Z8ZUJxjVAyAGy0jlA=). Code Llama 70B consists of two new 70B parameter base models and one additional instruction fine-tuned model — CodeLlama-70B-Instruct, which achieves the strongest HumanEval performance of any Llama model we’ve released to date.
CodeLlama-70B, CodeLlama-70B-Python and CodeLlama-70B-Instruct are all available now under the same license as Llama 2 and previous Code Llama models to support both research and commercial innovation.
We’re excited to continue our support of the OSS community with Llama and we can’t wait to see what you’ll build."
Says Meta.
it could be great to have it in Ollama 👍
|
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I_kwDOJ0Z1Ps6nudJJ
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|
[Enhancement] New Cohere models are not validated in `config.json`
|
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| 2025-01-27T20:21:20
| 2025-01-27T20:30:57
| 2025-01-27T20:30:55
|
NONE
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Cohere [has released](https://docs.cohere.com/v2/v1/docs/models/an-overview-of-coheres-models) several updated versions of old models (i.e. Command-R, Command-Light, and Command-R Plus) plus their embedding and reranker models; they have also release their new `command-r7b`. I note that all of these are available for free via their API, and they integrate well without significant setup.
Continue's `config.json` doesn't validate anything except `command-r` and `command-r-plus`, so even the new updated version (`command-r-plus-08-2024`) wouldn't validate.
I note that you can still put these values, it just throws a warning error. If using `AUTODETECT`, only `command-r` and `command-r-plus` will be detected.
OS: MacOS
GPU: Apple
CPU: Apple
Ollama version: 0.5.7
|
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I_kwDOJ0Z1Ps59ldcy
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|
macOS vram layer offloading
|
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I_kwDOJ0Z1Ps5sOIeQ
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Using already downloaded models
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I want to use the models I have already downloaded using the link provided via email from Meta which are saved in a specific location on my PC. Is there any way to do that?
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runner.go: Add unit tests for context shifting
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| 2024-11-25T23:07:31
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This also makes it easier to truncate long inputs the same as shifting but does not actually implement it. This type of truncation has a trade off between quality and time to first token.
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PR_kwDOJ0Z1Ps5U3HIs
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pass model and predict options
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I_kwDOJ0Z1Ps6m14bn
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ollama run llama3-70b pulling very slow
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### What is the issue?
Hi,
I just downloaded ollama and tried to run the command ` ollama pull llama3:70b-instruct-q2_K`, the download starts with good speed and exactly after 4-5 seconds speeds drop drastically to 165kbps and lower. Most of the times it stays at 0B, I tried this with both my home network and office networks same results.
Sometimes download itself fails with the error message
`Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/a0/a09affdc2dffd0db03e7f0d1344c374d85add72ea4c715aa69b7b427f03d35d3/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%2F20250120%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20250120T122125Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=16cdd624dc40ad8b40c4ca96bca353dfcfd259afe1ba911684b37fd44bb12541": net/http: TLS handshake timeout
`
Please help me !
### OS
Linux
### GPU
AMD
### CPU
Intel
### Ollama version
ollama version is 0.5.7
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Polish loganalyzer example
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Just added a couple commands in case folks aren't familiar with python.
```bash
$ python loganalysis.py logtest.logfile
>>>bash
2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:43 +0000] "GET / HTTP/1.1" 200 615 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
2023-11-10 07:17:44 2023/11/10 13:17:44 [error] 29#29: *1 open() "/usr/share/nginx/html/favicon.ico" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /favicon.ico HTTP/1.1", host: "localhost:8080", referrer: "http://localhost:8080/"
2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:44 +0000] "GET /favicon.ico HTTP/1.1" 404 555 "http://localhost:8080/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
2023-11-10 07:17:50 2023/11/10 13:17:50 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
2023-11-10 07:17:50 192.168.65.1 - - [10/Nov/2023:13:17:50 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
2023-11-10 07:18:53 2023/11/10 13:18:53 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
2023-11-10 07:18:53 192.168.65.1 - - [10/Nov/2023:13:18:53 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
>>>
This log shows that there are four requests made to your Nginx server:
1. A successful request for the root directory "/" which returns a `200 OK` status code and 615 bytes of data.
2. An unsuccessful request for `/favicon.ico` which returns a `404 Not Found` status code and 555 bytes of data. This is because the file was not found in the expected location (`/usr/share/nginx/html/favicon.ico`).
3. Two unsuccessful requests for `/ahstat`, each returning a `404 Not Found` status code and 555 bytes of data. This is because the file was not found in the expected location (`/usr/share/nginx/html/ahstat`).
The first error message indicates that Nginx could not find the `favicon.ico` file in the specified location, but it's not a critical error and does not affect the operation of your server. The other two errors are for requests to `/ahstat`, which means that there is no such file or directory on your server.
To fix these errors, you should make sure that all requested files exist in the appropriate locations and update your Nginx configuration if necessary.
```
p.s. if you switch from codebooga to qwen2, the results are still pretty good despite a much smaller model. It didn't make my machine crawl rendering it, which was a nice plus.
```bash
$ python loganalysis.py logtest.logfile
The log entries you've provided seem to be from an Nginx server, which is commonly used as a web server and reverse proxy. Here's what each entry tells us:
### 2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 37
This line indicates that a new worker process (process ID 37) has started in the Nginx server.
### 2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:43 +0000] "GET / HTTP/1.1" 200 615 "-"
This line shows a request for the root URL ("/") from an IP address (192.168.65.1) with status code `200` indicating success, and a response size of `615`. The `-` symbol typically indicates that no user agent or referer was sent.
### 2023-11-10 07:17:44 [error] 29#29: *1 open() "/usr/share/nginx/html/favicon.ico" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /favicon.ico HTTP/1.1", host: "localhost:8080", referrer: "http://localhost:8080/"
This error indicates that the Nginx server was unable to find the file `favicon.ico` in the specified directory (`/usr/share/nginx/html`). The client attempting this request is at IP address 192.168.65.1, and it refers back to a previous request made from the same IP.
### 2023-11-10 07:17:44 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
This error is similar to the previous one but concerns a file named `ahstat`. The same IP address (192.168.65.1) and client context are involved.
### 2023-11-10 07:18:53 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
This is a repeat of the previous error message from 7 minutes later.
### 2023-11-10 07:18:53 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
This is a repeat of the previous error message from another instance.
### Other Entries
The other entries are similar to the first two but show requests for different files (`favicon.ico` and `ahstat`) that do not exist in the specified directory, resulting in 404 Not Found errors.
### Conclusion
These logs indicate issues with Nginx attempting to serve non-existent files. The server is unable to locate `favicon.ico` and `ahstat`, which are likely custom resources intended for web pages but are missing from their designated directories (`/usr/share/nginx/html`). This could be due to incorrect file paths, missing files during deployment, or changes in the filesystem that Nginx does not recognize. To resolve these issues:
1. **Check File Paths**: Ensure that the file paths specified in your configuration (e.g., `root /usr/share/nginx/html;`) are correct and match where your files actually reside.
2. **File Existence**: Confirm that the files (`favicon.ico`, `ahstat`) exist at the specified locations on the filesystem.
3. **Configuration Update**: If you've moved or deleted these files, update your Nginx configuration to reflect the new file paths or remove references to non-existent files.
By addressing these points, you can ensure that Nginx serves the correct resources without errors.
```
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I_kwDOJ0Z1Ps51-3sM
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Support AMD GPUs on Intel Macs
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I'm currently trying out the ollama app on my iMac (i7/Vega64) and I can't seem to get it to use my GPU.
I have tried running it with num_gpu 1 but that generated the warnings below.
`
2023/11/06 16:06:33 llama.go:384: starting llama runner
2023/11/06 16:06:33 llama.go:386: error starting the external llama runner: fork/exec /var/folders/2z/r_0t221x2blbq02n5dp2m5fr0000gn/T/ollama1975281143/llama.cpp/gguf/build/metal/bin/ollama-runner: bad CPU type in executable
2023/11/06 16:06:33 llama.go:384: starting llama runner
2023/11/06 16:06:33 llama.go:442: waiting for llama runner to start responding
{"timestamp":1699283193,"level":"WARNING","function":"server_params_parse","line":873,"message":"Not compiled with GPU offload support, --n-gpu-layers option will be ignored. See main README.md for information on enabling GPU BLAS support","n_gpu_layers":-1}
{"timestamp":1699283193,"level":"INFO","function":"main","line":1324,"message":"build info","build":219,"commit":"9e70cc0"}
{"timestamp":1699283193,"level":"INFO","function":"main","line":1330,"message":"system info","n_threads":6,"n_threads_batch":-1,"total_threads":12,"system_info":"AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | "}
`
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I_kwDOJ0Z1Ps55wHsF
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[Feature Request] Add Discussion Tab to Ollama Repo
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| 2023-12-14T23:32:19
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There's a lot of useful content in the Discord Ollama that is hard to search/organize. Given that the relatively large Ollama discord only has one channel `general` - requests for help/etc can get lost pretty easily.
@jmorganca I think it would be worth adding the Discussions Tab to the Github
|
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PR_kwDOJ0Z1Ps5s2DNL
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Support unicode characters in model path
|
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| 2024-04-16T20:11:25
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When running the c++ subprocess, unicode characters in file names were not being parsed correctly, resulting in an error. This changes `server.cpp` to use `wmain` to receive the wide characters and converts them first.
Closes #3273
Fixes #2888
Fixes #3120
|
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I_kwDOJ0Z1Ps58D1mP
| 1,998
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ggml-cuda.cu:7850: !"CUDA error" Aborted (core dumped) with 8 GPUs
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Error: Post "http://127.0.0.1:11434/api/generate": EOF
GPU INFO:
![Uploading image.png…]()
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I_kwDOJ0Z1Ps6KJ721
| 4,651
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Error When Running Granite Code 20b
|
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| 2024-05-26T17:36:56
| 2024-05-29T21:09:44
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### What is the issue?
When executing `ollama run granite-code:20b` getting the following error on `linux`:
```bash
Error: llama runner process has terminated: signal: aborted (core dumped)
```
### OS
Linux
### GPU
Other
### CPU
Intel
### Ollama version
0.1.38
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PR_kwDOJ0Z1Ps5WWUKD
| 209
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enable k quants
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| 2023-07-25T15:40:15
| 2023-07-25T18:53:59
| 2023-07-25T18:53:29
|
CONTRIBUTOR
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I_kwDOJ0Z1Ps6EYaQn
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dual GPU 8G/16G - CUDA error: out of memory with dolphin-mixtral
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### What is the issue?
Ollama crashes out entirely with error (throws error, then terminates process)
[CUDA error: out of memory current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:445 cudaMalloc((void **) &ptr, look_ahead_size) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error"](error: out of memory current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:445 cudaMalloc((void **) &ptr, look_ahead_size) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error")
### What did you expect to see?
Output (any)
### Steps to reproduce
1. Start Ollama / Navigate to Open WebUI
2. Enter any text
Notes:
- used dolphin-mixtral as the model
- CUDA_VISIBLE_DEVICES used to set GPU order (16GB, 8GB)
### Are there any recent changes that introduced the issue?
Update from 0.1.29 to 0.1.30 (reverting back to 0.1.29 fixed)
### OS
Windows
### Architecture
amd64
### Platform
_No response_
### Ollama version
0.1.30
### GPU
Nvidia
### GPU info
GPU 0: NVIDIA GeForce GTX 1080 (8GB)
GPU 1: Tesla P100-PCIE-16GB
### CPU
AMD
### Other software
Windows Server 2022 Standard x64 21H2
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PR_kwDOJ0Z1Ps5g2uw2
| 1,340
|
Update generate_linux.go
|
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| 2023-12-01T04:59:06
| 2024-01-18T22:17:26
| 2024-01-18T22:17:26
|
NONE
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Previous PR: https://github.com/jmorganca/ollama/pull/985
The issue still here
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I_kwDOJ0Z1Ps6ZjL3W
| 7,151
|
Falcon Mamba Quality Issues
|
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| 2024-10-09T14:39:01
| 2024-10-09T15:02:36
| 2024-10-09T15:02:36
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NONE
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### What is the issue?
Ollama has a quality issue when running Falcon Mamba that is not present when using Llama.cpp.
#### Ollama:
```
>>> What is 10*10/10 + 10/10*10?
Let's solve the given equation step by step:
- First, we need to simplify each term in parentheses according to BODMAS rule which stands for Brackets first then Orders(or Powers and Square Roots), followed by Division or Multiplication
from left side. Finally Addition/Subtraction
So our expression becomes
=10*(1+0)
- Next, we solve the sum inside bracket
This gives us:
=(20)*2
Therefore
<div style="text-align:center">
The final answer is equal to **40**.
</br>
</body></html>
```
#### Llama.cpp:
```
> What is 10*10/10 + 10/10*10?
To solve this expression, we should follow the order of operations, which is often remembered by the acronym PEMDAS (Parentheses, Exponents, Multiplication and Division, and Addition and Subtraction).
The expression given is: 10*10/10 + 10/10*10
First, we will do the multiplication and division from left to right:
1. 10*10/10 = 100/10 = 10
2. 10/10*10 = 1*10 = 10
Now, we can replace these values back into the expression:
10 + 10
Finally, we add the numbers:
10 + 10 = 20
So, the result of the expression 10*10/10 + 10/10*10 is 20.
```
Both were using the same model, [Falcon Mamba 7b Instruct q4_0](https://ollama.com/Hudson/falcon-mamba-instruct) and the same system prompt. All settings are default on both.
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.12
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I_kwDOJ0Z1Ps6X4jCr
| 6,955
|
nvidia gpu discovery problem in docker container on wsl
|
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| 2024-09-25T14:37:45
| 2024-11-05T23:24:56
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NONE
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### What is the issue?
Developed a chat app with a function of pulling the model by user directly into streamlit and select the model through dropdown.
i have pulled several model succesfully but when i try to do with large model Eg: Llava ii gives me a Asyncio.timeout.error.
i am using WSL2 (UBUNTU) and running the chat app through docker file
ERROR:
[ERROR FILE TIMEOUT ERROR.txt](https://github.com/user-attachments/files/17132576/ERROR.FILE.TIMEOUT.ERROR.txt)
Docker compose File :
[Docker_compose.txt](https://github.com/user-attachments/files/17132787/Docker_compose.txt)
also, somehow i am not able to use GPU in docker, any solution for that too?
thankyou!
### OS
Windows, Docker, WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.11
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PR_kwDOJ0Z1Ps5usgqt
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|
llm: add minimum based on layer size
|
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| 2024-05-07T00:05:47
| 2024-05-07T16:26:34
| 2024-05-07T16:26:33
|
CONTRIBUTOR
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adjust minimum memory requirements based on the model being loaded and reduce the static minimum
|
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| 2,363,307,735
|
I_kwDOJ0Z1Ps6M3TbX
| 5,155
|
Error when using deepseek-coder-v2
|
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| 2024-06-20T00:54:34
| 2024-06-20T15:35:20
| 2024-06-20T15:20:44
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### What is the issue?
