llama.cpp : Install2024/02/22 |
Install [llama.cpp] taht is the interface for Meta's Llama (Large Language Model Meta AI) model. |
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[1] | |
[2] | Install other required packages. |
[root@dlp ~]# dnf -y install cudnn9-cuda-12 python3-pip python3-devel python3-numpy gcc gcc-c++ cmake ccache jq |
[3] | Build [llama-cpp]. |
[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
# * If you want to build a binary that runs only on the CPU, run only [make] without options |
[4] |
Download the GGML format model and convert it to GGUF format. ⇒ https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/tree/main ⇒ https://huggingface.co/TheBloke/Llama-2-70B-Chat-GGML/tree/main |
[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
# convert to GGUF format [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] : number of layers to put on the GPU # -- specify [-1] to use all if you do not know [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 |
[5] | Post some questions like follows and verify it works normally. The response time and response contents will vary depending on the question and the model used. By the way, this example is running on a machine with 8 vCPU + 16G memory + GeForce RTX 3060 (12G). |
[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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