当前位置：首页 > news >正文

使用llama.cpp实现LLM大模型的格式转换、量化、推理、部署

news 2025/9/12 15:05:43

使用llama.cpp实现LLM大模型的格式转换、量化、推理、部署

概述

llama.cpp的主要目标是能够在各种硬件上实现LLM推理，只需最少的设置，并提供最先进的性能。提供1.5位、2位、3位、4位、5位、6位和8位整数量化，以加快推理速度并减少内存使用。

GitHub：https://github.com/ggerganov/llama.cpp

克隆和编译

克隆最新版llama.cpp仓库代码

python复制代码git clone https://github.com/ggerganov/llama.cpp

对llama.cpp项目进行编译，在目录下会生成一系列可执行文件

css复制代码main：使用模型进行推理quantize：量化模型server：提供模型API服务

1.编译构建CPU执行环境，安装简单，适用于没有GPU的操作系统

python复制代码cd llama.cppmkdir

2.编译构建GPU执行环境，确保安装CUDA工具包，适用于有GPU的操作系统

如果CUDA设置正确，那么执行nvidia-smi、nvcc --version没有错误提示，则表示一切设置正确。

python复制代码make clean &&  make LLAMA_CUDA=1

3.如果编译失败或者需要重新编译，可尝试清理并重新编译，直至编译成功

python复制代码make clean

😝有需要的小伙伴，可以V扫描下方二维码免费领取🆓

## 环境准备

1.下载受支持的模型

要使用llamma.cpp，首先需要准备它支持的模型。在官方文档中给出了说明，这里仅仅截取其中一部分

在这里插入图片描述 2.安装依赖

llama.cpp项目下带有requirements.txt 文件，直接安装依赖即可。

python复制代码pip install -r requirements.txt

模型格式转换

根据模型架构，可以使用convert.py或convert-hf-to-gguf.py文件。

转换脚本读取模型配置、分词器、张量名称+数据，并将它们转换为GGUF元数据和张量。

GGUF格式

Llama-3相比其前两代显著扩充了词表大小，由32K扩充至128K，并且改为BPE词表。因此需要使用--vocab-type参数指定分词算法，默认值是spm，如果是bpe，需要显示指定

注意：

官方文档说convert.py不支持LLaMA 3，喊使用convert-hf-to-gguf.py，但它不支持--vocab-type，且出现异常：error: unrecognized arguments: --vocab-type bpe，因此使用convert.py且没出问题

使用llama.cpp项目中的convert.py脚本转换模型为GGUF格式

python复制代码root@master:~/work/llama.cpp# python3 ./convert.py  /root/work/models/Llama3-Chinese-8B-Instruct/ --outtype f16 --vocab-type bpe --outfile ./models/Llama3-FP16.gguf
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00001-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00001-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00002-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00003-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00004-of-00004.safetensors
INFO:convert:model parameters count : 8030261248 (8B)
INFO:convert:params = Params(n_vocab=128256, n_embd=4096, n_layer=32, n_ctx=8192, n_ff=14336, n_head=32, n_head_kv=8, n_experts=None, n_experts_used=None, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=500000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=<GGMLFileType.MostlyF16: 1>, path_model=PosixPath('/root/work/models/Llama3-Chinese-8B-Instruct'))
INFO:convert:Loaded vocab file PosixPath('/root/work/models/Llama3-Chinese-8B-Instruct/tokenizer.json'), type 'bpe'
INFO:convert:Vocab info: <BpeVocab with 128000 base tokens and 256 added tokens>
INFO:convert:Special vocab info: <SpecialVocab with 280147 merges, special tokens {'bos': 128000, 'eos': 128001}, add special tokens unset>
INFO:convert:Writing models/Llama3-FP16.gguf, format 1
WARNING:convert:Ignoring added_tokens.json since model matches vocab size without it.
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:gguf.vocab:Adding 280147 merge(s).
INFO:gguf.vocab:Setting special token type bos to 128000
INFO:gguf.vocab:Setting special token type eos to 128001
INFO:gguf.vocab:Setting chat_template to {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>' }}
INFO:convert:[  1/291] Writing tensor token_embd.weight                      | size 128256 x   4096  | type F16  | T+   1
INFO:convert:[  2/291] Writing tensor blk.0.attn_norm.weight                 | size   4096           | type F32  | T+   2
INFO:convert:[  3/291] Writing tensor blk.0.ffn_down.weight                  | size   4096 x  14336  | type F16  | T+   2
INFO:convert:[  4/291] Writing tensor blk.0.ffn_gate.weight                  | size  14336 x   4096  | type F16  | T+   2
INFO:convert:[  5/291] Writing tensor blk.0.ffn_up.weight                    | size  14336 x   4096  | type F16  | T+   2
INFO:convert:[  6/291] Writing tensor blk.0.ffn_norm.weight                  | size   4096           | type F32  | T+   2
INFO:convert:[  7/291] Writing tensor blk.0.attn_k.weight                    | size   1024 x   4096  | type F16  | T+   2
INFO:convert:[  8/291] Writing tensor blk.0.attn_output.weight               | size   4096 x   4096  | type F16  | T+   2
INFO:convert:[  9/291] Writing tensor blk.0.attn_q.weight                    | size   4096 x   4096  | type F16  | T+   3
INFO:convert:[ 10/291] Writing tensor blk.0.attn_v.weight                    | size   1024 x   4096  | type F16  | T+   3
INFO:convert:[ 11/291] Writing tensor blk.1.attn_norm.weight                 | size   4096           | type F32  | T+   3