Error when running deepseek-coder-v2:
`
(base) root@fdtech-ai-node08:~# ollama run deepseek-coder-v2
pulling manifest
pulling 5ff0abeeac1d... 94% ▕██████████████████████████████████████████████████ pulling manifest
pulling 5ff0abeeac1d... 100% ▕████████████████▏ 8.9 GB
pulling 732caedf08d1... 100% ▕████████████████▏ 112 B
pulling 4bb71764481f... 100% ▕████████████████▏ 13 KB
pulling 1c8f573e830c... 100% ▕████████████████▏ 1.1 KB
pulling 19f2fb9e8bc6... 100% ▕████████████████▏ 32 B
pulling c17ee51fe152... 100% ▕████████████████▏ 568 B
verifying sha256 digest
writing manifest
removing any unused layers
success
Error: error loading model /root/.ollama/models/blobs/sha256:5ff0abeeac1d2dbdd54 55c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046
(base) root@fdtech-ai-node08:~# ollama run deepseek-coder-v2
Error: error loading model /root/.ollama/models/blobs/sha256:5ff0abeeac1d2dbdd5455c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046
(base) root@fdtech-ai-node08:~# ollama run deepseek-coder-v2
Error: error loading model /root/.ollama/models/blobs/sha256:5ff0abeeac1d2dbdd5455c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046
(base) root@fdtech-ai-node08:~# ollama run deepseek-coder-v2
Error: error loading model /root/.ollama/models/blobs/sha256:5ff0abeeac1d2dbdd5455c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046
`
I use 4*A30 to run ollama 0.1.44
### OS
Linux
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.1.44
|
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| 2,758,606,126
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PR_kwDOJ0Z1Ps6GM6x0
| 8,236
|
Update README.md
|
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| 2024-12-25T08:05:46
| 2024-12-27T18:16:06
| 2024-12-27T18:16:06
|
CONTRIBUTOR
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New extention contribution
|
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| 2,384,588,027
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I_kwDOJ0Z1Ps6OIez7
| 5,416
|
Improve ollama's Output Speed
|
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| 2024-07-01T19:30:45
| 2024-10-16T16:19:37
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NONE
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I have noticed that ollama always outputs content at a fixed speed, and most of the time, the GPU is not fully utilized(0% load), indicating that the bottleneck in generating content is not in the GPU.
At the same time, I have observed that during content output, one CPU core consistently has a high load(70% load), suggesting that the bottleneck might be in the CPU decoding process.
Therefore, could you consider implementing multi-threaded decoding to leverage the multi-core capabilities of the CPU to accelerate the output speed?
I have already searched Google for discussions on this issue but did not find anything relevant.
If my analysis is incorrect or the issue cannot be resolved, you can directly close this issue. Thank you.
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| 2,194,253,438
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I_kwDOJ0Z1Ps6CyaZ-
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|
Out of memory - GTX 1650 4G
|
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NONE
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### What is the issue?
CUDA error: out of memory
current device: 0, in function ggml_cuda_pool_malloc_vmm at C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:8583
cuMemCreate(&handle, reserve_size, &prop, 0)
GGML_ASSERT: C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:256: !"CUDA error"
### What did you expect to see?
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Windows
### Architecture
x86
### Platform
_No response_
### Ollama version
0.1.28
### GPU
Nvidia
### GPU info
### CPU
Intel
### Other software
_No response_
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app: also clean up tempdir runners on install
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MEMBER
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PR_kwDOJ0Z1Ps50zAEj
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feat: compatible with openai embedding api
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Compatible with openAI embedding api
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I_kwDOJ0Z1Ps6QD-EH
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Search/Filter by license
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First, thanks for all the awesome work !
It would be nice to be able to search / filter by license on this page https://ollama.com/library
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PR_kwDOJ0Z1Ps50xgYG
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server: fix model reloads when setting `OLLAMA_NUM_PARALLEL`
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I_kwDOJ0Z1Ps6Vu3YY
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[Feature request] compatibility with vm balloon ram
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| 2024-09-17T18:24:53
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NONE
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Hi, it looks like ollama is not compatibel with ballon ram inside of an VM, i wanted to run ollama inside of an balloon ram, but i realized that when i have balloon enabled ollama thinks that there is just as example 5GB Ram available out of the 15GB it could get, because they are not provisoned at the start time when ollama checks how mutch ram is available.
i think it would be aweseome to have an envionment variable to explicitly specify how mutch ram its allowed to take regardless how many there is at the time of starting.
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I_kwDOJ0Z1Ps6VSu_Z
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Add model Phi3-Vision
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| 2024-09-04T08:57:28
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Phi3 Vision weights are opensourced in hugging face
https://huggingface.co/microsoft/Phi-3-vision-128k-instruct
Hoping to see it in ollama models.
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The DeepSeek-Coder AI model is not loading entirely into RAM, causing the model responses to be very slow.
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| 2023-11-22T13:40:28
| 2023-12-19T18:48:51
| 2023-12-19T18:48:50
|
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Hi,
I am using Ollama on a Mac Studio M1 Max with 64GB RAM. I have experimented with different models such as DeepSeek Coder AI 33b, WizardCoder Python 13b, and Mistral 7b text. Most of these models are stored entirely in RAM, except for the DeepSeek Coder model. The 33b model uses less than 4GB of RAM, while WizardCoder uses a little over 13GB of RAM. I am not sure how I can increase the memory limit for a specific model. I've tried different versions of the DeepSeek Coder model, but they all encounter similar problems when using 33b models.
Is there any parameter that I need to include in `Modelfile` or `Command` while running the model?
|
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| 2,276,719,349
|
I_kwDOJ0Z1Ps6Hs_r1
| 4,113
|
The API has become extremely flaky. It used to be very stable.
|
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### What is the issue?
There was a problem with request to openai API:
('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
I guess it is the same as this:
panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0xf2607e]
goroutine 7 [running]:
github.com/ollama/ollama/server.(*runnerRef).needsReload(0xc000477900, {0x1b670da0, 0xc0000e8c80}, 0xc0001422d0)
/home/developer/ollama/server/sched.go:459 +0x15e
github.com/ollama/ollama/server.(*Scheduler).processPending(0xc0000e8dc0, {0x1b670da0, 0xc0000e8c80})
/home/developer/ollama/server/sched.go:130 +0x465
github.com/ollama/ollama/server.(*Scheduler).Run.func1()
/home/developer/ollama/server/sched.go:107 +0x1f
created by github.com/ollama/ollama/server.(*Scheduler).Run in goroutine 1
/home/developer/ollama/server/sched.go:106 +0xb4
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
just built from source
|
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| 2,442,653,638
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PR_kwDOJ0Z1Ps53IbKT
| 6,121
|
Speech mod feature
|
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| 2024-08-01T14:39:33
| 2024-11-21T10:03:09
| 2024-11-21T10:03:09
|
NONE
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|
This PR adds a speech mod.
|
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| 2,290,870,503
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I_kwDOJ0Z1Ps6Ii-jn
| 4,358
|
No Devices Found on Ryzen 7 8840u
|
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| 2024-05-11T12:17:13
| 2024-05-25T04:07:43
| 2024-05-21T23:18:13
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### What is the issue?
When I try to load a model I receive this error message:
`Error: llama runner process has terminated: signal: aborted (core dumped) error:Could not initialize Tensile host: No devices found`
Here is the docker compose file that I am using to run this:
```
version: '3'
services:
ollama:
image: ollama/ollama:rocm
container_name: ollama
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
group_add:
- video
ports:
- "11434:11434"
security_opt:
- "seccomp:unconfined"
environment:
- HSA_OVERRIDE_GFX_VERSION="11.0.3"
volumes:
- ollama_data:/root/.ollama
volumes:
ollama_data:
```
I assumed that since this CPU says that it uses a radeon 780m that it would be the same override as the previous 7000 series, but that doesn't look to be the case.
I have tried manually setting the VRAM in the BIOS to 8G also.
Here is the full logs:
```
2024/05/11 12:13:16 routes.go:1006: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:*] OLLAMA_RUNNERS_DIR: OLLAMA_TMPDIR:]"
time=2024-05-11T12:13:16.258Z level=INFO source=images.go:704 msg="total blobs: 5"
time=2024-05-11T12:13:16.258Z level=INFO source=images.go:711 msg="total unused blobs removed: 0"
time=2024-05-11T12:13:16.258Z level=INFO source=routes.go:1052 msg="Listening on [::]:11434 (version 0.1.36)"
time=2024-05-11T12:13:16.259Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama3497058117/runners
time=2024-05-11T12:13:18.342Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60002]"
time=2024-05-11T12:13:18.344Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:18.345Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:18.345Z level=INFO source=types.go:71 msg="inference compute" id=0 library=rocm compute=gfx1103 driver=0.0 name=1002:1900 total="16.0 GiB" available="16.0 GiB"
[GIN] 2024/05/11 - 12:13:19 | 200 | 38.371µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/05/11 - 12:13:19 | 200 | 629.833µs | 127.0.0.1 | GET "/api/tags"
[GIN] 2024/05/11 - 12:13:31 | 200 | 40.516µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/05/11 - 12:13:31 | 200 | 1.554865ms | 127.0.0.1 | POST "/api/show"
[GIN] 2024/05/11 - 12:13:31 | 200 | 517.854µs | 127.0.0.1 | POST "/api/show"
time=2024-05-11T12:13:31.125Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:31.125Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:32.412Z level=INFO source=memory.go:127 msg="offload to gpu" layers.real=-1 layers.estimate=33 memory.available="16.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.4 GiB" memory.weights.repeating="4.0 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-05-11T12:13:32.413Z level=INFO source=memory.go:127 msg="offload to gpu" layers.real=-1 layers.estimate=33 memory.available="16.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.4 GiB" memory.weights.repeating="4.0 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-05-11T12:13:32.414Z level=INFO source=server.go:318 msg="starting llama server" cmd="/tmp/ollama3497058117/runners/rocm_v60002/ollama_llama_server --model /root/.ollama/models/blobs/sha256-60af83b47d53e839830a77eb7cf8b7d474a8b4f778aca21dc73b337a304c4b54 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 1 --port 43325"
time=2024-05-11T12:13:32.414Z level=INFO source=sched.go:333 msg="loaded runners" count=1
time=2024-05-11T12:13:32.414Z level=INFO source=server.go:488 msg="waiting for llama runner to start responding"
time=2024-05-11T12:13:32.415Z level=INFO source=server.go:524 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="952d03d" tid="140500936236096" timestamp=1715429612
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140500936236096" timestamp=1715429612 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="43325" tid="140500936236096" timestamp=1715429612
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /root/.ollama/models/blobs/sha256-60af83b47d53e839830a77eb7cf8b7d474a8b4f778aca21dc73b337a304c4b54 (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 = 15
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.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 128001
llama_model_loader: - kv 19: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 20: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
time=2024-05-11T12:13:32.667Z level=INFO source=server.go:524 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: missing pre-tokenizer type, using: 'default'
llm_load_vocab:
llm_load_vocab: ************************************
llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!
llm_load_vocab: CONSIDER REGENERATING THE MODEL
llm_load_vocab: ************************************
llm_load_vocab:
llm_load_vocab: special tokens definition check successful ( 256/128256 ).
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: n_ctx_train = 8192
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_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_yarn_orig_ctx = 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_K - Medium
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.58 GiB (4.89 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 = 128001 '<|end_of_text|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
rocBLAS error: Could not initialize Tensile host: No devices found
time=2024-05-11T12:13:33.420Z level=ERROR source=sched.go:339 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped) error:Could not initialize Tensile host: No devices found"
[GIN] 2024/05/11 - 12:13:33 | 500 | 2.30079255s | 127.0.0.1 | POST "/api/chat"
time=2024-05-11T12:13:33.424Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:33.424Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:33.680Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:33.680Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:33.929Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:33.929Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:34.177Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:34.178Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:34.429Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:34.429Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:34.681Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:34.681Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:34.928Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:34.928Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:35.179Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:35.180Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:35.429Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:35.429Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:35.680Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:35.681Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:35.930Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:35.931Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:36.180Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:36.181Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:36.430Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:36.431Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:36.680Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:36.680Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:36.929Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:36.930Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:37.179Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:37.180Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:37.430Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:37.430Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:37.681Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:37.682Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:37.930Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:37.930Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:38.179Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:38.180Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:38.425Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.00502729
time=2024-05-11T12:13:38.430Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:38.431Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:38.675Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.254936802
time=2024-05-11T12:13:38.680Z level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2024-05-11T12:13:38.681Z level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"11.0.3\""
time=2024-05-11T12:13:38.925Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.505078878
```
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.36
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PR_kwDOJ0Z1Ps5dEs5j
| 829
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added python rag news summary
|
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6HGPqh
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Serving Ollama behind a corporate proxy - Not working in Mac M3
|
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### What is the issue?
Encountered an error while running ollama behind corporate proxy.
Error:
pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/gemma/manifests/2b": dial tcp: lookup registry.ollama.ai: no such host
Tried https://github.com/ollama/ollama/issues/729, But it is not working in Mac M3.
PS: I installed ollam using homebrew. So applied the solution mentioned in #729 in the file "sudo nano /opt/homebrew/opt/ollama/homebrew.ollama.service"
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.32
|
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I_kwDOJ0Z1Ps56E7-k
| 1,601
|
Error: 403 on pulling manifest
|
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| 2023-12-19T07:53:42
| 2023-12-19T21:00:56
| 2023-12-19T17:22:28
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NONE
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Hi,
Thanks very much for building this great project!
I would like to set up ollama in the internal Linux server of my office but it fails pulling pre-trained models as follows.
```
# installation
$ curl https://ollama.ai/install.sh | sh
# run
$ ollama run llama2
pulling manifest
Error: 403:
```
I've tested it before and it was fine in my home, but it only fails in the office internal server maybe due to security policy.
Could anyone please let me know where ollama downloads the pre-trained models? I need to know its URL to make a firewall exception.
Thanks.
|
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I_kwDOJ0Z1Ps6Iit32
| 4,352
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How to set the parameters to make the ollama model output more detailed and comprehensive answers?
|
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The output content is not rich enough and appears to be very uniform.
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I_kwDOJ0Z1Ps51G97e
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|
Unable to push
|
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NONE
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<img width="1349" alt="image" src="https://github.com/jmorganca/ollama/assets/18315/41f9c107-c2a2-4b1b-a9e3-3e4aa70ddcf0">
I have copied the pub keys to my profile, maybe it is a firewall issue?
|
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PR_kwDOJ0Z1Ps5zALjf
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Ngrok yml config
|
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NONE
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An example added for users who are using yml config file to start their Ngrok tunnels.
|
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I_kwDOJ0Z1Ps56DsRb
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Fedora 39 Install Failure on NVIDIA Repo
|
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|
NONE
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I am trying to install Ollama on Fedora 39, but the install script fails on the following:
```
>>> Installing ollama to /usr/local/bin...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
>>> Installing NVIDIA repository...
Adding repo from: https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo
Status code: 404 for https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo (IP: 152.199.39.144)
Error: Configuration of repo failed
```
|
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golang 1.20 version not include slices tool package
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I_kwDOJ0Z1Ps6QYaJa
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codestral 7b
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NONE
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Please add an 7b version of codestral. It is the best coding model, but the 22b version runs just very slow.
