llama.cpp : インストール2024/02/22 |
Meta の Llama (Large Language Model Meta AI) モデルのインターフェースである [llama.cpp] をインストールします。 |
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[1] | |
[2] | その他必要なパッケージをインストールしておきます。 |
root@dlp:~# apt -y install nvidia-cudnn gcc g++ cmake ccache python3-numpy curl git jq |
[3] | [llama-cpp] をビルドします。 |
debian@dlp:~$ git clone https://github.com/ggerganov/llama.cpp Cloning into 'llama.cpp'... remote: Enumerating objects: 18978, done. remote: Counting objects: 100% (6489/6489), done. remote: Compressing objects: 100% (540/540), done. remote: Total 18978 (delta 6260), reused 5989 (delta 5947), pack-reused 12489 Receiving objects: 100% (18978/18978), 21.49 MiB | 19.02 MiB/s, done. Resolving deltas: 100% (13356/13356), done.
debian@dlp:~$
cd llama.cpp debian@dlp:~/llama.cpp$ make LLAMA_CUBLAS=1
# * CPU のみで実行するバイナリをビルドする場合はオプション無しの [make] のみで実行 |
[4] |
GGML 形式のモデルをダウンロードして GGUF 形式に変換し、[llama-cpp] を起動します。 ⇒ https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/tree/main ⇒ https://huggingface.co/TheBloke/Llama-2-70B-Chat-GGML/tree/main |
debian@dlp:~/llama.cpp$
curl -LO https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q8_0.bin?download=true
# GGUF に変換 debian@dlp:~/llama.cpp$ python3 ./convert-llama-ggml-to-gguf.py --input ./llama-2-13b-chat.ggmlv3.q8_0.bin --output ./llama-2-13b-chat.ggmlv3.q8_0.gguf
..... ..... * Preparing to save GGUF file gguf: This GGUF file is for Little Endian only * Adding model parameters and KV items * Adding 32000 vocab item(s) * Adding 363 tensor(s) gguf: write header gguf: write metadata gguf: write tensors * Successful completion. Output saved to: llama-2-13b-chat.ggmlv3.q8_0.gguf # [--n-gpu-layers] : GPU に配置するレイヤーの数 # -- よくわからない場合は [-1] を指定 debian@dlp:~/llama.cpp$ ./server --model ./llama-2-13b-chat.ggmlv3.q8_0.gguf --n-gpu-layers -1 --host 0.0.0.0 --port 8000 & ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes {"timestamp":1708566953,"level":"INFO","function":"main","line":2573,"message":"build info","build":2234,"commit":"973053d8"} {"timestamp":1708566953,"level":"INFO","function":"main","line":2576,"message":"system info","n_threads":4,"n_threads_batch":-1,"total_threads":8,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | "} llama server listening at http://0.0.0.0:8000 {"timestamp":1708566953,"level":"INFO","function":"main","line":2731,"message":"HTTP server listening","port":"8000","hostname":"0.0.0.0"} llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from ./llama-2-13b-chat.ggmlv3.q8_0.gguf (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 = llama-2-13b-chat.ggmlv3.q8_0.bin llama_model_loader: - kv 2: general.description str = converted from legacy GGJTv3 MOSTLY_Q... llama_model_loader: - kv 3: general.file_type u32 = 7 llama_model_loader: - kv 4: llama.context_length u32 = 2048 llama_model_loader: - kv 5: llama.embedding_length u32 = 5120 llama_model_loader: - kv 6: llama.block_count u32 = 40 llama_model_loader: - kv 7: llama.feed_forward_length u32 = 13824 llama_model_loader: - kv 8: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 9: llama.attention.head_count u32 = 40 llama_model_loader: - kv 10: llama.attention.head_count_kv u32 = 40 llama_model_loader: - kv 11: llama.attention.layer_norm_rms_epsilon f32 = 0.000005 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q8_0: 282 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 = 2048 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 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 = 1 llm_load_print_meta: n_embd_k_gqa = 5120 llm_load_print_meta: n_embd_v_gqa = 5120 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 5.0e-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: n_ff = 13824 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 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 = 2048 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = Q8_0 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 12.88 GiB (8.50 BPW) llm_load_print_meta: general.name = llama-2-13b-chat.ggmlv3.q8_0.bin 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>' llm_load_tensors: ggml ctx size = 0.14 MiB llm_load_tensors: offloading 0 repeating layers to GPU llm_load_tensors: offloaded 0/41 layers to GPU llm_load_tensors: CPU buffer size = 13189.86 MiB .................................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA_Host KV buffer size = 400.00 MiB llama_new_context_with_model: KV self size = 400.00 MiB, K (f16): 200.00 MiB, V (f16): 200.00 MiB llama_new_context_with_model: CUDA_Host input buffer size = 12.01 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 80.00 MiB llama_new_context_with_model: graph splits (measure): 1 Available slots: -> Slot 0 - max context: 512 {"timestamp":1708566955,"level":"INFO","function":"main","line":2752,"message":"model loaded"} all slots are idle and system prompt is empty, clear the KV cache |
[5] | 簡単な質問を投入して動作確認します。 質問の内容や使用しているモデルによって、応答時間や応答内容は変わります。 ちなみに、当例では、8 vCPU + 16G メモリ + GeForce RTX 3060 (12G) のマシンで実行しています。 |
# 球の体積を求める公式を教えて debian@dlp:~/llama.cpp$ curl -s -XPOST -H 'Content-Type: application/json' localhost:8000/v1/chat/completions \ -d '{"messages": [{"role": "user", "content": "Tell me the formula to calculate the volume of a sphere."}]}' | jq | sed -e 's/\\n/\n/g' print_timings: prompt eval time = 3530.30 ms / 40 tokens ( 88.26 ms per token, 11.33 tokens per second) print_timings: eval time = 50448.40 ms / 79 runs ( 638.59 ms per token, 1.57 tokens per second) print_timings: total time = 53978.71 ms slot 0 released (119 tokens in cache) {"timestamp":1708568316,"level":"INFO","function":"log_server_request","line":2510,"message":"request","remote_addr":"127.0.0.1","remote_port":38516,"status":200,"method":"POST","path":"/v1/chat/completions","params":{}} { "choices": [ { "finish_reason": "stop", "index": 0, "message": { "content": "Certainly! The formula to calculate the volume of a sphere is: V = (4/3)πr³ Where V is the volume of the sphere, and r is the radius of the sphere. Would you like me to explain how this formula works or would you like to try solving some problems using it?", "role": "assistant" } } ], "created": 1708568316, "id": "chatcmpl-bxLY6rzxnoAKYraeTLAgDu8OQxngdNyA", "model": "unknown", "object": "chat.completion", "usage": { "completion_tokens": 79, "prompt_tokens": 40, "total_tokens": 119 } } |
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