Repository: ollama
Issue: llama runner process has terminated: exit status 0xc0000005
Labels: bug,model request

### What is the issue?

It's again the https://github.com/ollama/ollama/issues/6011 issue.

**The issue is with embedding call with the model converted using convert_hf_to_gguf.py.**

litellm.llms.ollama.OllamaError: {"error":"llama runner process has terminated: exit status 0xc0000005"}

```
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"
```

### OS

Windows

### GPU

_No response_

### CPU

Intel

### Ollama version

0.3.11 0.3.12