LocalでLLMを試すのにOllamaが人気です。Spring AIにはOllama用のChat Clientが用意されていますが、OllamaにはOpenAI API互換APIも用意されているので、OpenAIへの切り替えも想定して、OpenAI用のChat Clientを使ってOllamaにアクセスしてみます。
Warning
Spring AIのOpenAI Clientは1.0.0-M1時点で、Ollama含む多くの互換APIプロバイダーが実装していないAPIを使用しています。 こちらのコミットで使用するAPIが修正されたため、本記事ではSpring AI 1.0.0-SNAPSHOTを使用しています。
目次
Ollamaのインストール
brew install ollama
$ ollama -v
Warning: could not connect to a running Ollama instance
Warning: client version is 0.1.41
Ollamaの起動
$ ollama serve
2024/06/07 11:31:36 routes.go:1007: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST: OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS: OLLAMA_NOHISTORY:false 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=2024-06-07T11:31:36.080+09:00 level=INFO source=images.go:729 msg="total blobs: 0"
time=2024-06-07T11:31:36.081+09:00 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=2024-06-07T11:31:36.081+09:00 level=INFO source=routes.go:1053 msg="Listening on 127.0.0.1:11434 (version 0.1.41)"
time=2024-06-07T11:31:36.087+09:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/var/folders/6p/vxhp1wpj2mq5w8drct9k8t4w0000gq/T/ollama895778410/runners
time=2024-06-07T11:31:36.108+09:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [metal]"
time=2024-06-07T11:31:36.132+09:00 level=INFO source=types.go:71 msg="inference compute" id=0 library=metal compute="" driver=0.0 name="" total="21.3 GiB" available="21.3 GiB"
Gemmaモデルを使用
ollama pull gemma:2b
$ ollama ls
NAME ID SIZE MODIFIED
gemma:2b b50d6c999e59 1.7 GB 2 minutes ago
curlでOpenAI APIにアクセス。
curl -s http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gemma:2b",
"messages": [
{"role": "user", "content": "Give me a joke."}
]
}' | jq .
{
"id": "chatcmpl-838",
"object": "chat.completion",
"created": 1717727879,
"model": "gemma:2b",
"system_fingerprint": "fp_ollama",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Why did the scarecrow win an award?\n\nBecause he was outstanding in his field!"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 31,
"completion_tokens": 19,
"total_tokens": 50
}
}
今回はApple SiliconのMac上でOllamaを実行しており、ggml_metal_init: found device: Apple M2 Proと検出され、Metalが使用されていることがわかります。
time=2024-06-07T11:37:51.481+09:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.3 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="504.2 MiB"
time=2024-06-07T11:37:51.481+09:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.3 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="504.2 MiB"
time=2024-06-07T11:37:51.482+09:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/var/folders/6p/vxhp1wpj2mq5w8drct9k8t4w0000gq/T/ollama895778410/runners/metal/ollama_llama_server --model /Users/tmaki/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 19 --parallel 1 --port 52102"
time=2024-06-07T11:37:51.485+09:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-07T11:37:51.485+09:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-07T11:37:51.486+09:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=3051 commit="5921b8f0" tid="0x1f239fac0" timestamp=1717727871
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="0x1f239fac0" timestamp=1717727871 total_threads=12
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="52102" tid="0x1f239fac0" timestamp=1717727871
llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from /Users/tmaki/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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 = gemma
llama_model_loader: - kv 1: general.name str = gemma-2b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.block_count u32 = 18
llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1
llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3
time=2024-06-07T11:37:51.737+09:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: general.quantization_version u32 = 2
llama_model_loader: - kv 20: general.file_type u32 = 2
llama_model_loader: - type f32: 37 tensors
llama_model_loader: - type q4_0: 126 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: special tokens cache size = 388
llm_load_vocab: token to piece cache size = 3.2028 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 8
llm_load_print_meta: n_head_kv = 1
llm_load_print_meta: n_layer = 18
llm_load_print_meta: n_rot = 256
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 16384
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 = 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_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 = 2B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 2.51 B
llm_load_print_meta: model size = 1.56 GiB (5.34 BPW)
llm_load_print_meta: general.name = gemma-2b-it
llm_load_print_meta: BOS token = 2 '<bos>'
llm_load_print_meta: EOS token = 1 '<eos>'
llm_load_print_meta: UNK token = 3 '<unk>'
llm_load_print_meta: PAD token = 0 '<pad>'
llm_load_print_meta: LF token = 227 '<0x0A>'
llm_load_print_meta: EOT token = 107 '<end_of_turn>'
llm_load_tensors: ggml ctx size = 0.17 MiB
ggml_backend_metal_log_allocated_size: allocated buffer, size = 1594.95 MiB, ( 1595.02 / 21845.34)
llm_load_tensors: offloading 18 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 19/19 layers to GPU
llm_load_tensors: CPU buffer size = 531.52 MiB
llm_load_tensors: Metal buffer size = 1594.94 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 = 10000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2 Pro
ggml_metal_init: picking default device: Apple M2 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M2 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 22906.50 MB
llama_kv_cache_init: Metal KV buffer size = 36.00 MiB
llama_new_context_with_model: KV self size = 36.00 MiB, K (f16): 18.00 MiB, V (f16): 18.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.98 MiB
llama_new_context_with_model: Metal compute buffer size = 504.25 MiB
llama_new_context_with_model: CPU compute buffer size = 8.01 MiB
llama_new_context_with_model: graph nodes = 601
llama_new_context_with_model: graph splits = 2
INFO [main] model loaded | tid="0x1f239fac0" timestamp=1717727878
time=2024-06-07T11:37:59.022+09:00 level=INFO source=server.go:572 msg="llama runner started in 7.54 seconds"
[GIN] 2024/06/07 - 11:37:59 | 200 | 8.456048209s | 127.0.0.1 | POST "/v1/chat/completions"
Llama3モデルの使用
ollama pull llama3:8b
$ ollama ls
NAME ID SIZE MODIFIED
llama3:8b 365c0bd3c000 4.7 GB 1 second ago
gemma:2b b50d6c999e59 1.7 GB 9 minutes ago
curl -s http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3:8b",
"messages": [
{"role": "user", "content": "Give me a joke."}
]
}' | jq .