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I_kwDOJ0Z1Ps5rysQD
| 89
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layer pulling issue when connection drops and comes back
|
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CONTRIBUTOR
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I ran `ollama run nous-hermes` and it started dl the model. I quit when I saw I was on wifi, and connected wired and tried again. It hung for a minute and then when I asked a question it error'd.
```
❯ ./ollama run library/nous-hermes:latest (base)
pulling manifest
pulling d1735b93e1dc503f... 4% |███████ | (289 MB/6.8 GB, 13 MB/s) [20s:8m36s]^C⏎
main 26s
❯ ./ollama run library/nous-hermes:latest (base)
pulling manifest
>>> Where is justin bieber form
⠋ Error: 400 Bad Request: couldn't open file '/Users/matt/.ollama/models/manifests/library/nous-hermes:latest'
```
So I tried again and it started downloading
this is similar to #61 . no longer see the error, but kinda wish there was an error
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I_kwDOJ0Z1Ps6l0-DL
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Ollama not completing chat request
|
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CONTRIBUTOR
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### What is the issue?
At times, Ollama does not complete chat requests. The client waits and timeouts. Ollama reports the cancellation in the log and cancels the runner. If streaming, Ollama will continue to stream repeating data and not end the stream. If not streaming, Ollama will never respond to the chat request.
In my attempt to track down the issue, I have found the following, which will recreate the issue.
Chat Request:
model: llama3.1:8b-instruct-q2_K
User: good job!
Temperature: 0
Top P: 1
Context Size: 1
A stream request, Ollama will never stop the stream. It will respond with repeating data.
A non-stream request, Ollama will never respond. It will continue to process the chat request.
With this request context size: 3 did the same.
I have tested this on two different computers.
I understand the request above is not an optimal chat request. It only demonstrates the issue. My program is seeing the issue with the following options.
llama3.1:8b-instruct-q2_K
NumCtx = 4096,
Temperature = 0.1f,
TopP = 0.9f,
The history of the chat requests are the following.
1. Context request: 4096 Prompt Eval Count: 439 Eval Count: 72
2. Context request: 4096 Prompt Eval Count: 605 Eval Count: 76
3. Context request: 4096 Prompt Eval Count: 275 Eval Count: 3
4. Context request: 4096 Prompt Eval Count: 677 Eval Count: 76
5. Context request: 4096 Prompt Eval Count: 271 Eval Count: 3
6. Context request: 4096 Prompt Eval Count: 753 Eval Count: 102
The issue was hit when the client was making its 7th chat request. In Ollama's log the 7th message is the last chat request in the log. The server was processing the request and not responding. I have truncated the log. When I canceled the client's request Ollama stopped the request and logged a 500 error for the chat request.
Ollama's output
`[GIN] 2025/01/11 - 19:18:53 | 200 | 709.326503ms | 10.0.0.123 | POST "/api/chat"
time=2025-01-11T19:18:53.376Z level=DEBUG source=sched.go:407 msg="context for request finished"
time=2025-01-11T19:18:53.376Z level=DEBUG source=sched.go:339 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 duration=2562047h47m16.854775807s
time=2025-01-11T19:18:53.376Z level=DEBUG source=sched.go:357 msg="after processing request finished event" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 refCount=0
time=2025-01-11T19:18:53.397Z level=DEBUG source=sched.go:575 msg="evaluating already loaded" model=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9
time=2025-01-11T19:18:53.398Z level=DEBUG source=routes.go:1470 msg="chat request" images=0 prompt="<|start_header_id|>system<|end_header_id|>\n\nYou are a coordinator responsible for managing a group of agents. Your primary task is to choose which agent should be called next based on the user's instructions and the context of the conversation.\r\n\r\nYou have a list of agent names that you may choose from: [A0,A2].\r\n\r\nCritical Rule:\r\n\r\nIf the user's prompt explicitly specifies \"next\" and an agent name follows \"next\" and that name matches an agent in your list, you must select that agent, without exception. This rule applies even if other agents were mentioned earlier in the conversation.\r\n\r\nSecondary Rules:\r\n\r\nIf the user's suggestion does not match any names in the list, ignore the user's suggestion and select the most contextually appropriate agent from the list.\r\nIf the user does not provide any explicit instructions, choose the agent that best continues the flow of the conversation, ensuring logical progression.\r\n\r\nOnly return the agent's name and nothing else.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nI'm A2, a member of Team A. I have 2 chocolates.\n\n{\nA0:4\nA1:? \nA2:? \nTeamATotal:? \nB0:? \nB1:? \nB2:? \nTeamBTotal:? \nC0:? \nC1:? \nC2:? \nTeamCTotal:? }\n\nNEXT: A2<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
time=2025-01-11T19:18:53.398Z level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=752 prompt=271 used=7 remaining=264
[GIN] 2025/01/11 - 19:18:53 | 200 | 94.967933ms | 10.0.0.123 | POST "/api/chat"
time=2025-01-11T19:18:53.473Z level=DEBUG source=sched.go:407 msg="context for request finished"
time=2025-01-11T19:18:53.473Z level=DEBUG source=sched.go:339 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 duration=2562047h47m16.854775807s
time=2025-01-11T19:18:53.473Z level=DEBUG source=sched.go:357 msg="after processing request finished event" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 refCount=0
time=2025-01-11T19:18:53.496Z level=DEBUG source=sched.go:575 msg="evaluating already loaded" model=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9
time=2025-01-11T19:18:53.498Z level=DEBUG source=routes.go:1470 msg="chat request" images=0 prompt="<|start_header_id|>system<|end_header_id|>\n\nYou are A2, a member of Team A. \r\nYour team consists of [A0, A1, A2].\r\nEach member of your team has been given a number of chocolates. Remember the count of chocolates. \r\n\r\nYou have 1 chocolates.\r\n\r\nInstructions:\r\n\r\nTeam Dynamics:\r\n Teams: There are three teams, A, B, and C. Your team consists of [A0, A1, A2].\r\n Team Leaders: The second character '0' in your name indicates you are the team leader. Team leaders can communicate with leaders of other teams but not with non-leaders outside their team.\r\n Team Members: Team members can only communicate within their team.\r\n Teams: must complete the task before calling a different team.\r\n\r\nCollaboration: \r\n A team must collaborate together to accomplish the task. \r\n Suggest the next team leader to do the same by using the `NEXT:` tag, e.g., `NEXT: B0`.\r\n \r\nTermination: \r\n - Once all teams have answered terminate the discussion using `TERMINATE`.\r\n - The termination must include the complete answer, not just a summary.\r\n\r\nConstraints:\r\n - Only suggest players from the given list [A0,A1].\r\n - Adhere strictly to the communication rules and team constraints.\r\n - A team member's chocolate count must not change, it must remain constant.\r\n - The count must be based on the initial count given to you.\r\n - Do not answer for the team unless each team member has provided their count.\r\n\r\nNext Action:\r\nUse `NEXT:` to suggest the next speaker who should contribute based on the current state of the discussion.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nThere are 9 players in this game, split equally into Teams A, B, C. Therefore, each team has 3 players, including the team leader.\r\nThe task is to find out the chocolate count from all nine players. Each team lead must call on another team after they have their team's total.\r\nEvery player must keep track of all player's tally using a JSON format. Every player must answer with the JSON format\r\n{\r\nA0:?\r\nA1:?\r\nA2:?\r\nTeamATotal:?\r\nB0:?\r\nB1:?\r\nB2:?\r\nTeamBTotal:?\r\nC0:?\r\nC1:?\r\nC2:?\r\nTeamCTotal:?\r\n}\r\n\r\nThe termination must include the JSON format with all the player's tally.\r\n\r\nAn example of team leader's answer:\r\nI'm B0, a leader of Team B. I have 1 chocolate.\r\n{\r\nA0:3\r\nA1:5\r\nA2:2\r\nTeamATotal:10\r\nB0:1\r\nB1:?\r\nB2:?\r\nTeamBTotal:?\r\nC0:?\r\nC1:?\r\nC2:?\r\nTeamCTotal:?\r\n}\r\nNEXT: B1\r\n\n\nI'm A0, the leader of Team A. I have 4 chocolates.\n\n{\nA0:4\nA1:? \nA2:? \nTeamATotal:? \nB0:? \nB1:? \nB2:? \nTeamBTotal:? \nC0:? \nC1:? \nC2:? \nTeamCTotal:? }\n\nNEXT: A1\n\nI'm A2, a member of Team A. I have 2 chocolates.\n\n{\nA0:4\nA1:? \nA2:? \nTeamATotal:? \nB0:? \nB1:? \nB2:? \nTeamBTotal:? \nC0:? \nC1:? \nC2:? \nTeamCTotal:? }\n\nNEXT: A2<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
time=2025-01-11T19:18:53.499Z level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=273 prompt=753 used=7 remaining=746
[GIN] 2025/01/11 - 19:18:54 | 200 | 929.905932ms | 10.0.0.123 | POST "/api/chat"
time=2025-01-11T19:18:54.406Z level=DEBUG source=sched.go:407 msg="context for request finished"
time=2025-01-11T19:18:54.406Z level=DEBUG source=sched.go:339 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 duration=2562047h47m16.854775807s
time=2025-01-11T19:18:54.406Z level=DEBUG source=sched.go:357 msg="after processing request finished event" modelPath=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9 refCount=0
time=2025-01-11T19:18:54.432Z level=DEBUG source=sched.go:575 msg="evaluating already loaded" model=/home/vscode/.ollama/models/blobs/sha256-36cc41839cc170480c8e4fd176b404024e06df4bd7128bcbe199293283b844a9
time=2025-01-11T19:18:54.433Z level=DEBUG source=routes.go:1470 msg="chat request" images=0 prompt="<|start_header_id|>system<|end_header_id|>\n\nYou are a coordinator responsible for managing a group of agents. Your primary task is to choose which agent should be called next based on the user's instructions and the context of the conversation.\r\n\r\nYou have a list of agent names that you may choose from: [A0,A1].\r\n\r\nCritical Rule:\r\n\r\nIf the user's prompt explicitly specifies \"next\" and an agent name follows \"next\" and that name matches an agent in your list, you must select that agent, without exception. This rule applies even if other agents were mentioned earlier in the conversation.\r\n\r\nSecondary Rules:\r\n\r\nIf the user's suggestion does not match any names in the list, ignore the user's suggestion and select the most contextually appropriate agent from the list.\r\nIf the user does not provide any explicit instructions, choose the agent that best continues the flow of the conversation, ensuring logical progression.\r\n\r\nOnly return the agent's name and nothing else.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nI'm A2, a member of Team A. I have 2 chocolates.\n\n{\nA0:4\nA1:? \nA2:? \nTeamATotal:? \nB0:? \nB1:? \nB2:? \nTeamBTotal:? \nC0:? \nC1:? \nC2:? \nTeamCTotal:? }\n\nNEXT: B1\n\nYour team members have provided their chocolate count, so you can now calculate the total for Team A and proceed with the task.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
time=2025-01-11T19:18:54.434Z level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=854 prompt=297 used=7 remaining=290
time=2025-01-11T19:19:23.745Z level=DEBUG source=cache.go:231 msg="context limit hit - shifting" id=0 limit=4096 input=4096 keep=5 discard=2045
time=2025-01-11T19:19:40.621Z level=DEBUG source=cache.go:231 msg="context limit hit - shifting" id=0 limit=4096 input=4096 keep=5 discard=2045`
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.5, 0.5.4, 0.5.3,
|
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PR_kwDOJ0Z1Ps6Ia3QT
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Added golang template string manipulation functions, contains, hasPrefix, hasSuffix, split.
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Uncovered and corrected a bug where the Template.Execute block drops custom registered functions. Extended test suite to cover these new template functions.
These have been introduced to support DeepSeek R1 models, their training templates elide old `<think>` blocks as the conversation progresses.
Supporting this use case;
```
{{- else if eq $msg.Role "assistant" }}
{{- if $is_tool }}
<｜tool▁outputs▁end｜>{{ $msg.Content }}<｜end▁of▁sentence｜>
{{- $is_tool = false -}}
{{- else }}
{{- $content := $msg.Content -}}
{{- /* Only strip chain-of-thought if we detect `</think>` via `contains` */ -}}
{{- if (contains $content "</think>") }}
{{- $parts := split $content "</think>" -}}
{{- if gt (len $parts) 1 }}
{{- $content = index $parts 1 -}}
{{- end }}
{{- end }}
<｜Assistant｜>{{ $content }}<｜end▁of▁sentence｜>
{{- end }}
```
Duplicating this template (spacing mine):
```
{% set content = message['content'] %}
{% if '</think>' in content %}
{% set content = content.split('</think>')[-1] %}
{% endif %}
{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}
{%- endif %}
```
Addresses: #8502
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I_kwDOJ0Z1Ps56NRjo
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Showing modelfiles on ollama.com
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Similar to docker hub showing the Dockerfile that each docker image was created with, is there a repository of the Modelfiles that models in the Ollama library were created with? If not, what do you think of creating one?
I can run `ollama show <model> --modelfile` but that only works for models I have downloaded.
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I_kwDOJ0Z1Ps6Q0VTw
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Text Generation Documentation
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Dear all,
we ❤️ Ollama. Thank you for this great framework. I wa
There are many parameters for text generation Many of such parameters overlap with [llama.ccp](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md#common-options), while few of them do not, e.g.
1. num_thread
2. repeat_last_n
3. num_batch
4. f16_kv
I guess It would be great if you guys could write few sentences.
```
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"prompt": "Why is the sky blue?",
"stream": false,
"options": {
"num_keep": 5,
"seed": 42,
"num_predict": 100,
"top_k": 20,
"top_p": 0.9,
"tfs_z": 0.5,
"typical_p": 0.7,
"repeat_last_n": 33,
"temperature": 0.8,
"repeat_penalty": 1.2,
"presence_penalty": 1.5,
"frequency_penalty": 1.0,
"mirostat": 1,
"mirostat_tau": 0.8,
"mirostat_eta": 0.6,
"penalize_newline": true,
"stop": ["\n", "user:"],
"numa": false,
"num_ctx": 1024,
"num_batch": 2,
"num_gpu": 1,
"main_gpu": 0,
"low_vram": false,
"f16_kv": true,
"vocab_only": false,
"use_mmap": true,
"use_mlock": false,
"num_thread": 8
}
}'
```
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I_kwDOJ0Z1Ps6HItuO
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CUDA error while trying to run llama3-8B: out of memory
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### What is the issue?
Hello,
I am trying to run llama3-8B:instruct on 2 * GTX 970 (4GB, CUDA 5.2), no SLI. #1288 led me to believe it should be possible in terms of VRAM requirements (8GB total) and I also have enough RAM (16GB). However, each time I try to run the model the ollama service crashes due to out of memory error and no response is returned (tried with oterm, open-webui and `ollama run`).
This seems similar to #3765. I tried editing the modelfile as mentioned there without success.
