llama.cpp : インストール2024/02/22 |
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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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| [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。 |
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[root@dlp ~]# dnf -y install cudnn9-cuda-12 python3-pip python3-devel python3-numpy gcc gcc-c++ cmake ccache jq |
| [3] | [llama-cpp] をビルドします。 |
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[cent@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.
[cent@dlp ~]$
cd llama.cpp [cent@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 |
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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 |
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[cent@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 [cent@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 [cent@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":1708584995,"level":"INFO","function":"main","line":2573,"message":"build info","build":2234,"commit":"973053d8"}
{"timestamp":1708584995,"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":1708584995,"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":1708584999,"level":"INFO","function":"main","line":2752,"message":"model loaded"}
all slots are idle and system prompt is empty, clear the KV cache
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| [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。 |
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# 東京証券取引所における日経平均株価の最高値は？ [cent@dlp llama.cpp]$ curl -s -XPOST -H 'Content-Type: application/json' localhost:8000/v1/chat/completions \ -d '{"messages": [{"role": "user", "content": "What is the highest price of the Nikkei Stock Average on the Tokyo Stock Exchange?"}]}' | jq | sed -e 's/\\n/\n/g'
print_timings: prompt eval time = 3638.34 ms / 47 tokens ( 77.41 ms per token, 12.92 tokens per second)
print_timings: eval time = 64947.79 ms / 102 runs ( 636.74 ms per token, 1.57 tokens per second)
print_timings: total time = 68586.13 ms
slot 0 released (149 tokens in cache)
{"timestamp":1708585483,"level":"INFO","function":"log_server_request","line":2510,"message":"request","remote_addr":"127.0.0.1","remote_port":59018,"status":200,"method":"POST","path":"/v1/chat/completions","params":{}}
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "The highest price of the Nikkei Stock Average on the Tokyo Stock Exchange was 38,915.47 on December 29, 1989. However, please note that this is a historical data point and may not reflect current market conditions or future performance. It's important to do your own research and consult with a financial advisor before making any investment decisions. Is there anything else I can help you with?",
"role": "assistant"
}
}
],
"created": 1708585483,
"id": "chatcmpl-Lk7RjQkTHFIdrueDfIIW7g8P7CaE8jh8",
"model": "unknown",
"object": "chat.completion",
"usage": {
"completion_tokens": 102,
"prompt_tokens": 47,
"total_tokens": 149
}
}
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