Error: an unknown error was encountered while running the model

OpenBMB / ollama

Get up and running with Llama 3, Mistral, Gemma, and other large language models.

MIT License

11 stars 5 forks source link

What is the issue?

按doc文档获取git分支，编译成功后，通过modelfile生成模型，在进行对话时报错Error: an unknown error was encountered while running the model

以下是serve端报错日志 2024/05/27 23:22:34 routes.go:1028: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION: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=2024-05-27T23:22:34.090+08:00 level=INFO source=images.go:729 msg="total blobs: 8" time=2024-05-27T23:22:34.090+08:00 level=INFO source=images.go:736 msg="total unused blobs removed: 0" [GIN-debug] [WARNING] Creating an Engine instance with the Logger and Recovery middleware already attached.

[GIN-debug] [WARNING] Running in "debug" mode. Switch to "release" mode in production.

using env: export GIN_MODE=release
using code: gin.SetMode(gin.ReleaseMode)

[GIN-debug] POST /api/pull [GIN-debug] POST /api/generate [GIN-debug] POST /api/chat [GIN-debug] POST /api/embeddings [GIN-debug] POST /api/create [GIN-debug] POST /api/push [GIN-debug] POST /api/copy [GIN-debug] DELETE /api/delete [GIN-debug] POST /api/show [GIN-debug] POST /api/blobs/:digest [GIN-debug] HEAD /api/blobs/:digest [GIN-debug] GET /api/ps [GIN-debug] POST /v1/chat/completions [GIN-debug] GET / [GIN-debug] GET /api/tags [GIN-debug] GET /api/version [GIN-debug] HEAD / [GIN-debug] HEAD /api/tags [GIN-debug] HEAD /api/version time=2024-05-27T23:22:34.091+08:00 time=2024-05-27T23:22:34.091+08:00 time=2024-05-27T23:22:34.166+08:00 time=2024-05-27T23:22:35.854+08:00 [GIN] 2024/05/27 - 23:22:40 | 200 | [GIN] 2024/05/27 - 23:22:40 | 200 | [GIN] 2024/05/27 - 23:22:50 | 200 | [GIN] 2024/05/27 - 23:22:50 | 200 | [GIN] 2024/05/27 - 23:22:50 | 200 | --> github.com/ollama/ollama/server.(Server).PullModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).GenerateHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).ChatHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).EmbeddingsHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).CreateModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).PushModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).CopyModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).DeleteModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).ShowModelHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).CreateBlobHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).HeadBlobHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).ProcessHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).ChatHandler-fm (6 handlers) --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func1 (5 handlers) --> github.com/ollama/ollama/server.(Server).ListModelsHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func2 (5 handlers) --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func1 (5 handlers) --> github.com/ollama/ollama/server.(Server).ListModelsHandler-fm (5 handlers) --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers) level=INFO source=routes.go:1074 msg="Listening on 127.0.0.1:11434 (version 0.0.0)" level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama3477894997/runners level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2]" level=INFO source=types.go:71 msg="inference compute" id=GPU-a93d53d5-add0-d73c-9800-83ba35515332 library=cuda compute=8.6 driver=12.4 name="NVIDIA GeForce RTX 3060 Ti" total="8.0 GiB" available="7.0 GiB" 5.063803ms | 127.0.0.1 | HEAD "/" 2.428835ms | 127.0.0.1 | GET "/api/tags" 33.81µs | 127.0.0.1 | HEAD "/" 3.816911ms | 127.0.0.1 | POST "/api/show" 223.798µs | 127.0.0.1 | POST "/api/show"

