【大模型】大模型 CPU 推理之 llama.cpp
- llama.cpp
- 安装llama.cpp
- Memory/Disk Requirements
- Quantization
- 测试推理
- 下载模型
- 测试
- 参考
llama.cpp
-
描述
The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud.
- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2 and AVX512 support for x86 architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
- Vulkan, SYCL, and (partial) OpenCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
-
官网
https://github.com/ggerganov/llama.cpp -
Supported platforms:
Mac OSLinuxWindows (via CMake)DockerFreeBSD -
Supported models:
- Typically finetunes of the base models below are supported as well.
LLaMA 🦙
LLaMA 2 🦙🦙
Mistral 7B
Mixtral MoE
Falcon
Chinese LLaMA / Alpaca and Chinese LLaMA-2 / Alpaca-2
Vigogne (French)
Koala
Baichuan 1 & 2 + derivations
Aquila 1 & 2
Starcoder models
Refact
Persimmon 8B
MPT
Bloom
Yi models
StableLM models
Deepseek models
Qwen models
PLaMo-13B
Phi models
GPT-2
Orion 14B
InternLM2
CodeShell
Gemma
Mamba
Xverse
Command-R- Multimodal models:
LLaVA 1.5 models, LLaVA 1.6 models
BakLLaVA
Obsidian
ShareGPT4V
MobileVLM 1.7B/3B models
Yi-VL
安装llama.cpp
- 下载代码
git clone https://github.com/ggerganov/llama.cpp - Build
On Linux or MacOS:
其他编译方法参考官网https://github.com/ggerganov/llama.cppcd llama.cppmake
Memory/Disk Requirements
Quantization
测试推理
下载模型
快速下载模型，参考： 无需 VPN 即可急速下载 huggingface 上的 LLM 模型
我这里下 qwen/Qwen1.5-1.8B-Chat-GGUF 进行测试
huggingface-cli download --resume-download qwen/Qwen1.5-1.8B-Chat-GGUF --local-dir qwen/Qwen1.5-1.8B-Chat-GGUF
测试
cd ./llama.cpp./main -m /your/path/qwen/Qwen1.5-1.8B-Chat-GGUF/qwen1_5-1_8b-chat-q4_k_m.gguf -n 512 --color -i -cml -f ./prompts/chat-with-qwen.txt
需要修改提示语，可以编辑 ./prompts/chat-with-qwen.txt 进行修改。
加载模型输出信息：
llama.cpp# ./main -m /mnt/data/llm/Qwen1.5-1.8B-Chat-GGUF/qwen1_5-1_8b-chat-q4_k_m.gguf -n 512 --color -i -cml -f ./prompts/chat-with-qwen
.txt
Log start
main: build = 2527 (ad3a0505)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1711760850
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /mnt/data/llm/Qwen1.5-1.8B-Chat-GGUF/qwen1_5-1_8b-chat-q4_k_m.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 = qwen2
llama_model_loader: - kv 1: general.name str = Qwen1.5-1.8B-Chat-AWQ-fp16
llama_model_loader: - kv 2: qwen2.block_count u32 = 24
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 2048
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 5504
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 16
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 16
llama_model_loader: - kv 8: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 9: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 10: qwen2.use_parallel_residual bool = true
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 13: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 14: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 15: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 16: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 18: tokenizer.chat_template str = {% for message in messages %}{{'<|im_...
llama_model_loader: - kv 19: general.quantization_version u32 = 2
llama_model_loader: - kv 20: general.file_type u32 = 15
llama_model_loader: - type f32: 121 tensors
llama_model_loader: - type q5_0: 12 tensors
llama_model_loader: - type q8_0: 12 tensors
llama_model_loader: - type q4_K: 133 tensors
llama_model_loader: - type q6_K: 13 tensors
llm_load_vocab: special tokens definition check successful ( 293/151936 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 16
llm_load_print_meta: n_head_kv = 16
llm_load_print_meta: n_layer = 24
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 = 2048
llm_load_print_meta: n_embd_v_gqa = 2048
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 = 5504
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
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 = 1B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 1.84 B
llm_load_print_meta: model size = 1.13 GiB (5.28 BPW)
llm_load_print_meta: general.name = Qwen1.5-1.8B-Chat-AWQ-fp16
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: CPU buffer size = 1155.67 MiB
...................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 96.00 MiB
llama_new_context_with_model: KV self size = 96.00 MiB, K (f16): 48.00 MiB, V (f16): 48.00 MiB
llama_new_context_with_model: CPU output buffer size = 296.75 MiB
llama_new_context_with_model: CPU compute buffer size = 300.75 MiB
llama_new_context_with_model: graph nodes = 868
llama_new_context_with_model: graph splits = 1system_info: n_threads = 4 / 4 | AVX = 1 | AVX_VNNI = 1 | 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 = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
main: interactive mode on.
Reverse prompt: '<|im_start|>user
'
sampling:repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature
generate: n_ctx = 512, n_batch = 2048, n_predict = 512, n_keep = 10== Running in interactive mode. ==- Press Ctrl+C to interject at any time.- Press Return to return control to LLaMa.- To return control without starting a new line, end your input with '/'.- If you want to submit another line, end your input with '\'.system
You are a helpful assistant.
user>输入文本：What’s AI?
输出示例：
参考
- https://github.com/ggerganov/llama.cpp
![55、美国德克萨斯大学奥斯汀分校、钱德拉家族电气与计算机工程系：通过迁移学习解决BCI个体差异性[不得不说，看技术还得是老美]](https://img-blog.csdnimg.cn/direct/d55c19aedbd8410da48721eafdddbd7e.png)
