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[GH-ISSUE #6011] lama runner process has terminated: exit status 0xc0000005 - snowflake-arctic-embed #65797

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opened 2026-05-03 22:44:44 -05:00 by GiteaMirror · 12 comments
GiteaMirror commented 2026-05-03 22:44:44 -05:00
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Originally created by @imkebe on GitHub (Jul 27, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/6011

What is the issue?

It's again the #4334 issue. I have multiple local CPU nodes and using ollama behind the litellm proxy.
The issue is with embedding call for snowflake-arctic-embed model.
For example nomic-embed-text seems to be working fine.

litellm.llms.ollama.OllamaError: {"error":"llama runner process has terminated: exit status 0xc0000005"}

OS

Windows

GPU

No response

CPU

Intel

Ollama version

0.2.8 - 0.3.0

Originally created by @imkebe on GitHub (Jul 27, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/6011 ### What is the issue? It's again the #4334 issue. I have multiple local CPU nodes and using ollama behind the litellm proxy. The issue is with embedding call for **snowflake-arctic-embed** model. For example **nomic-embed-text** seems to be working fine. ```litellm.llms.ollama.OllamaError: {"error":"llama runner process has terminated: exit status 0xc0000005"}``` ### OS Windows ### GPU _No response_ ### CPU Intel ### Ollama version 0.2.8 - 0.3.0
GiteaMirror added the bug label 2026-05-03 22:44:44 -05:00
GiteaMirror commented 2026-05-03 22:44:48 -05:00
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@rick-github commented on GitHub (Jul 27, 2024):

Server logs may help in diagnosis. The previous fix was for AMD GPU on Windows, but you are experiencing this on multiple platforms.

FWIW, it works fine on my linux system:

$ curl -s localhost:11434/api/version
{"version":"0.3.0"}
$ curl -s localhost:11434/v1/embeddings -d '{"model":"snowflake-arctic-embed","input":"Your text string goes here"}' | jq '.data[].embedding=[.data[].embedding|length]'
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        1024
      ],
      "index": 0
    }
  ],
  "model": "snowflake-arctic-embed"
}
<!-- gh-comment-id:2254119991 --> @rick-github commented on GitHub (Jul 27, 2024): Server logs may help in diagnosis. The previous fix was for AMD GPU on Windows, but you are experiencing this on multiple platforms. FWIW, it works fine on my linux system: ``` $ curl -s localhost:11434/api/version {"version":"0.3.0"} $ curl -s localhost:11434/v1/embeddings -d '{"model":"snowflake-arctic-embed","input":"Your text string goes here"}' | jq '.data[].embedding=[.data[].embedding|length]' { "object": "list", "data": [ { "object": "embedding", "embedding": [ 1024 ], "index": 0 } ], "model": "snowflake-arctic-embed" } ```
GiteaMirror commented 2026-05-03 22:44:49 -05:00
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@imkebe commented on GitHub (Jul 27, 2024):

win_x64.log

<!-- gh-comment-id:2254121775 --> @imkebe commented on GitHub (Jul 27, 2024): [win_x64.log](https://github.com/user-attachments/files/16399105/win_x64.log)
GiteaMirror commented 2026-05-03 22:44:50 -05:00
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@rick-github commented on GitHub (Jul 27, 2024):

Thanks. This shows a failure on a windows machine, which may very well be a repeat of the previous problem. Do you have a log from a failure on a linux machine?

<!-- gh-comment-id:2254123108 --> @rick-github commented on GitHub (Jul 27, 2024): Thanks. This shows a failure on a windows machine, which may very well be a repeat of the previous problem. Do you have a log from a failure on a linux machine?
GiteaMirror commented 2026-05-03 22:44:51 -05:00
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@imkebe commented on GitHub (Jul 27, 2024):

Sorry I was wrong. That is not happening on linux. My LiteLLM is doing some load-balancing and I thought that went to linux nodes.

But there is also a segmentation fault in case of MacOS x64.

mac_x64.log

<!-- gh-comment-id:2254130167 --> @imkebe commented on GitHub (Jul 27, 2024): Sorry I was wrong. That is not happening on linux. My LiteLLM is doing some load-balancing and I thought that went to linux nodes. But there is also a `segmentation fault` in case of MacOS x64. [mac_x64.log](https://github.com/user-attachments/files/16400060/mac_x64.log)
GiteaMirror commented 2026-05-03 22:44:52 -05:00
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@rick-github commented on GitHub (Jul 27, 2024):

The mac log doesn't have any errors in it.