time=2024-05-27T23:22:52.868+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="7.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" time=2024-05-27T23:22:52.868+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="7.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" time=2024-05-27T23:22:52.869+08:00 level=INFO source=server.go:338 msg="starting llama server" cmd="/tmp/ollama3477894997/runners/cpu_avx2/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-010ec3ba94cb5ad2d9c8f95f46f01c6d80f83deab9df0a0831334ea45afff3e2 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 1 --port 60783" time=2024-05-27T23:22:52.869+08:00 level=INFO source=sched.go:338 msg="loaded runners" count=1 WARN [server_params_parse] 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 tid="139932059617152" timestamp=1716823372 INFO [main] build info | build=2994 commit="8541e996" tid="139932059617152" timestamp=1716823372 time=2024-05-27T23:22:52.871+08:00 level=INFO source=server.go:525 msg="waiting for llama runner to start responding" INFO [main] system info | n_threads=6 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | 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="139932059617152" timestamp=1716823372 total_threads=12 time=2024-05-27T23:22:52.874+08:00 level=INFO source=server.go:562 msg="waiting for server to become available" status="llm server error" INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="60783" tid="139932059617152" timestamp=1716823372 llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-010ec3ba94cb5ad2d9c8f95f46f01c6d80f83deab9df0a0831334ea45afff3e2 (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 = model llama_model_loader: - kv 2: llama.vocab_size u32 = 128256 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.block_count u32 = 32 llama_model_loader: - kv 6: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 7: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 8: llama.attention.head_count u32 = 32 llama_model_loader: - kv 9: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 10: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 11: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 12: general.file_type u32 = 15 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.scores arr[f32,128256] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128001 llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 128002 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 22: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 23: 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=2024-05-27T23:22:53.126+08:00 level=INFO source=server.go:562 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 = model llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128001 '<|end_of_text|>' llm_load_print_meta: UNK token = 128002 '' llm_load_print_meta: PAD token = 0 '!' llm_load_print_meta: LF token = 128 'Ä' llm_load_print_meta: EOT token = 128009 '<|eot_id|>' llm_load_tensors: ggml ctx size = 0.15 MiB llm_load_tensors: CPU buffer size = 4685.30 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: flash_attn = 0 llama_new_context_with_model: freq_base = 500000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 256.00 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: CPU output buffer size = 0.50 MiB llama_new_context_with_model: CPU compute buffer size = 258.50 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 1 INFO [main] model loaded | tid="139932059617152" timestamp=1716823373 time=2024-05-27T23:22:53.629+08:00 level=INFO source=server.go:567 msg="llama runner started in 0.76 seconds" [GIN] 2024/05/27 - 23:22:53 | 200 | 2.832875282s | 127.0.0.1 | POST "/api/chat" [GIN] 2024/05/27 - 23:22:55 | 200 | 98.939441ms | 127.0.0.1 | POST "/api/chat" time=2024-05-27T23:28:01.188+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=6.156460594 time=2024-05-27T23:28:02.666+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=7.634409778 time=2024-05-27T23:28:04.142+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=9.11041182 [GIN] 2024/05/27 - 23:29:13 | 200 | 6.560773ms | 127.0.0.1 | HEAD "/" [GIN] 2024/05/27 - 23:29:13 | 200 | 23.035463ms | 127.0.0.1 | GET "/api/tags"

OS

WSL2

GPU

Nvidia

CPU

AMD

Ollama version

0.0.0

What is the issue?

按doc文档获取git分支，编译成功后，通过modelfile生成模型，在进行对话时报错Error: an unknown error was encountered while running the model

以下是serve端报错日志 2024/05/27 23:22:34 routes.go:1028: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION: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=2024-05-27T23:22:34.090+08:00 level=INFO source=images.go:729 msg="total blobs: 8" time=2024-05-27T23:22:34.090+08:00 level=INFO source=images.go:736 msg="total unused blobs removed: 0" [GIN-debug] [WARNING] Creating an Engine instance with the Logger and Recovery middleware already attached.

[GIN-debug] [WARNING] Running in "debug" mode. Switch to "release" mode in production.

using env: export GIN_MODE=release

using code: gin.SetMode(gin.ReleaseMode)

[GIN-debug] POST /api/pull --> github.com/ollama/ollama/server.(Server).PullModelHandler-fm (5 handlers) [GIN-debug] POST /api/generate --> github.com/ollama/ollama/server.(Server).GenerateHandler-fm (5 handlers) [GIN-debug] POST /api/chat --> github.com/ollama/ollama/server.(Server).ChatHandler-fm (5 handlers) [GIN-debug] POST /api/embeddings --> github.com/ollama/ollama/server.(Server).EmbeddingsHandler-fm (5 handlers) [GIN-debug] POST /api/create --> github.com/ollama/ollama/server.(Server).CreateModelHandler-fm (5 handlers) [GIN-debug] POST /api/push --> github.com/ollama/ollama/server.(Server).PushModelHandler-fm (5 handlers) [GIN-debug] POST /api/copy --> github.com/ollama/ollama/server.(Server).CopyModelHandler-fm (5 handlers) [GIN-debug] DELETE /api/delete --> github.com/ollama/ollama/server.(Server).DeleteModelHandler-fm (5 handlers) [GIN-debug] POST /api/show --> github.com/ollama/ollama/server.(Server).ShowModelHandler-fm (5 handlers) [GIN-debug] POST /api/blobs/:digest --> github.com/ollama/ollama/server.(Server).CreateBlobHandler-fm (5 handlers) [GIN-debug] HEAD /api/blobs/:digest --> github.com/ollama/ollama/server.(Server).HeadBlobHandler-fm (5 handlers) [GIN-debug] GET /api/ps --> github.com/ollama/ollama/server.(Server).ProcessHandler-fm (5 handlers) [GIN-debug] POST /v1/chat/completions --> github.com/ollama/ollama/server.(Server).ChatHandler-fm (6 handlers) [GIN-debug] GET / --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func1 (5 handlers) [GIN-debug] GET /api/tags --> github.com/ollama/ollama/server.(Server).ListModelsHandler-fm (5 handlers) [GIN-debug] GET /api/version --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func2 (5 handlers) [GIN-debug] HEAD / --> github.com/ollama/ollama/server.(Server).GenerateRoutes.func1 (5 handlers) [GIN-debug] HEAD /api/tags --> github.com/ollama/ollama/server.(Server).ListModelsHandler-fm (5 handlers) [GIN-debug] HEAD /api/version --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers) time=2024-05-27T23:22:34.091+08:00 level=INFO source=routes.go:1074 msg="Listening on 127.0.0.1:11434 (version 0.0.0)" time=2024-05-27T23:22:34.091+08:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama3477894997/runners time=2024-05-27T23:22:34.166+08:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2]" time=2024-05-27T23:22:35.854+08:00 level=INFO source=types.go:71 msg="inference compute" id=GPU-a93d53d5-add0-d73c-9800-83ba35515332 library=cuda compute=8.6 driver=12.4 name="NVIDIA GeForce RTX 3060 Ti" total="8.0 GiB" available="7.0 GiB" [GIN] 2024/05/27 - 23:22:40 | 200 | 5.063803ms | 127.0.0.1 | HEAD "/" [GIN] 2024/05/27 - 23:22:40 | 200 | 2.428835ms | 127.0.0.1 | GET "/api/tags" [GIN] 2024/05/27 - 23:22:50 | 200 | 33.81µs | 127.0.0.1 | HEAD "/" [GIN] 2024/05/27 - 23:22:50 | 200 | 3.816911ms | 127.0.0.1 | POST "/api/show" [GIN] 2024/05/27 - 23:22:50 | 200 | 223.798µs | 127.0.0.1 | POST "/api/show"