The model runs successfully without GPU accleration. Other applications can use cuda just fine (checked with [these examples](https://github.com/grahamc/nixos-cuda-example)).
I installed ollama through the NixOS-unstable option.
Modelfile I tried:
```
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
FROM llama3:instruct
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
PARAMETER num_ctx 4196
PARAMETER num_gpu 42
PARAMETER num_keep 24
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
```
Tail of journalctl log: https://pastebin.com/yVH5Sgai
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.31
|
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I_kwDOJ0Z1Ps6XAmu4
| 6,856
|
link downloaded model to ollama
|
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Is there any API for linking an downloaded model to ollama from elsewhere. Suppose the model is downloaded before installing ollama, is there any way to link the model to ollama?
|
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PR_kwDOJ0Z1Ps5vL7PI
| 4,380
|
use tokenize/detokenize
|
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| 2024-05-12T18:45:53
| 2024-05-29T19:01:00
| 2024-05-29T19:01:00
|
CONTRIBUTOR
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remove server's infill and system prompt which are unused
|
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I_kwDOJ0Z1Ps52USND
| 1,053
|
Requesting support for basic auth or API key authentication
|
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| 2023-11-09T07:51:37
| 2024-12-23T01:01:03
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|
NONE
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It would be great to have some sort of authentication in front of the ollama api. Currently I´m using Nginx Proxy Manager to add a Access List to prevent unauthorized access but a standard way implemented into Ollama itself would be great for all developers that are integrating Ollama into there software.
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I_kwDOJ0Z1Ps6cCa_N
| 7,395
|
Why is the performance of x/llama3.2-vision for describing images poor?
|
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| 2024-10-28T09:45:08
| 2024-11-13T22:50:28
| 2024-11-13T22:10:03
|
NONE
|
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### What is the issue?
I am experiencing x/llama3.2-vision(11b) using ollama-0.4.0-rc5, but the performance is poor compared to the [official version](https://deepinfra.com/meta-llama/Llama-3.2-11B-Vision-Instruct). What causes this result?
**x/llama3.2-vision:**
**meta-llama:**
**image:**
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.4.0-rc5
|
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I_kwDOJ0Z1Ps6I2c6l
| 4,431
|
BUG: Custom System Prompt not loading
|
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| 2024-05-14T17:09:21
| 2024-05-14T17:19:05
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NONE
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### What is the issue?
# Bug Report
## Description
If I use any LLava models ([LLava-Phi-3](https://ollama.com/library/llava-phi3)), the custom System prompt ist working fine.
But if I upload an Picture at the start of the Conversation, the System Prompt is not Loading and if I continue the Conversation it still not have the custom System Prompt.
Only if I start the new Chat with only text as an Input, the System prompt is loading correctly.
**Bug Summary:**
Custom System Prompt not loading
**Steps to Reproduce:**
[Outline the steps to reproduce the bug. Be as detailed as possible.]
1. Load an LLava Model
2. Create a Custom System Prompt in Settings or in Modelfile
3. Upload a Picture and ask something about the Picture, and no Custom System Prompt loaded!!!
4. Continue conversation, but still there is no Custom System Prompt loaded!!!
## Environment
- **Open WebUI Version:** [0.1.124]
- **Ollama (if applicable):** [e.g., 0.1.37]
- **Operating System:** [Ubuntu 20.04]
- **Browser:** [Chrome]
### OS
Linux
### GPU
_No response_
### CPU
AMD
### Ollama version
0.1.37
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I_kwDOJ0Z1Ps6BygZm
| 3,032
|
Ollama errors with `msg="Failed to load dynamic library [...]/libext_server.so exception std::bad_alloc`
|
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| 2024-03-10T03:13:10
| 2024-05-29T19:28:16
| 2024-05-29T19:28:15
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Error: Unable to load dynamic library: Unable to load dynamic server library: �Ҳ���ָ����ģ�顣
|
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I_kwDOJ0Z1Ps6GapOX
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|
Can this library utilize Intel's UHD graphics cards?
|
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| 2024-04-21T15:24:36
| 2024-08-28T02:52:49
| 2024-08-09T18:37:39
|
NONE
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My question is that most of the models are supported/backed by cuda to run on NVIDIA's cards, but what about Intel?
Many machines (most probably consumer grade intel laptops) still have intel gpus (UHD cards) so will ollama add a support for them?
Or intel gpus are just too hard to be used?
Cause to run those big models like llama 2 7b is still slow on cpu, so utilizing uhd cards might make them a little faster than the cpu and might just reduce the latency
I mean like libraries like open vino can even use intel uhd cards
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PR_kwDOJ0Z1Ps5zF8st
| 5,190
|
Remove Quotes from Parameters in Ollama Show
|
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| 2024-06-20T16:08:03
| 2024-08-11T22:19:40
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|
CONTRIBUTOR
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Resolves #5183
Before:
<img width="492" alt="Screenshot 2024-06-20 at 9 07 56 AM" src="https://github.com/ollama/ollama/assets/65097070/0c6e8f89-854b-4069-a387-dc191766ee70">
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I_kwDOJ0Z1Ps6BDBng
| 2,887
|
check default generator for windows
|
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| 2024-03-03T00:49:02
| 2024-03-12T01:37:22
| 2024-03-12T01:37:22
|
NONE
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Generator Ninja does not support platform specification but platform -A x64 was specified. when trying to build ollama.
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I_kwDOJ0Z1Ps6Ljnku
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|
ollama download时下载的server地址是开源的吗？在国内感觉不好拉，想弄个类似的
|
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| 2024-06-08T01:54:45
| 2024-06-09T17:39:05
| 2024-06-09T17:39:05
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NONE
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PR_kwDOJ0Z1Ps585s7_
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cli: pull models without starting server, fixes #3369
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| 2024-09-27T10:12:50
| 2024-10-21T13:57:32
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NONE
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In this change, I've added a new `-local` flag to the `ollama pull` command.
This enables models to be downloaded to the server directories without first starting the server, as per requests at #3369
This change makes it easier to package models for distribution and deployment.
To make the change, I made `registryOptions` into a public type. This shouldn't be a problem, because the functions that accept `registryOptions` were already public.
The CLI already directly used parts of the server package, so I didn't see any potential problems with the change there.
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`CUDA error 999: unknown error`
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MEMBER
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```
ollama serve
2024/01/10 12:36:43 images.go:808: total blobs: 9
2024/01/10 12:36:43 images.go:815: total unused blobs removed: 0
2024/01/10 12:36:43 routes.go:930: Listening on 127.0.0.1:11434 (version 0.1.19)
2024/01/10 12:36:43 shim_ext_server.go:142: Dynamic LLM variants [cuda rocm]
2024/01/10 12:36:43 gpu.go:35: Detecting GPU type
2024/01/10 12:36:43 gpu.go:54: Nvidia GPU detected
2024/01/10 12:36:43 gpu.go:84: CUDA Compute Capability detected: 7.5
[GIN] 2024/01/10 - 12:36:55 | 200 | 41.734µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/01/10 - 12:36:55 | 200 | 624.916µs | 127.0.0.1 | POST "/api/show"
[GIN] 2024/01/10 - 12:36:55 | 200 | 359.397µs | 127.0.0.1 | POST "/api/show"
size 4109853248
filetype Q4_0
architecture llama
type 7B
name gguf
embd 4096
head 32
head_kv 8
gqa 4
2024/01/10 12:36:57 gpu.go:84: CUDA Compute Capability detected: 7.5
2024/01/10 12:36:57 llm.go:70: system memory bytes: 3681740391
2024/01/10 12:36:57 llm.go:71: required model bytes: 4109853248
2024/01/10 12:36:57 llm.go:72: required kv bytes: 268435456
2024/01/10 12:36:57 llm.go:73: required alloc bytes: 178956970
2024/01/10 12:36:57 llm.go:74: required total bytes: 4557245674
2024/01/10 12:36:57 gpu.go:84: CUDA Compute Capability detected: 7.5
2024/01/10 12:36:57 llm.go:114: splitting 3502783421 of available memory bytes into layers
2024/01/10 12:36:57 llm.go:116: bytes per layer: 136821522
2024/01/10 12:36:57 llm.go:118: total required with split: 3599495020
2024/01/10 12:36:57 shim_ext_server_linux.go:24: Updating PATH to /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/tmp/ollama22470349/cuda
Lazy loading /tmp/ollama22470349/cuda/libext_server.so library
2024/01/10 12:36:57 shim_ext_server.go:92: Loading Dynamic Shim llm server: /tmp/ollama22470349/cuda/libext_server.so
2024/01/10 12:36:57 ext_server_common.go:136: Initializing internal llama server
...
CUDA error 999 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:495: unknown error
current device: -1876424368
GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:495: !"CUDA error"
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Vorgang nicht zulässig.
No stack.
The program is not being run.
SIGABRT: abort
PC=0x7fc40c29999b m=13 sigcode=18446744073709551610
signal arrived during cgo execution
```
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CORS configuration error blocking authorization in Ollama's OpenAI compatible endpoint
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| 2024-04-28T11:09:27
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### What is the issue?
Related: #3571
Ollama's implementation of an OpenAI-compatible endpoint faces a CORS (Cross-Origin Resource Sharing) configuration issue. The preflight OPTIONS response currently includes the headers
```http
HTTP/1.1 204 No Content
Access-Control-Allow-Headers: Origin,Content-Length,Content-Type
Access-Control-Allow-Methods: GET,POST,PUT,PATCH,DELETE,HEAD,OPTIONS
Access-Control-Allow-Origin: *
Access-Control-Max-Age: 43200
Date: Sun, 28 Apr 2024 10:41:22 GMT
````
However, this setup is insufficient for compatibility with OpenAI's authentication requirements, which utilize the `Authorization` header.
When attempting POST requests, browsers enforce security policies that block the request due to the absence of the Authorization header in the Access-Control-Allow-Headers. This results in the error: `missing token 'authorization' in CORS header 'Access-Control-Allow-Headers' from CORS preflight channel.`
To resolve this issue, the CORS configuration in the Gin middleware should be updated to include Authorization in the Access-Control-Allow-Headers:
https://github.com/ollama/ollama/blob/114c932a8e872846fc714353c65d041feb886027/server/routes.go#L972-L993
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.32
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I_kwDOJ0Z1Ps56ecG2
| 1,689
|
Documentation suggestion: add small paragraph about quantization in README.md
|
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| 2023-12-23T14:12:50
| 2023-12-27T19:34:55
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### Enhancement / Documentation
I suggest to add a small paragraph explaining the effect and why someone should or should not download a quantized version of a model.
I feel like it's a fairly technical subject but it can impact the perceived performance of any kind of user.
I would add it possibly under the Model Library section.
I'd be open to write it.
(Ollama is awesome thanks for creating it!)
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I_kwDOJ0Z1Ps6oEzN1
| 8,685
|
Request to change the file location and model path, and also gui
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| 2025-01-30T03:38:48
| 2025-01-30T03:56:41
| 2025-01-30T03:56:40
|
NONE
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This software is automatically install on the default path in my C disk. And all model is download to specific path in C. However, there is limited space in my disk C, can we change the software install location and the model download path? And it is possible to have a gui of this softeware? Hope this can be better one day.
|
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PR_kwDOJ0Z1Ps6DhtKD
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|
server: add warning message for deprecated context field in /api/generate
|
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| 2024-11-29T04:34:56
| 2024-11-30T22:05:52
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The `context` parameter in `/api/generate` has been longtime replaced by functionality in the `/api/chat` endpoint. This PR adds a deprecation warning in the logs when used.
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I_kwDOJ0Z1Ps6MWm3h
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request for one useful vison model
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| 2024-06-15T10:44:46
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as there is no one useful...
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I_kwDOJ0Z1Ps6J3UJK
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How i can understand, that model is not sure with answers ? (Get Entropy/Probas)
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| 2024-05-23T13:47:42
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i'm aware about temperature, top k/p parameters which i can configure using Ollama as serv. function for models inferencing,
But how do i can understand, that model is not sure about particular answer by my promts? While model inferences with Ollama.
I need to understand this to perform post-processing of 'low quality' answers.
Is it any possible ways to understand this ? Maybe by some optional parametr ? or get probas of tokens and calculate entropy.
Thanks!
|
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PR_kwDOJ0Z1Ps5dFXnw
| 830
|
Add basic JSON Schema support to the API (converts to GBNF grammar)
|
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| 2023-10-18T02:43:45
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NONE
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This PR is based on PR https://github.com/jmorganca/ollama/pull/565 which adds GBNF grammar support.
JSON Schema is now available as an option (`schema`) in `/api/generate` API. If provided, it will be converted into GBNF grammar and added into the predict request for llama.cpp. Also, make sure to set the `format` to `json` in order for this to function.
Here's a demo:
#### Request
```json
{
"model": "llama2",
"prompt": "Generate a mock user.",
"format": "json",
"options": {
"schema": "{\"type\":\"object\",\"properties\":{\"firstname\":{\"type\":\"string\"},\"lastname\":{\"type\":\"string\"},\"age\":{\"type\":\"integer\"},\"address\":{\"type\":\"string\"}},\"email\":{\"type\":\"string\"},\"isMember\":{\"type\":\"boolean\"}}"
}
}
```
#### Response (extracted)
```json
{"firstname": "John", "lastname": "Doe", "age": 32, "address": "123 Main St, Anytown USA 12345"}
```
|
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PR_kwDOJ0Z1Ps59-T_q
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llama: Decouple patching script from submodule
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| 2024-10-08T16:20:58
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COLLABORATOR
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The current patching script relies on the existing llama.cpp submodule, which we plan to remove in favor of the vendored code soon, so this updates the patching flow to no longer rely on that. It still uses the git commit from the submodule to ensure we don't drift while the submodule is still present. In the commit that removes the submodule in the future, this would be switched to a manifest file recording the git hash we're sync'd to.
This adjusts our patches to be mailbox format patches which can be round-tripped through the system deterministically. In doing so, this should improve developer experience when updating the llama.cpp commit, or working on fixes/features that impact the native code.
Fixes #2534
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PR_kwDOJ0Z1Ps5mhoPK
| 2,435
|
Update domain name references in docs and install script
|
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PR_kwDOJ0Z1Ps5f9QMJ
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|
Remove redundant filename parameter in api request for `ollama create`
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I_kwDOJ0Z1Ps6BQ9U8
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Will Ollama support FuncMaster-v0.1-Mistral-7B-Instruct-GGUF?
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Will Ollama support FuncMaster-v0.1-Mistral-7B-Instruct-GGUF?
https://huggingface.co/allyson-ai/FuncMaster-v0.1-Mistral-7B-Instruct-GGUF
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PR_kwDOJ0Z1Ps5fEqzE
| 1,062
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Added gptel to list of integrations
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6CluWw
| 3,203
|
Allow auth between the Ollama server/client
|
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### What are you trying to do?
I'm trying to secure the connection between ollama client and server.
HTTPS works fine:
Basic auth does not work with ollama client:
### How should we solve this?
Allow to set basic auth in OLLAMA_HOST or other variables for the client.