<!-- gh-comment-id:2254136928 --> @rick-github commented on GitHub (Jul 27, 2024): The mac log doesn't have any errors in it.
GiteaMirror commented 2026-05-03 22:44:52 -05:00
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@imkebe commented on GitHub (Jul 27, 2024):

I've re-uploaded it.

time=2024-07-27T14:04:05.563+02:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault"

<!-- gh-comment-id:2254138456 --> @imkebe commented on GitHub (Jul 27, 2024): I've re-uploaded it. `time=2024-07-27T14:04:05.563+02:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault"`
GiteaMirror commented 2026-05-03 22:44:53 -05:00
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@rick-github commented on GitHub (Jul 27, 2024):

Are there any crash logs in ~/Library/Logs/DiagnosticReports/ ?

<!-- gh-comment-id:2254139251 --> @rick-github commented on GitHub (Jul 27, 2024): Are there any crash logs in ~/Library/Logs/DiagnosticReports/ ?
GiteaMirror commented 2026-05-03 22:44:54 -05:00
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@imkebe commented on GitHub (Jul 27, 2024):

ollama_llama_server-2024-07-27-140405.txt

Changed the extension to .txt

<!-- gh-comment-id:2254140419 --> @imkebe commented on GitHub (Jul 27, 2024): [ollama_llama_server-2024-07-27-140405.txt](https://github.com/user-attachments/files/16400221/ollama_llama_server-2024-07-27-140405.txt) Changed the extension to .txt
GiteaMirror commented 2026-05-03 22:44:55 -05:00
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@rick-github commented on GitHub (Jul 27, 2024):

Failed in ggml_compute_forward which is deep inside llama.cpp. There are currently no open tickets in the issue tracker, perhaps filing one will get a response from somebody who has seen something like this before.

From my limited understanding, Windows error 0xc0000005 and Mac SEGV can have different root causes, so you may be experiencing different faults.

<!-- gh-comment-id:2254148212 --> @rick-github commented on GitHub (Jul 27, 2024): Failed in `ggml_compute_forward` which is deep inside llama.cpp. There are currently no open tickets in the [issue tracker](https://github.com/ggerganov/llama.cpp/issues), perhaps filing one will get a response from somebody who has seen something like this before. From my limited understanding, Windows error 0xc0000005 and Mac SEGV can have different root causes, so you may be experiencing different faults.
GiteaMirror commented 2026-05-03 22:44:55 -05:00
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@imkebe commented on GitHub (Jul 27, 2024):

I will raise a llama.cpp ticket. I understand there is different root cause for each.

<!-- gh-comment-id:2254148822 --> @imkebe commented on GitHub (Jul 27, 2024): I will raise a llama.cpp ticket. I understand there is different root cause for each.
GiteaMirror commented 2026-05-03 22:44:56 -05:00
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@igorschlum commented on GitHub (Aug 10, 2024):

@imkebe, could you share the ticket you created for llama.cpp in the comments?

Do you think we need to keep an issue open on the Ollama GitHub for every issue reported on Ollama’s GitHub that is caused by a llama.cpp issue? This is an open question, as both options have merit. If @jmorganca thinks we should track llama.cpp issues on Ollama, it would be useful to add a "llama.cpp" tag to each issue reported on Ollama’s GitHub that is caused by a llama.cpp issue.

<!-- gh-comment-id:2280682491 --> @igorschlum commented on GitHub (Aug 10, 2024): @imkebe, could you share the ticket you created for llama.cpp in the comments? Do you think we need to keep an issue open on the Ollama GitHub for every issue reported on Ollama’s GitHub that is caused by a llama.cpp issue? This is an open question, as both options have merit. If @jmorganca thinks we should track llama.cpp issues on Ollama, it would be useful to add a "llama.cpp" tag to each issue reported on Ollama’s GitHub that is caused by a llama.cpp issue.
GiteaMirror commented 2026-05-03 22:44:57 -05:00
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@viosay commented on GitHub (Sep 25, 2024):

I also encountered this issue with the model converted using convert_hf_to_gguf.py on Windows.

INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="13505" tid="18380" timestamp=1727231008
llama_model_loader: loaded meta data with 26 key-value pairs and 389 tensors from C:\Users\Administrator\.ollama\models\blobs\sha256-aad91e93e9ec705a527cfa8701698055cf473223437acd029762bb77be6fc92d (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              = bert
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Conan_Embedding_V1
llama_model_loader: - kv   3:                         general.size_label str              = 324M
llama_model_loader: - kv   4:                            general.license str              = cc-by-nc-4.0
llama_model_loader: - kv   5:                               general.tags arr[str,1]       = ["mteb"]
llama_model_loader: - kv   6:                           bert.block_count u32              = 24
llama_model_loader: - kv   7:                        bert.context_length u32              = 512
llama_model_loader: - kv   8:                      bert.embedding_length u32              = 1024
llama_model_loader: - kv   9:                   bert.feed_forward_length u32              = 4096
llama_model_loader: - kv  10:                  bert.attention.head_count u32              = 16
llama_model_loader: - kv  11:          bert.attention.layer_norm_epsilon f32              = 0.000000
llama_model_loader: - kv  12:                          general.file_type u32              = 1
llama_model_loader: - kv  13:                      bert.attention.causal bool             = false
llama_model_loader: - kv  14:                          bert.pooling_type u32              = 1
llama_model_loader: - kv  15:            tokenizer.ggml.token_type_count u32              = 2
llama_model_loader: - kv  16:                       tokenizer.ggml.model str              = bert
llama_model_loader: - kv  17:                         tokenizer.ggml.pre str              = Conan-embedding-v1
llama_model_loader: - kv  18:                      tokenizer.ggml.tokens arr[str,21128]   = ["[PAD]", "[unused1]", "[unused2]", "...
llama_model_loader: - kv  19:                  tokenizer.ggml.token_type arr[i32,21128]   = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  20:            tokenizer.ggml.unknown_token_id u32              = 100
llama_model_loader: - kv  21:          tokenizer.ggml.seperator_token_id u32              = 102
llama_model_loader: - kv  22:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  23:                tokenizer.ggml.cls_token_id u32              = 101
llama_model_loader: - kv  24:               tokenizer.ggml.mask_token_id u32              = 103
llama_model_loader: - kv  25:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  244 tensors
llama_model_loader: - type  f16:  145 tensors
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.0769 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = bert
llm_load_print_meta: vocab type       = WPM
llm_load_print_meta: n_vocab          = 21128
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 512
llm_load_print_meta: n_embd           = 1024
llm_load_print_meta: n_layer          = 24
llm_load_print_meta: n_head           = 16
llm_load_print_meta: n_head_kv        = 16
llm_load_print_meta: n_rot            = 64
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 64
llm_load_print_meta: n_embd_head_v    = 64
llm_load_print_meta: n_gqa            = 1
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       = 1.0e-12
llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
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             = 4096
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 0
llm_load_print_meta: pooling type     = 1
llm_load_print_meta: rope type        = 2
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 512
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 335M
llm_load_print_meta: model ftype      = F16
llm_load_print_meta: model params     = 324.47 M
llm_load_print_meta: model size       = 620.50 MiB (16.04 BPW) 
llm_load_print_meta: general.name     = Conan_Embedding_V1
llm_load_print_meta: UNK token        = 100 '[UNK]'
llm_load_print_meta: SEP token        = 102 '[SEP]'
llm_load_print_meta: PAD token        = 0 '[PAD]'
llm_load_print_meta: CLS token        = 101 '[CLS]'
llm_load_print_meta: MASK token       = 103 '[MASK]'
llm_load_print_meta: LF token         = 0 '[PAD]'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size =    0.16 MiB
llm_load_tensors:        CPU buffer size =   620.50 MiB
time=2024-09-25T10:23:28.796+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model"
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: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =   192.00 MiB
llama_new_context_with_model: KV self size  =  192.00 MiB, K (f16):   96.00 MiB, V (f16):   96.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.00 MiB
llama_new_context_with_model:        CPU compute buffer size =    26.00 MiB
llama_new_context_with_model: graph nodes  = 851
llama_new_context_with_model: graph splits = 1
time=2024-09-25T10:23:30.338+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding"