time=2024-05-27T23:22:52.868+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="7.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" time=2024-05-27T23:22:52.868+08:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="7.0 GiB" memory.required.full="5.3 GiB" memory.required.partial="5.3 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" time=2024-05-27T23:22:52.869+08:00 level=INFO source=server.go:338 msg="starting llama server" cmd="/tmp/ollama3477894997/runners/cpu_avx2/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-010ec3ba94cb5ad2d9c8f95f46f01c6d80f83deab9df0a0831334ea45afff3e2 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 1 --port 60783" time=2024-05-27T23:22:52.869+08:00 level=INFO source=sched.go:338 msg="loaded runners" count=1 WARN [server_params_parse] 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 tid="139932059617152" timestamp=1716823372 INFO [main] build info | build=2994 commit="8541e996" tid="139932059617152" timestamp=1716823372 time=2024-05-27T23:22:52.871+08:00 level=INFO source=server.go:525 msg="waiting for llama runner to start responding" INFO [main] system info | n_threads=6 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | 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="139932059617152" timestamp=1716823372 total_threads=12 time=2024-05-27T23:22:52.874+08:00 level=INFO source=server.go:562 msg="waiting for server to become available" status="llm server error" INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="60783" tid="139932059617152" timestamp=1716823372 llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-010ec3ba94cb5ad2d9c8f95f46f01c6d80f83deab9df0a0831334ea45afff3e2 (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 = model llama_model_loader: - kv 2: llama.vocab_size u32 = 128256 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.block_count u32 = 32 llama_model_loader: - kv 6: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 7: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 8: llama.attention.head_count u32 = 32 llama_model_loader: - kv 9: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 10: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 11: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 12: general.file_type u32 = 15 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.scores arr[f32,128256] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128001 llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 128002 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 22: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 23: 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=2024-05-27T23:22:53.126+08:00 level=INFO source=server.go:562 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 = model llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128001 '<|end_of_text|>' llm_load_print_meta: UNK token = 128002 '' llm_load_print_meta: PAD token = 0 '!' llm_load_print_meta: LF token = 128 'Ä' llm_load_print_meta: EOT token = 128009 '<|eot_id|>' llm_load_tensors: ggml ctx size = 0.15 MiB llm_load_tensors: CPU buffer size = 4685.30 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: flash_attn = 0 llama_new_context_with_model: freq_base = 500000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 256.00 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: CPU output buffer size = 0.50 MiB llama_new_context_with_model: CPU compute buffer size = 258.50 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 1 INFO [main] model loaded | tid="139932059617152" timestamp=1716823373 time=2024-05-27T23:22:53.629+08:00 level=INFO source=server.go:567 msg="llama runner started in 0.76 seconds" [GIN] 2024/05/27 - 23:22:53 | 200 | 2.832875282s | 127.0.0.1 | POST "/api/chat" [GIN] 2024/05/27 - 23:22:55 | 200 | 98.939441ms | 127.0.0.1 | POST "/api/chat" time=2024-05-27T23:28:01.188+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=6.156460594 time=2024-05-27T23:28:02.666+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=7.634409778 time=2024-05-27T23:28:04.142+08:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=9.11041182 [GIN] 2024/05/27 - 23:29:13 | 200 | 6.560773ms | 127.0.0.1 | HEAD "/" [GIN] 2024/05/27 - 23:29:13 | 200 | 23.035463ms | 127.0.0.1 | GET "/api/tags"

OS

WSL2

GPU

Nvidia

CPU

AMD

Ollama version

0.0.0

有点奇怪。不过我修改了代码，或许你可以之后重新pull代码并重新尝试。 modelfile中模型的顺序有互换，记得修改。如果仍然有问题，可以随时issue里提，我会尽快回复。

OpenBMB / ollama