### What is the impact of not solving this?
The only way to secure the connection between ollama client and ollama server is with IP filtering. Basic auth would be a bit better.
Note that for any other client (such as python requests), this is not an issue since those packages can manage basic auth (or other auth mechanisms for that matter).
### Anything else?
Allowing the server to use TLS would be nice too. Traffic is still cleartext between the reverse proxy and ollama server.
Displaying a better error message with http status code would be nice.
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I_kwDOJ0Z1Ps6G3eOq
| 3,902
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Model Pooling and Instance Management
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This builds on from model concurrency (#3418), and the `keep_alive` option.
First of all it would be great to be able to load multiple instances of the _same_ model, if I'm not mistaken the model concurrency only works for different models.
Of course loading multiple instances would require a new way of managing them since each model name could refer to many instances - therefore instances should be identifiable.
In addition to this having more methods to be able to get the state of the system is important, here are some suggestions of what a user might want to do:
- Get the ID of a model instance (when generating or preloading).
- List the models/instances currently loaded into memory.
- Get information about each instance (keep_alive, expiry, queue length, RAM/VRAM usage etc.)
- Unload specific instances from memory.
- Queue requests to any instance of a model.
- Queue requests to a particular instance of a model.
My specific use case is that I have different models for different applications (multi-modal LLM, smarter LLM, faster LLM, etc.) and I would like to have (if possible - dynamic) pools of these models so each model has several instances and can manage multiple concurrent requests on the same machine similar to a thread pool.
References:
- https://github.com/ollama/ollama/pull/3418
- https://github.com/ollama/ollama/issues/2431
- https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-pre-load-a-model-to-get-faster-response-times
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GGML_ASSERT(i01 >= 0 && i01 < ne01) failed and SIGSEGV had occoured
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### What is the issue?
``` text
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.468+08:00 level=INFO source=.:0 msg="Server listening on 127.0.0.1:45295"
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: loaded meta data with 23 key-value pairs and 197 tensors from /data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa (version GGUF V3 (latest))
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 0: general.architecture str = bert
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 1: general.name str = Dmeta-embedding-zh
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 2: bert.block_count u32 = 12
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 3: bert.context_length u32 = 1024
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 4: bert.embedding_length u32 = 768
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 5: bert.feed_forward_length u32 = 3072
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 6: bert.attention.head_count u32 = 12
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 7: bert.attention.layer_norm_epsilon f32 = 0.000000
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 8: general.file_type u32 = 1
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 9: bert.attention.causal bool = false
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 10: bert.pooling_type u32 = 2
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 12: tokenizer.ggml.model str = bert
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 13: tokenizer.ggml.pre str = Dmeta-embedding-zh
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 16: tokenizer.ggml.unknown_token_id u32 = 100
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 17: tokenizer.ggml.seperator_token_id u32 = 102
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 19: tokenizer.ggml.cls_token_id u32 = 101
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 20: tokenizer.ggml.mask_token_id u32 = 103
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 21: tokenizer.ggml.bos_token_id u32 = 0
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - kv 22: tokenizer.ggml.eos_token_id u32 = 2
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - type f32: 123 tensors
Dec 18 02:31:29 ksy ollama[2877902]: llama_model_loader: - type f16: 74 tensors
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_vocab: special tokens cache size = 5
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_vocab: token to piece cache size = 0.0769 MB
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: format = GGUF V3 (latest)
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: arch = bert
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: vocab type = WPM
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_vocab = 21128
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_merges = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: vocab_only = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_ctx_train = 1024
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_embd = 768
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_layer = 12
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_head = 12
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_head_kv = 12
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_rot = 64
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_swa = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_embd_head_k = 64
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_embd_head_v = 64
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_gqa = 1
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_embd_k_gqa = 768
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_embd_v_gqa = 768
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: f_norm_eps = 1.0e-12
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: f_norm_rms_eps = 0.0e+00
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: f_logit_scale = 0.0e+00
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_ff = 3072
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_expert = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_expert_used = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: causal attn = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: pooling type = 2
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: rope type = 2
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: rope scaling = linear
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: freq_base_train = 10000.0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: freq_scale_train = 1
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: n_ctx_orig_yarn = 1024
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: rope_finetuned = unknown
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: ssm_d_conv = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: ssm_d_inner = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: ssm_d_state = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: ssm_dt_rank = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: ssm_dt_b_c_rms = 0
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: model type = 109M
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: model ftype = F16
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: model params = 102.07 M
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: model size = 194.92 MiB (16.02 BPW)
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: general.name = Dmeta-embedding-zh
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: BOS token = 0 '[PAD]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: EOS token = 2 '[unused2]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: UNK token = 100 '[UNK]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: SEP token = 102 '[SEP]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: PAD token = 0 '[PAD]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: CLS token = 101 '[CLS]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: MASK token = 103 '[MASK]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: LF token = 0 '[PAD]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: EOG token = 2 '[unused2]'
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_print_meta: max token length = 48
Dec 18 02:31:29 ksy ollama[2877902]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
Dec 18 02:31:29 ksy ollama[2877902]: ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
Dec 18 02:31:29 ksy ollama[2877902]: ggml_cuda_init: found 1 CUDA devices:
Dec 18 02:31:29 ksy ollama[2877902]: Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.550+08:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.309635673 model=/data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.550+08:00 level=DEBUG source=gpu.go:398 msg="updating system memory data" before.total="31.2 GiB" before.free="22.2 GiB" before.free_swap="7.1 GiB" now.total="31.2 GiB" now.free="22.0 GiB" now.free_swap="7.1 GiB"
Dec 18 02:31:29 ksy ollama[2877902]: initializing /usr/lib/x86_64-linux-gnu/libcuda.so.535.161.07
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuInit - 0x7f2482655430
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDriverGetVersion - 0x7f2482655450
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDeviceGetCount - 0x7f2482655490
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDeviceGet - 0x7f2482655470
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDeviceGetAttribute - 0x7f2482655570
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDeviceGetUuid - 0x7f24826554d0
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuDeviceGetName - 0x7f24826554b0
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuCtxCreate_v3 - 0x7f248265d130
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuMemGetInfo_v2 - 0x7f2482668600
Dec 18 02:31:29 ksy ollama[2877902]: dlsym: cuCtxDestroy - 0x7f24826b7600
Dec 18 02:31:29 ksy ollama[2877902]: calling cuInit
Dec 18 02:31:29 ksy ollama[2877902]: calling cuDriverGetVersion
Dec 18 02:31:29 ksy ollama[2877902]: raw version 0x2ef4
Dec 18 02:31:29 ksy ollama[2877902]: CUDA driver version: 12.2
Dec 18 02:31:29 ksy ollama[2877902]: calling cuDeviceGetCount
Dec 18 02:31:29 ksy ollama[2877902]: device count 1
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: ggml ctx size = 0.16 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: offloading 12 repeating layers to GPU
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: offloading non-repeating layers to GPU
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: offloaded 13/13 layers to GPU
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: CPU buffer size = 32.46 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llm_load_tensors: CUDA0 buffer size = 162.46 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: n_ctx = 2048
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: n_batch = 512
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: n_ubatch = 512
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: flash_attn = 0
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: freq_base = 10000.0
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: freq_scale = 1
Dec 18 02:31:29 ksy ollama[2877902]: llama_kv_cache_init: CUDA0 KV buffer size = 72.00 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: KV self size = 72.00 MiB, K (f16): 36.00 MiB, V (f16): 36.00 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: CPU output buffer size = 0.00 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: CUDA0 compute buffer size = 19.00 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: CUDA_Host compute buffer size = 4.01 MiB
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: graph nodes = 429
Dec 18 02:31:29 ksy ollama[2877902]: llama_new_context_with_model: graph splits = 2
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.598+08:00 level=DEBUG source=gpu.go:448 msg="updating cuda memory data" gpu=GPU-f4ac237a-4252-ac0e-b006-d7ae4f03cbf9 name="NVIDIA GeForce RTX 3090" overhead="0 B" before.total="23.7 GiB" before.free="23.4 GiB" now.total="23.7 GiB" now.free="23.0 GiB" now.used="681.8 MiB"
Dec 18 02:31:29 ksy ollama[2877902]: releasing cuda driver library
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.598+08:00 level=DEBUG source=sched.go:659 msg="gpu VRAM free memory converged after 5.36 seconds" model=/data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.700+08:00 level=INFO source=server.go:615 msg="llama runner started in 0.25 seconds"
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.700+08:00 level=DEBUG source=sched.go:462 msg="finished setting up runner" model=/data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.701+08:00 level=DEBUG source=runner.go:752 msg="embedding request" content="I\r\n目录\r\n第 1 章 概述.......................................................................................................................................................... 1\r\n1.1 适用范围.................................................................................................................................................. 1\r\n1.2 遵照标准.................................................................................................................................................. 1\r\n1.3 数据库说明.............................................................................................................................................. 1\r\n1.4 数据同步频率......................................................................................................................................... 2\r\n1.5 数据校验规则说明................................................................................................................................. 3\r\n1.6 数据表常见数据类型说明..................................................................................................................... 3\r\n第 2 章 数据采集内容 ......................................................................................................................................... 5\r\n2.1 实时采集数据表说明............................................................................................................................. 5\r\n2.1.1 患者基本信息表 emr_patient_info ............................................................................................. 5\r\n2.1.2 诊疗活动信息表 emr_activity_info ............................................................................................. 9\r\n2.1.3 传染病报告卡 emr_inf_report ................................................................................................... 14\r\n2.2 常规监测数据表说明........................................................................................................................... 36\r\n2.2.1 门（急）诊病历 emr_outpatient_record ................................................................................. 36\r\n2.2.2 门（急）诊留 观记录 emr_outpatient_obs ............................................................................. 42\r\n2.2.3 入院记录 emr_admission_info .................................................................................................."
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.707+08:00 level=WARN source=runner.go:129 msg="truncating input prompt" limit=2048 prompt=2131 keep=1 new=2048
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.707+08:00 level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=0 prompt=2048 used=0 remaining=2048
Dec 18 02:31:29 ksy ollama[2877902]: ggml.c:13343: GGML_ASSERT(i01 >= 0 && i01 < ne01) failed
Dec 18 02:31:29 ksy ollama[2877902]: SIGSEGV: segmentation violation
Dec 18 02:31:29 ksy ollama[2877902]: PC=0x7f7cc1f06f77 m=0 sigcode=1 addr=0x204a03fd8
Dec 18 02:31:29 ksy ollama[2877902]: signal arrived during cgo execution
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 7 gp=0xc000184000 m=0 mp=0x556d1a36cf20 [syscall]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.cgocall(0x556d19e50a90, 0xc000080b48)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/cgocall.go:157 +0x4b fp=0xc000080b20 sp=0xc000080ae8 pc=0x556d19bd18ab
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama._Cfunc_llama_decode(0x7f7c48006490, {0x200, 0x7f7c4804a260, 0x0, 0x0, 0x7f7c4804aa70, 0x7f7c4804b280, 0x7f7c4804ba90, 0x7f7c487873d0, 0x0, ...})
Dec 18 02:31:29 ksy ollama[2877902]: _cgo_gotypes.go:548 +0x52 fp=0xc000080b48 sp=0xc000080b20 pc=0x556d19ccee32
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama.(*Context).Decode.func1(0x556d19e4c4eb?, 0x7f7c48006490?)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/llama.go:189 +0xd8 fp=0xc000080c68 sp=0xc000080b48 pc=0x556d19cd1518
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama.(*Context).Decode(0xc000080d58?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/llama.go:189 +0x13 fp=0xc000080cb0 sp=0xc000080c68 pc=0x556d19cd13b3
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).processBatch(0xc0000ce120, 0xc00011a000, 0xc000080f10)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:434 +0x24d fp=0xc000080ed0 sp=0xc000080cb0 pc=0x556d19e4b1ad
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).run(0xc0000ce120, {0x556d1a19d9a0, 0xc0000a40a0})
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:342 +0x1e5 fp=0xc000080fb8 sp=0xc000080ed0 pc=0x556d19e4ac25
Dec 18 02:31:29 ksy ollama[2877902]: main.main.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:980 +0x28 fp=0xc000080fe0 sp=0xc000080fb8 pc=0x556d19e4fa88
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc000080fe8 sp=0xc000080fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by main.main in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:980 +0xd3e
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 1 gp=0xc0000061c0 m=nil [IO wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x1?, 0xc0000298e0?, 0xd4?, 0x82?, 0xc0000298c0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc000029860 sp=0xc000029840 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.netpollblock(0x10?, 0x19bd1006?, 0x6d?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:573 +0xf7 fp=0xc000029898 sp=0xc000029860 pc=0x556d19c00737
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.runtime_pollWait(0x7f7cba9c8020, 0x72)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:345 +0x85 fp=0xc0000298b8 sp=0xc000029898 pc=0x556d19c34f85
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).wait(0x3?, 0x7f7cc14c1368?, 0x0)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0000298e0 sp=0xc0000298b8 pc=0x556d19c84ea7
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).waitRead(...)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:89
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*FD).Accept(0xc0000fe080)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_unix.go:611 +0x2ac fp=0xc000029988 sp=0xc0000298e0 pc=0x556d19c8636c
Dec 18 02:31:29 ksy ollama[2877902]: net.(*netFD).accept(0xc0000fe080)
Dec 18 02:31:29 ksy ollama[2877902]: net/fd_unix.go:172 +0x29 fp=0xc000029a40 sp=0xc000029988 pc=0x556d19cf4fa9
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPListener).accept(0xc00007c1c0)
Dec 18 02:31:29 ksy ollama[2877902]: net/tcpsock_posix.go:159 +0x1e fp=0xc000029a68 sp=0xc000029a40 pc=0x556d19d05cde
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPListener).Accept(0xc00007c1c0)
Dec 18 02:31:29 ksy ollama[2877902]: net/tcpsock.go:327 +0x30 fp=0xc000029a98 sp=0xc000029a68 pc=0x556d19d05030
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*onceCloseListener).Accept(0xc000122000?)