{
"id": "chatcmpl-658",
"object": "chat.completion",
"created": 1717728179,
"model": "llama3:8b",
"system_fingerprint": "fp_ollama",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Here's one:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!\n\nHope that made you giggle!"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 14,
"completion_tokens": 25,
"total_tokens": 39
}
}
time=2024-06-07T11:42:53.295+09:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="21.3 GiB" memory.required.full="5.1 GiB" memory.required.partial="5.1 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="164.0 MiB"
time=2024-06-07T11:42:53.295+09:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=33 memory.available="21.3 GiB" memory.required.full="5.1 GiB" memory.required.partial="5.1 GiB" memory.required.kv="256.0 MiB" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="164.0 MiB"
time=2024-06-07T11:42:53.296+09:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/var/folders/6p/vxhp1wpj2mq5w8drct9k8t4w0000gq/T/ollama895778410/runners/metal/ollama_llama_server --model /Users/tmaki/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 1 --port 52386"
time=2024-06-07T11:42:53.298+09:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-07T11:42:53.298+09:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-07T11:42:53.298+09:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=3051 commit="5921b8f0" tid="0x1f239fac0" timestamp=1717728173
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="0x1f239fac0" timestamp=1717728173 total_threads=12
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="52386" tid="0x1f239fac0" timestamp=1717728173
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /Users/tmaki/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Meta-Llama-3-8B-Instruct
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 8192
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-06-07T11:42:53.549+09:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 1.5928 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: 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_0
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.33 GiB (4.64 BPW)
llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_tensors: ggml ctx size = 0.30 MiB
ggml_backend_metal_log_allocated_size: allocated buffer, size = 4156.00 MiB, ( 4156.06 / 21845.34)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CPU buffer size = 281.81 MiB
llm_load_tensors: Metal buffer size = 4156.00 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
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2 Pro
ggml_metal_init: picking default device: Apple M2 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M2 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 22906.50 MB
llama_kv_cache_init: Metal 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: Metal compute buffer size = 258.50 MiB
llama_new_context_with_model: CPU compute buffer size = 12.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 2
INFO [main] model loaded | tid="0x1f239fac0" timestamp=1717728178
time=2024-06-07T11:42:58.572+09:00 level=INFO source=server.go:572 msg="llama runner started in 5.27 seconds"
[GIN] 2024/06/07 - 11:42:59 | 200 | 6.870645292s | 127.0.0.1 | POST "/v1/chat/completions"
Spring AIでOllamaにアクセス
Spring AIを使ったアプリからアクセスしてみます。
OpenAI互換なので、Spring AIのOpenAI用のChat Clientが利用できます。
サンプルアプリはこちらです。
https://github.com/making/hello-spring-ai
このアプリはOpenAI APIを使用する想定になっていますが、次のプロパティを変更することでコードはそのままでOpenAIの代わりにOllamaへアクセスできます。
spring.ai.openai.base-urlspring.ai.openai.api-keyspring.ai.openai.chat.options.model
git clone https://github.com/making/hello-spring-ai
cd hello-spring-ai
./mvnw clean package -DskipTests=true
java -jar target/hello-spring-ai-0.0.1-SNAPSHOT.jar --spring.ai.openai.base-url=http://localhost:11434 --spring.ai.openai.api-key=dummy --spring.ai.openai.chat.options.model=llama3:8b
$ curl localhost:8080
Here's one:
Why couldn't the bicycle stand up by itself?
(wait for it...)
Because it was two-tired!
Hope that brought a smile to your face! Do you want another one?
アプリ自体はOpenAI向けでもプロパティを変えるだけでOllama経由で様々なモデルを扱えるのが今回の方式の利点です。
OpenAI APIとの互換性は気にせず、Ollamaを使いたいだけであれば、spring-ai-ollama経由でOllamaを使うこともできますが、
OpenAIにスイッチすることも想定するとOpenAI APIを使ったほうが良いのではないかと筆者は考えます。
同様のことはllama-cpp-pythonでも行えましたが、Ollamaの方がモデルのダウンロードが簡単でした。
一方、llama-cpp-pythonではFunction Callingが使えるというメリットがあります。