time=2024-09-25T10:23:31.963+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error"
time=2024-09-25T10:23:32.226+08:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000005"
[GIN] 2024/09/25 - 10:23:32 | 500 |    3.7323168s |       127.0.0.1 | POST     "/api/embed"```
<!-- gh-comment-id:2372757487 --> @viosay commented on GitHub (Sep 25, 2024): I also encountered this issue with the model converted using convert_hf_to_gguf.py on Windows. ```INFO [wmain] system info | n_threads=6 n_threads_batch=6 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="18380" timestamp=1727231008 total_threads=12 INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="13505" tid="18380" timestamp=1727231008 llama_model_loader: loaded meta data with 26 key-value pairs and 389 tensors from C:\Users\Administrator\.ollama\models\blobs\sha256-aad91e93e9ec705a527cfa8701698055cf473223437acd029762bb77be6fc92d (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 = bert llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Conan_Embedding_V1 llama_model_loader: - kv 3: general.size_label str = 324M llama_model_loader: - kv 4: general.license str = cc-by-nc-4.0 llama_model_loader: - kv 5: general.tags arr[str,1] = ["mteb"] llama_model_loader: - kv 6: bert.block_count u32 = 24 llama_model_loader: - kv 7: bert.context_length u32 = 512 llama_model_loader: - kv 8: bert.embedding_length u32 = 1024 llama_model_loader: - kv 9: bert.feed_forward_length u32 = 4096 llama_model_loader: - kv 10: bert.attention.head_count u32 = 16 llama_model_loader: - kv 11: bert.attention.layer_norm_epsilon f32 = 0.000000 llama_model_loader: - kv 12: general.file_type u32 = 1 llama_model_loader: - kv 13: bert.attention.causal bool = false llama_model_loader: - kv 14: bert.pooling_type u32 = 1 llama_model_loader: - kv 15: tokenizer.ggml.token_type_count u32 = 2 llama_model_loader: - kv 16: tokenizer.ggml.model str = bert llama_model_loader: - kv 17: tokenizer.ggml.pre str = Conan-embedding-v1 llama_model_loader: - kv 18: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "... llama_model_loader: - kv 19: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 100 llama_model_loader: - kv 21: tokenizer.ggml.seperator_token_id u32 = 102 llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 23: tokenizer.ggml.cls_token_id u32 = 101 llama_model_loader: - kv 24: tokenizer.ggml.mask_token_id u32 = 103 llama_model_loader: - kv 25: general.quantization_version u32 = 2 llama_model_loader: - type f32: 244 tensors llama_model_loader: - type f16: 145 tensors llm_load_vocab: special tokens cache size = 5 llm_load_vocab: token to piece cache size = 0.0769 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = bert llm_load_print_meta: vocab type = WPM llm_load_print_meta: n_vocab = 21128 llm_load_print_meta: n_merges = 0 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 512 llm_load_print_meta: n_embd = 1024 llm_load_print_meta: n_layer = 24 llm_load_print_meta: n_head = 16 llm_load_print_meta: n_head_kv = 16 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_swa = 0 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 1 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 = 1.0e-12 llm_load_print_meta: f_norm_rms_eps = 0.0e+00 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 = 4096 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 0 llm_load_print_meta: pooling type = 1 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 512 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: ssm_dt_b_c_rms = 0 llm_load_print_meta: model type = 335M llm_load_print_meta: model ftype = F16 llm_load_print_meta: model params = 324.47 M llm_load_print_meta: model size = 620.50 MiB (16.04 BPW) llm_load_print_meta: general.name = Conan_Embedding_V1 llm_load_print_meta: UNK token = 100 '[UNK]' llm_load_print_meta: SEP token = 102 '[SEP]' llm_load_print_meta: PAD token = 0 '[PAD]' llm_load_print_meta: CLS token = 101 '[CLS]' llm_load_print_meta: MASK token = 103 '[MASK]' llm_load_print_meta: LF token = 0 '[PAD]' llm_load_print_meta: max token length = 48 llm_load_tensors: ggml ctx size = 0.16 MiB llm_load_tensors: CPU buffer size = 620.50 MiB time=2024-09-25T10:23:28.796+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model" 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: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 192.00 MiB llama_new_context_with_model: KV self size = 192.00 MiB, K (f16): 96.00 MiB, V (f16): 96.00 MiB llama_new_context_with_model: CPU output buffer size = 0.00 MiB llama_new_context_with_model: CPU compute buffer size = 26.00 MiB llama_new_context_with_model: graph nodes = 851 llama_new_context_with_model: graph splits = 1 time=2024-09-25T10:23:30.338+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding" time=2024-09-25T10:23:31.963+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error" time=2024-09-25T10:23:32.226+08:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000005" [GIN] 2024/09/25 - 10:23:32 | 500 | 3.7323168s | 127.0.0.1 | POST "/api/embed"```
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Reference: github-starred/ollama#65797
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?