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x24 fp=0xc000029ab0 sp=0xc000029a98 pc=0x556d19e2c244
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*Server).Serve(0xc0000181e0, {0x556d1a19d360, 0xc00007c1c0})
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3260 +0x33e fp=0xc000029be0 sp=0xc000029ab0 pc=0x556d19e2305e
Dec 18 02:31:29 ksy ollama[2877902]: main.main()
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:1000 +0x10cd fp=0xc000029f50 sp=0xc000029be0 pc=0x556d19e4f80d
Dec 18 02:31:29 ksy ollama[2877902]: runtime.main()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:271 +0x29d fp=0xc000029fe0 sp=0xc000029f50 pc=0x556d19c080bd
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc000029fe8 sp=0xc000029fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 2 gp=0xc000006c40 m=nil [force gc (idle)]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006cfa8 sp=0xc00006cf88 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.forcegchelper()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:326 +0xb8 fp=0xc00006cfe0 sp=0xc00006cfa8 pc=0x556d19c08378
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006cfe8 sp=0xc00006cfe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.init.6 in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:314 +0x1a
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 3 gp=0xc000007180 m=nil [GC sweep wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006d780 sp=0xc00006d760 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.bgsweep(0xc00007e000)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcsweep.go:278 +0x94 fp=0xc00006d7c8 sp=0xc00006d780 pc=0x556d19bf3034
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gcenable.gowrap1()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:203 +0x25 fp=0xc00006d7e0 sp=0xc00006d7c8 pc=0x556d19be7b65
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006d7e8 sp=0xc00006d7e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.gcenable in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:203 +0x66
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 4 gp=0xc000007340 m=nil [GC scavenge wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0xc00007e000?, 0x556d1a09a4f0?, 0x1?, 0x0?, 0xc000007340?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006df78 sp=0xc00006df58 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.(*scavengerState).park(0x556d1a36c560)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcscavenge.go:425 +0x49 fp=0xc00006dfa8 sp=0xc00006df78 pc=0x556d19bf0a29
Dec 18 02:31:29 ksy ollama[2877902]: runtime.bgscavenge(0xc00007e000)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcscavenge.go:653 +0x3c fp=0xc00006dfc8 sp=0xc00006dfa8 pc=0x556d19bf0fbc
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gcenable.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:204 +0x25 fp=0xc00006dfe0 sp=0xc00006dfc8 pc=0x556d19be7b05
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006dfe8 sp=0xc00006dfe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.gcenable in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:204 +0xa5
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 5 gp=0xc000007c00 m=nil [finalizer wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0xc00006c648?, 0x556d19bdb465?, 0xa8?, 0x1?, 0xc0000061c0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006c620 sp=0xc00006c600 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.runfinq()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mfinal.go:194 +0x107 fp=0xc00006c7e0 sp=0xc00006c620 pc=0x556d19be6ba7
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006c7e8 sp=0xc00006c7e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.createfing in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mfinal.go:164 +0x3d
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 18 gp=0xc000007dc0 m=nil [chan receive]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x556d19c382d4?, 0xc0000f3890?, 0x65?, 0xa6?, 0xc0000f3878?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc0000f3858 sp=0xc0000f3838 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.chanrecv(0xc0002000c0, 0xc0000f3a08, 0x1)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/chan.go:583 +0x3bf fp=0xc0000f38d0 sp=0xc0000f3858 pc=0x556d19bd3ebf
Dec 18 02:31:29 ksy ollama[2877902]: runtime.chanrecv1(0xc000112030?, 0xc00029e000?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/chan.go:442 +0x12 fp=0xc0000f38f8 sp=0xc0000f38d0 pc=0x556d19bd3af2
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).embeddings(0xc0000ce120, {0x556d1a19d510, 0xc000218000}, 0xc000204000)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:793 +0x746 fp=0xc0000f3ab8 sp=0xc0000f38f8 pc=0x556d19e4dc66
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).embeddings-fm({0x556d1a19d510?, 0xc000218000?}, 0x556d19e2738d?)
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x36 fp=0xc0000f3ae8 sp=0xc0000f3ab8 pc=0x556d19e50236
Dec 18 02:31:29 ksy ollama[2877902]: net/http.HandlerFunc.ServeHTTP(0xc0000b4d00?, {0x556d1a19d510?, 0xc000218000?}, 0x10?)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2171 +0x29 fp=0xc0000f3b10 sp=0xc0000f3ae8 pc=0x556d19e1fe29
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*ServeMux).ServeHTTP(0x556d19bdb465?, {0x556d1a19d510, 0xc000218000}, 0xc000204000)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2688 +0x1ad fp=0xc0000f3b60 sp=0xc0000f3b10 pc=0x556d19e21cad
Dec 18 02:31:29 ksy ollama[2877902]: net/http.serverHandler.ServeHTTP({0x556d1a19c860?}, {0x556d1a19d510?, 0xc000218000?}, 0x6?)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3142 +0x8e fp=0xc0000f3b90 sp=0xc0000f3b60 pc=0x556d19e22cce
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*conn).serve(0xc000122000, {0x556d1a19d968, 0xc0000b2db0})
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2044 +0x5e8 fp=0xc0000f3fb8 sp=0xc0000f3b90 pc=0x556d19e1ea68
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*Server).Serve.gowrap3()
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3290 +0x28 fp=0xc0000f3fe0 sp=0xc0000f3fb8 pc=0x556d19e23448
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc0000f3fe8 sp=0xc0000f3fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by net/http.(*Server).Serve in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3290 +0x4b4
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 34 gp=0xc000224000 m=nil [IO wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0xb?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc0000685a8 sp=0xc000068588 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.netpollblock(0x556d19c6ea38?, 0x19bd1006?, 0x6d?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:573 +0xf7 fp=0xc0000685e0 sp=0xc0000685a8 pc=0x556d19c00737
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.runtime_pollWait(0x7f7cba9c7f28, 0x72)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:345 +0x85 fp=0xc000068600 sp=0xc0000685e0 pc=0x556d19c34f85
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).wait(0xc000120000?, 0xc0000b2e21?, 0x0)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc000068628 sp=0xc000068600 pc=0x556d19c84ea7
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).waitRead(...)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:89
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*FD).Read(0xc000120000, {0xc0000b2e21, 0x1, 0x1})
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_unix.go:164 +0x27a fp=0xc0000686c0 sp=0xc000068628 pc=0x556d19c859fa
Dec 18 02:31:29 ksy ollama[2877902]: net.(*netFD).Read(0xc000120000, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: net/fd_posix.go:55 +0x25 fp=0xc000068708 sp=0xc0000686c0 pc=0x556d19cf3ea5
Dec 18 02:31:29 ksy ollama[2877902]: net.(*conn).Read(0xc000114008, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: net/net.go:185 +0x45 fp=0xc000068750 sp=0xc000068708 pc=0x556d19cfe165
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPConn).Read(0x0?, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x25 fp=0xc000068780 sp=0xc000068750 pc=0x556d19d09b45
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*connReader).backgroundRead(0xc0000b2e10)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:681 +0x37 fp=0xc0000687c8 sp=0xc000068780 pc=0x556d19e189d7
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*connReader).startBackgroundRead.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:677 +0x25 fp=0xc0000687e0 sp=0xc0000687c8 pc=0x556d19e18905
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc0000687e8 sp=0xc0000687e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by net/http.(*connReader).startBackgroundRead in goroutine 18
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:677 +0xba
Dec 18 02:31:29 ksy ollama[2877902]: rax 0x204a03fd8
Dec 18 02:31:29 ksy ollama[2877902]: rbx 0x7f7c485e5370
Dec 18 02:31:29 ksy ollama[2877902]: rcx 0xff6
Dec 18 02:31:29 ksy ollama[2877902]: rdx 0x7f7c483fe430
Dec 18 02:31:29 ksy ollama[2877902]: rdi 0x7f7c483fe440
Dec 18 02:31:29 ksy ollama[2877902]: rsi 0x0
Dec 18 02:31:29 ksy ollama[2877902]: rbp 0x7ffd316fc660
Dec 18 02:31:29 ksy ollama[2877902]: rsp 0x7ffd316fc640
Dec 18 02:31:29 ksy ollama[2877902]: r8 0x4
Dec 18 02:31:29 ksy ollama[2877902]: r9 0x0
Dec 18 02:31:29 ksy ollama[2877902]: r10 0x4
Dec 18 02:31:29 ksy ollama[2877902]: r11 0x8
Dec 18 02:31:29 ksy ollama[2877902]: r12 0x556d1ae4f830
Dec 18 02:31:29 ksy ollama[2877902]: r13 0x7f7c483fe440
Dec 18 02:31:29 ksy ollama[2877902]: r14 0x0
Dec 18 02:31:29 ksy ollama[2877902]: r15 0x7f7d0d1557e0
Dec 18 02:31:29 ksy ollama[2877902]: rip 0x7f7cc1f06f77
Dec 18 02:31:29 ksy ollama[2877902]: rflags 0x10297
Dec 18 02:31:29 ksy ollama[2877902]: cs 0x33
Dec 18 02:31:29 ksy ollama[2877902]: fs 0x0
Dec 18 02:31:29 ksy ollama[2877902]: gs 0x0
Dec 18 02:31:29 ksy ollama[2877902]: SIGABRT: abort
Dec 18 02:31:29 ksy ollama[2877902]: PC=0x7f7c9c6419fc m=0 sigcode=18446744073709551610
Dec 18 02:31:29 ksy ollama[2877902]: signal arrived during cgo execution
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 7 gp=0xc000184000 m=0 mp=0x556d1a36cf20 [syscall]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.cgocall(0x556d19e50a90, 0xc000080b48)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/cgocall.go:157 +0x4b fp=0xc000080b20 sp=0xc000080ae8 pc=0x556d19bd18ab
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama._Cfunc_llama_decode(0x7f7c48006490, {0x200, 0x7f7c4804a260, 0x0, 0x0, 0x7f7c4804aa70, 0x7f7c4804b280, 0x7f7c4804ba90, 0x7f7c487873d0, 0x0, ...})
Dec 18 02:31:29 ksy ollama[2877902]: _cgo_gotypes.go:548 +0x52 fp=0xc000080b48 sp=0xc000080b20 pc=0x556d19ccee32
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama.(*Context).Decode.func1(0x556d19e4c4eb?, 0x7f7c48006490?)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/llama.go:189 +0xd8 fp=0xc000080c68 sp=0xc000080b48 pc=0x556d19cd1518
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama.(*Context).Decode(0xc000080d58?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/llama.go:189 +0x13 fp=0xc000080cb0 sp=0xc000080c68 pc=0x556d19cd13b3
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).processBatch(0xc0000ce120, 0xc00011a000, 0xc000080f10)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:434 +0x24d fp=0xc000080ed0 sp=0xc000080cb0 pc=0x556d19e4b1ad
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).run(0xc0000ce120, {0x556d1a19d9a0, 0xc0000a40a0})
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:342 +0x1e5 fp=0xc000080fb8 sp=0xc000080ed0 pc=0x556d19e4ac25
Dec 18 02:31:29 ksy ollama[2877902]: main.main.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:980 +0x28 fp=0xc000080fe0 sp=0xc000080fb8 pc=0x556d19e4fa88
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc000080fe8 sp=0xc000080fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by main.main in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:980 +0xd3e
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 1 gp=0xc0000061c0 m=nil [IO wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x1?, 0xc0000298e0?, 0xd4?, 0x82?, 0xc0000298c0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc000029860 sp=0xc000029840 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.netpollblock(0x10?, 0x19bd1006?, 0x6d?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:573 +0xf7 fp=0xc000029898 sp=0xc000029860 pc=0x556d19c00737
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.runtime_pollWait(0x7f7cba9c8020, 0x72)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:345 +0x85 fp=0xc0000298b8 sp=0xc000029898 pc=0x556d19c34f85
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).wait(0x3?, 0x7f7cc14c1368?, 0x0)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0000298e0 sp=0xc0000298b8 pc=0x556d19c84ea7
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).waitRead(...)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:89
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*FD).Accept(0xc0000fe080)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_unix.go:611 +0x2ac fp=0xc000029988 sp=0xc0000298e0 pc=0x556d19c8636c
Dec 18 02:31:29 ksy ollama[2877902]: net.(*netFD).accept(0xc0000fe080)
Dec 18 02:31:29 ksy ollama[2877902]: net/fd_unix.go:172 +0x29 fp=0xc000029a40 sp=0xc000029988 pc=0x556d19cf4fa9
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPListener).accept(0xc00007c1c0)
Dec 18 02:31:29 ksy ollama[2877902]: net/tcpsock_posix.go:159 +0x1e fp=0xc000029a68 sp=0xc000029a40 pc=0x556d19d05cde
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPListener).Accept(0xc00007c1c0)
Dec 18 02:31:29 ksy ollama[2877902]: net/tcpsock.go:327 +0x30 fp=0xc000029a98 sp=0xc000029a68 pc=0x556d19d05030
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*onceCloseListener).Accept(0xc000122000?)
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x24 fp=0xc000029ab0 sp=0xc000029a98 pc=0x556d19e2c244
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*Server).Serve(0xc0000181e0, {0x556d1a19d360, 0xc00007c1c0})
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3260 +0x33e fp=0xc000029be0 sp=0xc000029ab0 pc=0x556d19e2305e
Dec 18 02:31:29 ksy ollama[2877902]: main.main()
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:1000 +0x10cd fp=0xc000029f50 sp=0xc000029be0 pc=0x556d19e4f80d
Dec 18 02:31:29 ksy ollama[2877902]: runtime.main()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:271 +0x29d fp=0xc000029fe0 sp=0xc000029f50 pc=0x556d19c080bd
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc000029fe8 sp=0xc000029fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 2 gp=0xc000006c40 m=nil [force gc (idle)]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006cfa8 sp=0xc00006cf88 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.forcegchelper()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:326 +0xb8 fp=0xc00006cfe0 sp=0xc00006cfa8 pc=0x556d19c08378
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006cfe8 sp=0xc00006cfe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.init.6 in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:314 +0x1a
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 3 gp=0xc000007180 m=nil [GC sweep wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006d780 sp=0xc00006d760 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.bgsweep(0xc00007e000)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcsweep.go:278 +0x94 fp=0xc00006d7c8 sp=0xc00006d780 pc=0x556d19bf3034
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gcenable.gowrap1()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:203 +0x25 fp=0xc00006d7e0 sp=0xc00006d7c8 pc=0x556d19be7b65
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006d7e8 sp=0xc00006d7e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.gcenable in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:203 +0x66
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 4 gp=0xc000007340 m=nil [GC scavenge wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0xc00007e000?, 0x556d1a09a4f0?, 0x1?, 0x0?, 0xc000007340?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006df78 sp=0xc00006df58 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goparkunlock(...)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:408
Dec 18 02:31:29 ksy ollama[2877902]: runtime.(*scavengerState).park(0x556d1a36c560)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcscavenge.go:425 +0x49 fp=0xc00006dfa8 sp=0xc00006df78 pc=0x556d19bf0a29
Dec 18 02:31:29 ksy ollama[2877902]: runtime.bgscavenge(0xc00007e000)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgcscavenge.go:653 +0x3c fp=0xc00006dfc8 sp=0xc00006dfa8 pc=0x556d19bf0fbc
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gcenable.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:204 +0x25 fp=0xc00006dfe0 sp=0xc00006dfc8 pc=0x556d19be7b05
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006dfe8 sp=0xc00006dfe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.gcenable in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mgc.go:204 +0xa5
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 5 gp=0xc000007c00 m=nil [finalizer wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0xc00006c648?, 0x556d19bdb465?, 0xa8?, 0x1?, 0xc0000061c0?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc00006c620 sp=0xc00006c600 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.runfinq()
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mfinal.go:194 +0x107 fp=0xc00006c7e0 sp=0xc00006c620 pc=0x556d19be6ba7
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc00006c7e8 sp=0xc00006c7e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by runtime.createfing in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: runtime/mfinal.go:164 +0x3d
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 18 gp=0xc000007dc0 m=nil [chan receive]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x556d19c382d4?, 0xc0000f3890?, 0x65?, 0xa6?, 0xc0000f3878?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc0000f3858 sp=0xc0000f3838 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.chanrecv(0xc0002000c0, 0xc0000f3a08, 0x1)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/chan.go:583 +0x3bf fp=0xc0000f38d0 sp=0xc0000f3858 pc=0x556d19bd3ebf
Dec 18 02:31:29 ksy ollama[2877902]: runtime.chanrecv1(0xc000112030?, 0xc00029e000?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/chan.go:442 +0x12 fp=0xc0000f38f8 sp=0xc0000f38d0 pc=0x556d19bd3af2
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).embeddings(0xc0000ce120, {0x556d1a19d510, 0xc000218000}, 0xc000204000)
Dec 18 02:31:29 ksy ollama[2877902]: github.com/ollama/ollama/llama/runner/runner.go:793 +0x746 fp=0xc0000f3ab8 sp=0xc0000f38f8 pc=0x556d19e4dc66
Dec 18 02:31:29 ksy ollama[2877902]: main.(*Server).embeddings-fm({0x556d1a19d510?, 0xc000218000?}, 0x556d19e2738d?)
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x36 fp=0xc0000f3ae8 sp=0xc0000f3ab8 pc=0x556d19e50236
Dec 18 02:31:29 ksy ollama[2877902]: net/http.HandlerFunc.ServeHTTP(0xc0000b4d00?, {0x556d1a19d510?, 0xc000218000?}, 0x10?)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2171 +0x29 fp=0xc0000f3b10 sp=0xc0000f3ae8 pc=0x556d19e1fe29
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*ServeMux).ServeHTTP(0x556d19bdb465?, {0x556d1a19d510, 0xc000218000}, 0xc000204000)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2688 +0x1ad fp=0xc0000f3b60 sp=0xc0000f3b10 pc=0x556d19e21cad
Dec 18 02:31:29 ksy ollama[2877902]: net/http.serverHandler.ServeHTTP({0x556d1a19c860?}, {0x556d1a19d510?, 0xc000218000?}, 0x6?)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3142 +0x8e fp=0xc0000f3b90 sp=0xc0000f3b60 pc=0x556d19e22cce
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*conn).serve(0xc000122000, {0x556d1a19d968, 0xc0000b2db0})
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:2044 +0x5e8 fp=0xc0000f3fb8 sp=0xc0000f3b90 pc=0x556d19e1ea68
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*Server).Serve.gowrap3()
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3290 +0x28 fp=0xc0000f3fe0 sp=0xc0000f3fb8 pc=0x556d19e23448
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc0000f3fe8 sp=0xc0000f3fe0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by net/http.(*Server).Serve in goroutine 1
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:3290 +0x4b4
Dec 18 02:31:29 ksy ollama[2877902]: goroutine 34 gp=0xc000224000 m=nil [IO wait]:
Dec 18 02:31:29 ksy ollama[2877902]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0xb?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/proc.go:402 +0xce fp=0xc0000685a8 sp=0xc000068588 pc=0x556d19c084ee
Dec 18 02:31:29 ksy ollama[2877902]: runtime.netpollblock(0x556d19c6ea38?, 0x19bd1006?, 0x6d?)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:573 +0xf7 fp=0xc0000685e0 sp=0xc0000685a8 pc=0x556d19c00737
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.runtime_pollWait(0x7f7cba9c7f28, 0x72)
Dec 18 02:31:29 ksy ollama[2877902]: runtime/netpoll.go:345 +0x85 fp=0xc000068600 sp=0xc0000685e0 pc=0x556d19c34f85
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).wait(0xc000120000?, 0xc0000b2e21?, 0x0)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc000068628 sp=0xc000068600 pc=0x556d19c84ea7
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*pollDesc).waitRead(...)
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_poll_runtime.go:89
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll.(*FD).Read(0xc000120000, {0xc0000b2e21, 0x1, 0x1})
Dec 18 02:31:29 ksy ollama[2877902]: internal/poll/fd_unix.go:164 +0x27a fp=0xc0000686c0 sp=0xc000068628 pc=0x556d19c859fa
Dec 18 02:31:29 ksy ollama[2877902]: net.(*netFD).Read(0xc000120000, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: net/fd_posix.go:55 +0x25 fp=0xc000068708 sp=0xc0000686c0 pc=0x556d19cf3ea5
Dec 18 02:31:29 ksy ollama[2877902]: net.(*conn).Read(0xc000114008, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: net/net.go:185 +0x45 fp=0xc000068750 sp=0xc000068708 pc=0x556d19cfe165
Dec 18 02:31:29 ksy ollama[2877902]: net.(*TCPConn).Read(0x0?, {0xc0000b2e21?, 0x0?, 0x0?})
Dec 18 02:31:29 ksy ollama[2877902]: <autogenerated>:1 +0x25 fp=0xc000068780 sp=0xc000068750 pc=0x556d19d09b45
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*connReader).backgroundRead(0xc0000b2e10)
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:681 +0x37 fp=0xc0000687c8 sp=0xc000068780 pc=0x556d19e189d7
Dec 18 02:31:29 ksy ollama[2877902]: net/http.(*connReader).startBackgroundRead.gowrap2()
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:677 +0x25 fp=0xc0000687e0 sp=0xc0000687c8 pc=0x556d19e18905
Dec 18 02:31:29 ksy ollama[2877902]: runtime.goexit({})
Dec 18 02:31:29 ksy ollama[2877902]: runtime/asm_amd64.s:1695 +0x1 fp=0xc0000687e8 sp=0xc0000687e0 pc=0x556d19c3a2c1
Dec 18 02:31:29 ksy ollama[2877902]: created by net/http.(*connReader).startBackgroundRead in goroutine 18
Dec 18 02:31:29 ksy ollama[2877902]: net/http/server.go:677 +0xba
Dec 18 02:31:29 ksy ollama[2877902]: rax 0x0
Dec 18 02:31:29 ksy ollama[2877902]: rbx 0x7f7cc1974000
Dec 18 02:31:29 ksy ollama[2877902]: rcx 0x7f7c9c6419fc
Dec 18 02:31:29 ksy ollama[2877902]: rdx 0x6
Dec 18 02:31:29 ksy ollama[2877902]: rdi 0x2c0d82
Dec 18 02:31:29 ksy ollama[2877902]: rsi 0x2c0d82
Dec 18 02:31:29 ksy ollama[2877902]: rbp 0x2c0d82
Dec 18 02:31:29 ksy ollama[2877902]: rsp 0x7ffd316fc6b0
Dec 18 02:31:29 ksy ollama[2877902]: r8 0x7ffd316fc780
Dec 18 02:31:29 ksy ollama[2877902]: r9 0x7ffd316fc750
Dec 18 02:31:29 ksy ollama[2877902]: r10 0x8
Dec 18 02:31:29 ksy ollama[2877902]: r11 0x246
Dec 18 02:31:29 ksy ollama[2877902]: r12 0x6
Dec 18 02:31:29 ksy ollama[2877902]: r13 0x16
Dec 18 02:31:29 ksy ollama[2877902]: r14 0x0
Dec 18 02:31:29 ksy ollama[2877902]: r15 0x0
Dec 18 02:31:29 ksy ollama[2877902]: rip 0x7f7c9c6419fc
Dec 18 02:31:29 ksy ollama[2877902]: rflags 0x246
Dec 18 02:31:29 ksy ollama[2877902]: cs 0x33
Dec 18 02:31:29 ksy ollama[2877902]: fs 0x0
Dec 18 02:31:29 ksy ollama[2877902]: gs 0x0
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.804+08:00 level=INFO source=routes.go:507 msg="embedding generation failed: do embedding request: Post \"http://127.0.0.1:45295/embedding\": EOF"
Dec 18 02:31:29 ksy ollama[2877902]: [GIN] 2024/12/18 - 02:31:29 | 500 | 5.564875389s | 192.168.176.6 | POST "/api/embeddings"
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.804+08:00 level=DEBUG source=sched.go:466 msg="context for request finished"
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.804+08:00 level=DEBUG source=sched.go:339 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa duration=5m0s
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.804+08:00 level=DEBUG source=sched.go:357 msg="after processing request finished event" modelPath=/data/ollama/blobs/sha256-3757be8630cc587da3948fe2f1fbb646770a18fa04adc57f1c8977dd0e6281fa refCount=0
Dec 18 02:31:29 ksy ollama[2877902]: time=2024-12-18T02:31:29.815+08:00 level=DEBUG source=server.go:437 msg="llama runner terminated" error="exit status 2"
```
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.1 (client version is 0.5.3)
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I_kwDOJ0Z1Ps6hUh73
| 7,887
|
Add tests for openai response logic - potentially refactor middleware
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| 2024-11-30T02:45:55
| 2024-11-30T02:46:08
| null |
CONTRIBUTOR
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| 1,991,509,510
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I_kwDOJ0Z1Ps52tAYG
| 1,116
|
vicuna33b not executing on GPU
|
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NONE
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I am using Debian with an A4000. Vicuna13b will execute on GPU just fine but Vicuna33b will not.
Is this a bug or am I misunderstanding something in the documentation or configuration?
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PR_kwDOJ0Z1Ps5bbUoB
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|
Adding mistral 7B to README.md
|
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| 2023-09-28T10:39:05
| 2023-09-30T02:59:56
| 2023-09-29T00:36:25
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NONE
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| 2,599,522,473
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I_kwDOJ0Z1Ps6a8ZCp
| 7,272
|
multi-part model+safetensors
|
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| 2024-10-19T19:33:50
| 2024-10-23T01:36:43
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NONE
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How do I run a gguf multi-part model on ollaama?
https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/blob/main/qwen2.5-7b-instruct-fp16-00004-of-00004.gguf
qwen2.5-7b-instruct-fp16-00001-of-00004.gguf
qwen2.5-7b-instruct-fp16-00002-of-00004.gguf
qwen2.5-7b-instruct-fp16-00003-of-00004.gguf
qwen2.5-7b-instruct-fp16-00004-of-00004.gguf
how to run safetensors model on ollama ?
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I_kwDOJ0Z1Ps6ZW5yc
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|
Getting Error with OpenAI compatibility
|
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| 2024-10-08T11:27:46
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### What is the issue?
```js
import { NextApiRequest } from 'next';
import { OpenAIStream, StreamingTextResponse } from 'ai';
import OpenAI from 'openai';
const openai = new OpenAI({
baseURL: 'http://localhost:11434/v1',
apiKey: 'ollama', // required but unused
});
export async function POST(req: NextApiRequest) {
const body = await req.json();
console.log("messages", body);
try {
const response = await openai.chat.completions.create({
model: 'llama3',
messages: body.messages,
});
const stream = OpenAIStream(response);
return new StreamingTextResponse(stream);
} catch (error) {
console.error("error", error);
}
}
```
Log before Error:
```
messages { messages: [ { role: 'user', content: "What is today's date?" } ] }
```
Getting Error
```
error APIConnectionError: Connection error.
at OpenAI.makeRequest (webpack-internal:///(rsc)/./node_modules/openai/core.mjs:321:19)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async POST (webpack-internal:///(rsc)/./src/app/api/chat/route.ts:20:26)
```
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.0
| null |
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PR_kwDOJ0Z1Ps5y4zPl
| 5,126
|
update message processing
|
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| 2024-06-19T00:32:04
| 2024-07-09T16:20:48
| 2024-07-09T16:20:44
|
CONTRIBUTOR
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this change changes the way messages are processed before handing off to the llm. there are a few areas worth mentioning:
1. messages are now a first class component of the template. template rendering will only falling back to the previous iterative template if messages is unsupported by the template. however, new models _should_ implement the previous prompt/response template for compatibility with older ollama versions
2. the generate endpoint has been updated to use messages for prompt templating but the end result should be the same
3. the chat endpoint has been updated to preprocess incoming messages
- continuous messages of the same role are joined into a single message, separated with two newlines
- content and image data can be interleaved by sending messages with alternating fields, e.g.
```
[
{"role": "user", "content": "Consider the following images:"},
{"role": "user", "images": ["<base64 image data>", "<base64 image data>"]},
{"role": "user", "content": "What is the difference between the two images?"}
]
```
- system messages are aggregated and prepended to the _last_ user message
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| 2,786,508,663
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I_kwDOJ0Z1Ps6mFr93
| 8,418
|
Running OLLAMA_FLASH_ATTENTION=true with LoRA Models Returns: flash_attn is not compatible with LoRA
|
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open
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| null |
[] | null | 0
| 2025-01-14T08:09:59
| 2025-01-14T08:09:59
| null |
NONE
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### What is the issue?
Hello, I use fine tuned LLMs that use LoRA, when activating OLLAMA_FLASH_ATTENTION=true ollama serve the fine tuned models do not work, the error received is:
```
llama_lora_adapter_set: flash_attn is not compatible with LoRA
panic: error applying lora from file
```
This error stops the model from running until you flag it false. Then it runs correctly when its turned to false.
I have found on the llama.cpp git that they edited and removed part of their code last week to fix this. Here was the same issue: https://github.com/ggerganov/llama.cpp/discussions/11097
Here is the fix that was added to llama.cpp:
https://github.com/ggerganov/llama.cpp/pull/11104/files
I'm not sure what I've done is correct in providing the above information (first ever report), but I found that Ollama uses Llama.cpp so I thought I'd find the solution directly.
### OS
macOS
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.5.5
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I_kwDOJ0Z1Ps6K3J-8
| 4,785
|
ollama save feature
|
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| 2024-06-07T22:36:05
|
NONE
|
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Can you add the feature of ollama save as well as docker save that export the local docker image to migration to other envenriment?
|
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I_kwDOJ0Z1Ps561ptV
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|
The "seed" is not working reliable for me.
|
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| 2023-12-30T23:36:58
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| 2024-02-20T01:36:46
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NONE
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I am using a seed (int 1) for prompt generation with a mistral model, and it works not reliable. Instead, I get some interesting results with a pattern:
EDIT: It seems like this behavior is independent of the seed choice and the seeds are not working at all?
When freshly start `ollama serve` and send the exact same prompt together with a seed to "/api/generate" (stream: false) I always get three times the same reply. The fourth and all following replies are then different!
When I switch the model and make the same prompt to that, it also gives three of the same and then varying results!
As a workaround, I actually switch to another very small model and create a minimal embedding (is faster than doing an inference prompt) before doing the actual prompt and this gives me reliable results. Even if that actually works and is quite fast in my case, I think there is a problem that needs to be fixed.
I am on the current main [2a2fa3c](https://github.com/jmorganca/ollama/commit/2a2fa3c3298194f4f3790aade78df2f53d170d8e)
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|
Structured Outputs - Chat Endpoint
|
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## Structured outputs
A longtime ask from the community - we now support the passing in of a json schema, translate to grammar and use it for sampling.
## Why not full grammar support
We gave this a ton of thought and there's 3 main points around here:
1. Inherent complexity of grammars - Generating a grammar for the average user is not a great experience and should be one that is abstracted away from them. Digging into the code, the API layer also needs some TLC which would mean changing some interfaces on Ollama's end while maintaining a consistent UX.
2. Sampling performance - there are many new papers and methodologies for grammars (outlines, xgrammar, etc). We want to keep grammar generation and sampling coupled to improve the performance of sampling down the road.
3. Parity with existing experiences - other client SDKs (e.g. OpenAI) already support structured outputs and it's imperative we keep the experience simple on our end but also support those.
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I_kwDOJ0Z1Ps6KQDia
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Low GPU / High CPU Utilization ==> Slow Performance
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| 2024-05-27T16:07:43
| 2024-05-28T06:57:41
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NONE
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### What is the issue?
The Ollama on Ubuntu 22.04 can detect my Cuda GPU, and loads the model to its memory, but the processing seems to be mostly on CPU. Is this a normal behavior? The overall performance is not satisfying, like 1-token-per-second or so ... much slower than a human reading speed.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.38
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PR_kwDOJ0Z1Ps5rPnyx
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Community Integration: CRAG Ollama Chat
|
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| 2024-03-31T09:48:09
| 2024-04-01T15:16:14
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CONTRIBUTOR
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Corrective Retrieval Augmented Generation Demo, powered by Langgraph and Streamlit 🤗
Supports:
- Ollama
- OpenAI APIs
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I_kwDOJ0Z1Ps6L7zGW
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First value different on CUDA/ROCM when setting `seed`
|
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| 2024-06-12T03:14:30
| 2024-06-16T19:32:16
| null |
MEMBER
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### What is the issue?
This seems to be an issue with the kv cache on Nvidia/AMD GPUs. See https://github.com/ggerganov/llama.cpp/issues/2838
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PR_kwDOJ0Z1Ps5qchzv
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|
Add Testcontainers into Libraries section
|
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Testcontainers provides a module for Ollama.
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Ollama v0.1.32-rocm throws "CUDA error: out of memory" on AMD GPU with model that worked on v0.1.31-rocm
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"gists_url": "https://api.github.com/users/dhiltgen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/dhiltgen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/dhiltgen/subscriptions",
"organizations_url": "https://api.github.com/users/dhiltgen/orgs",
"repos_url": "https://api.github.com/users/dhiltgen/repos",
"events_url": "https://api.github.com/users/dhiltgen/events{/privacy}",
"received_events_url": "https://api.github.com/users/dhiltgen/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
] | null | 1
| 2024-04-17T06:49:16
| 2024-06-01T23:42:53
| 2024-06-01T23:42:45
|
NONE
|
{
"total": 0,
"completed": 0,
"percent_completed": 0
}
| null | null | null |
### What is the issue?
Hi, I've updated the Docker image `ollama/ollama:0.1.31-rocm` to `0.1.32-rocm` and started experiencing `CUDA error: out of memory` on `mixtral:8x7b` (`7708c059a8bb`) model that worked fine on `0.1.31-rocm`!
```js
CUDA error: out of memory
current device: 0, in function alloc at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:233
hipMalloc((void **) &ptr, look_ahead_size)
GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:60: !"CUDA error"
```
I am running on `24GB VRAM` AMD `7900 XTX GPU` with 64GB of RAM (`rocminfo` below).
<details>
<summary>Full log:</summary>
```js
time=2024-04-16T22:01:18.558-06:00 level=INFO source=images.go:817 msg="total blobs: 33"
time=2024-04-16T22:01:18.559-06:00 level=INFO source=images.go:824 msg="total unused blobs removed: 0"
time=2024-04-16T22:01:18.559-06:00 level=INFO source=routes.go:1143 msg="Listening on [::]:11434 (version 0.1.32)"
time=2024-04-16T22:01:18.560-06:00 level=INFO source=payload.go:28 msg="extracting embedded files" dir=/tmp/ollama3765403603/runners
time=2024-04-16T22:01:20.061-06:00 level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cuda_v11 rocm_v60002 cpu cpu_avx cpu_avx2]"
time=2024-04-16T22:01:20.061-06:00 level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-04-16T22:01:20.061-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-04-16T22:01:20.064-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-16T22:01:20.064-06:00 level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama"
time=2024-04-16T22:01:20.064-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-16T22:01:20.065-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []"
time=2024-04-16T22:01:20.065-06:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-16T22:01:20.065-06:00 level=INFO source=amd_linux.go:50 msg="AMD Driver: 6.3.6"
time=2024-04-16T22:01:20.065-06:00 level=INFO source=amd_linux.go:88 msg="detected amdgpu versions [gfx1100]"
time=2024-04-16T22:01:20.069-06:00 level=INFO source=amd_linux.go:121 msg="amdgpu [0] gfx1100 is supported"
time=2024-04-16T22:01:20.069-06:00 level=INFO source=amd_linux.go:263 msg="[0] amdgpu totalMemory 24560M"
time=2024-04-16T22:01:20.069-06:00 level=INFO source=amd_linux.go:264 msg="[0] amdgpu freeMemory 24560M"
[GIN] 2024/04/17 - 00:22:03 | 200 | 3.41949ms | 10.244.0.71 | GET "/api/tags"
[GIN] 2024/04/17 - 00:22:03 | 200 | 704.963µs | 10.244.0.71 | GET "/api/tags"
[GIN] 2024/04/17 - 00:22:03 | 200 | 887.792µs | 10.244.0.71 | GET "/api/tags"
[GIN] 2024/04/17 - 00:22:03 | 200 | 34.579µs | 10.244.0.71 | GET "/api/version"
time=2024-04-17T00:22:16.564-06:00 level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-04-17T00:22:16.564-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-04-17T00:22:16.565-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-17T00:22:16.565-06:00 level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama"
time=2024-04-17T00:22:16.565-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-17T00:22:16.566-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []"
time=2024-04-17T00:22:16.566-06:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-17T00:22:16.566-06:00 level=INFO source=amd_linux.go:50 msg="AMD Driver: 6.3.6"
time=2024-04-17T00:22:16.566-06:00 level=INFO source=amd_linux.go:88 msg="detected amdgpu versions [gfx1100]"
time=2024-04-17T00:22:16.568-06:00 level=INFO source=amd_linux.go:121 msg="amdgpu [0] gfx1100 is supported"
time=2024-04-17T00:22:16.568-06:00 level=INFO source=amd_linux.go:263 msg="[0] amdgpu totalMemory 24560M"
time=2024-04-17T00:22:16.568-06:00 level=INFO source=amd_linux.go:264 msg="[0] amdgpu freeMemory 24560M"
time=2024-04-17T00:22:16.568-06:00 level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-04-17T00:22:16.568-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-04-17T00:22:16.569-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-17T00:22:16.569-06:00 level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama3765403603/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama"
time=2024-04-17T00:22:16.569-06:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-17T00:22:16.570-06:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []"
time=2024-04-17T00:22:16.570-06:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-17T00:22:16.570-06:00 level=INFO source=amd_linux.go:50 msg="AMD Driver: 6.3.6"
time=2024-04-17T00:22:16.570-06:00 level=INFO source=amd_linux.go:88 msg="detected amdgpu versions [gfx1100]"
time=2024-04-17T00:22:16.571-06:00 level=INFO source=amd_linux.go:121 msg="amdgpu [0] gfx1100 is supported"
time=2024-04-17T00:22:16.571-06:00 level=INFO source=amd_linux.go:263 msg="[0] amdgpu totalMemory 24560M"
time=2024-04-17T00:22:16.571-06:00 level=INFO source=amd_linux.go:264 msg="[0] amdgpu freeMemory 24560M"
time=2024-04-17T00:22:16.572-06:00 level=INFO source=server.go:127 msg="offload to gpu" reallayers=29 layers=29 required="26042.6 MiB" used="24319.2 MiB" available="24560.0 MiB" kv="256.0 MiB" fulloffload="184.0 MiB" partialoffload="935.0 MiB"
time=2024-04-17T00:22:16.572-06:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-17T00:22:16.573-06:00 level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3765403603/runners/rocm_v60002/ollama_llama_server --model /root/.ollama/models/blobs/sha256-e9e56e8bb5f0fcd4860675e6837a8f6a94e659f5fa7dce6a1076279336320f2b --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 29 --port 45603"
time=2024-04-17T00:22:16.573-06:00 level=INFO source=server.go:389 msg="waiting for llama runner to start responding"
{"function":"server_params_parse","level":"INFO","line":2603,"msg":"logging to file is disabled.","tid":"134859583487040","timestamp":1713334936}
{"build":1,"commit":"7593639","function":"main","level":"INFO","line":2820,"msg":"build info","tid":"134859583487040","timestamp":1713334936}
{"function":"main","level":"INFO","line":2827,"msg":"system info","n_threads":12,"n_threads_batch":-1,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | ","tid":"134859583487040","timestamp":1713334936,"total_threads":24}
llama_model_loader: loaded meta data with 26 key-value pairs and 995 tensors from /root/.ollama/models/blobs/sha256-e9e56e8bb5f0fcd4860675e6837a8f6a94e659f5fa7dce6a1076279336320f2b (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = mistralai
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.expert_count u32 = 8
llama_model_loader: - kv 10: llama.expert_used_count u32 = 2
llama_model_loader: - kv 11: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 12: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: general.file_type u32 = 2
llama_model_loader: - kv 14: tokenizer.ggml.model str = llama
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,58980] = ["▁ t", "i n", "e r", "▁ a", "h e...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type f16: 32 tensors
llama_model_loader: - type q4_0: 833 tensors
llama_model_loader: - type q8_0: 64 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_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 8x7B
llm_load_print_meta: model ftype = Q4_0
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>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 ROCm devices:
Device 0: Radeon RX 7900 XTX, compute capability 11.0, VMM: no
llm_load_tensors: ggml ctx size = 0.96 MiB
llm_load_tensors: offloading 29 repeating layers to GPU
llm_load_tensors: offloaded 29/33 layers to GPU
llm_load_tensors: ROCm0 buffer size = 22695.22 MiB
llm_load_tensors: ROCm_Host buffer size = 2520.65 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: ROCm0 KV buffer size = 232.00 MiB
llama_kv_cache_init: ROCm_Host KV buffer size = 24.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: ROCm_Host output buffer size = 0.14 MiB
llama_new_context_with_model: ROCm0 compute buffer size = 826.00 MiB
llama_new_context_with_model: ROCm_Host compute buffer size = 12.01 MiB
llama_new_context_with_model: graph nodes = 1638
llama_new_context_with_model: graph splits = 41
{"function":"initialize","level":"INFO","line":448,"msg":"initializing slots","n_slots":1,"tid":"134859583487040","timestamp":1713334952}
{"function":"initialize","level":"INFO","line":460,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"134859583487040","timestamp":1713334952}
{"function":"main","level":"INFO","line":3064,"msg":"model loaded","tid":"134859583487040","timestamp":1713334952}
{"function":"main","hostname":"127.0.0.1","level":"INFO","line":3267,"msg":"HTTP server listening","n_threads_http":"23","port":"45603","tid":"134859583487040","timestamp":1713334952}
{"function":"update_slots","level":"INFO","line":1578,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"134859583487040","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":0,"tid":"134859583487040","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":2,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41606,"status":200,"tid":"134849364965120","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":59396,"status":200,"tid":"134849413240576","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":3,"tid":"134859583487040","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":4,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":59382,"status":200,"tid":"134849430025984","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":59400,"status":200,"tid":"134849421633280","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":5,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41588,"status":200,"tid":"134849348179712","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41602,"status":200,"tid":"134849356572416","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":6,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41236,"status":200,"tid":"134849404847872","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":7,"tid":"134859583487040","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41236,"status":200,"tid":"134849404847872","timestamp":1713334952}
{"function":"log_server_request","level":"INFO","line":2741,"method":"POST","msg":"request","params":{},"path":"/tokenize","remote_addr":"127.0.0.1","remote_port":41236,"status":200,"tid":"134849404847872","timestamp":1713334952}
{"function":"process_single_task","level":"INFO","line":1510,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":8,"tid":"134859583487040","timestamp":1713334953}
{"function":"log_server_request","level":"INFO","line":2741,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41236,"status":200,"tid":"134849404847872","timestamp":1713334953}
{"function":"launch_slot_with_data","level":"INFO","line":833,"msg":"slot is processing task","slot_id":0,"task_id":9,"tid":"134859583487040","timestamp":1713334953}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1816,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":80,"slot_id":0,"task_id":9,"tid":"134859583487040","timestamp":1713334953}
{"function":"update_slots","level":"INFO","line":1840,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":9,"tid":"134859583487040","timestamp":1713334953}
CUDA error: out of memory
current device: 0, in function alloc at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:233
hipMalloc((void **) &ptr, look_ahead_size)
GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:60: !"CUDA error"
No symbol table is loaded. Use the "file" command.
[New LWP 37]
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[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
0x00007aa73cfe71d9 in waitpid () from /lib64/libpthread.so.0
No symbol table is loaded. Use the "file" command.
```
</details>
---
When I try to run smaller model on `v0.1.32-rocm` such as `llama2:7b` it works well and I don't see any Nvidia/CUDA related errors in the log.
Happy to test dev Docker image builds, thank you for this project!
### What did you expect to see?
As per [release notes for v0.1.32](https://github.com/ollama/ollama/releases/tag/v0.1.32)
>Ollama will now better utilize available VRAM, leading to less out-of-memory errors
```js
CUDA error: out of memory
current device: 0, in function alloc at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:233
hipMalloc((void **) &ptr, look_ahead_size)
GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:60: !"CUDA error"
```
But in fact the model that fit the GPU previously now doesn't, not sure if error message is indicating that Nvidia stack was activated for my AMD GPU system or is a generic error message but it kind of suggests that unneeded CUDA assert was triggered on AMD GPU system 🙃
Expectation is for `v0.1.32-rocm` to be able to handle models that `v0.1.31-rocm` was able to handle :)
### Steps to reproduce
1. Update Docker image `ollama/ollama:0.1.31-rocm` to `0.1.32-rocm` on AMD 7900XTX 24GB VRAM 64GB RAM system
2. Select `mixtral:8x7b` model
3. Observe crash `CUDA error: out of memory`
### Are there any recent changes that introduced the issue?
_No response_
### OS
Linux
### Architecture
amd64
### Platform
Docker
### Ollama version
0.1.32-rocm
### GPU
AMD
### GPU info
<details>
<summary>rocminfo</summary>
```js
ROCk module is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.1
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
Uuid: CPU-XX
Marketing Name: AMD Ryzen 9 7900 12-Core Processor
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32768(0x8000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 5482
BDFID: 0
Internal Node ID: 0
Compute Unit: 24
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 65436972(0x3e67d2c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 65436972(0x3e67d2c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 65436972(0x3e67d2c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx1100
Uuid: GPU-6ab835b902b859a1
Marketing Name: Radeon RX 7900 XTX
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: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2482
BDFID: 768
Internal Node ID: 1
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Coherent Host Access: FALSE
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:: 550
SDMA engine uCode:: 19
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
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 ***
```
</details>
### CPU
AMD
### Other software
_No response_
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