转换为FP16的GGUF格式，模型体积大概15G。

python复制代码root@master:~/work/llama.cpp# ll models -h
-rw-r--r--  1 root root  15G May 17 07:47 Llama3-FP16.gguf

bin格式

python复制代码root@master:~/work/llama.cpp# python3 ./convert.py  /root/work/models/Llama3-Chinese-8B-Instruct/ --outtype f16 --vocab-type bpe --outfile ./models/Llama3-FP16.bin
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00001-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00001-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00002-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00003-of-00004.safetensors
INFO:convert:Loading model file /root/work/models/Llama3-Chinese-8B-Instruct/model-00004-of-00004.safetensors
INFO:convert:model parameters count : 8030261248 (8B)
INFO:convert:params = Params(n_vocab=128256, n_embd=4096, n_layer=32, n_ctx=8192, n_ff=14336, n_head=32, n_head_kv=8, n_experts=None, n_experts_used=None, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=500000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=<GGMLFileType.MostlyF16: 1>, path_model=PosixPath('/root/work/models/Llama3-Chinese-8B-Instruct'))
INFO:convert:Loaded vocab file PosixPath('/root/work/models/Llama3-Chinese-8B-Instruct/tokenizer.json'), type 'bpe'
INFO:convert:Vocab info: <BpeVocab with 128000 base tokens and 256 added tokens>
INFO:convert:Special vocab info: <SpecialVocab with 280147 merges, special tokens {'bos': 128000, 'eos': 128001}, add special tokens unset>
INFO:convert:Writing models/Llama3-FP16.bin, format 1
WARNING:convert:Ignoring added_tokens.json since model matches vocab size without it.
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:gguf.vocab:Adding 280147 merge(s).
INFO:gguf.vocab:Setting special token type bos to 128000
INFO:gguf.vocab:Setting special token type eos to 128001
INFO:gguf.vocab:Setting chat_template to {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>' }}
INFO:convert:[  1/291] Writing tensor token_embd.weight                      | size 128256 x   4096  | type F16  | T+   4
INFO:convert:[  2/291] Writing tensor blk.0.attn_norm.weight                 | size   4096           | type F32  | T+   4
INFO:convert:[  3/291] Writing tensor blk.0.ffn_down.weight                  | size   4096 x  14336  | type F16  | T+   4
INFO:convert:[  4/291] Writing tensor blk.0.ffn_gate.weight                  | size  14336 x   4096  | type F16  | T+   5
INFO:convert:[  5/291] Writing tensor blk.0.ffn_up.weight                    | size  14336 x   4096  | type F16  | T+   5
INFO:convert:[  6/291] Writing tensor blk.0.ffn_norm.weight                  | size   4096           | type F32  | T+   5
INFO:convert:[  7/291] Writing tensor blk.0.attn_k.weight                    | size   1024 x   4096  | type F16  | T+   5
INFO:convert:[  8/291] Writing tensor blk.0.attn_output.weight               | size   4096 x   4096  | type F16  | T+   5
INFO:convert:[  9/291] Writing tensor blk.0.attn_q.weight                    | size   4096 x   4096  | type F16  | T+   5
INFO:convert:[ 10/291] Writing tensor blk.0.attn_v.weight                    | size   1024 x   4096  | type F16  | T+   5
INFO:convert:[ 11/291] Writing tensor blk.1.attn_norm.weight                 | size   4096           | type F32  | T+   5
INFO:convert:[ 12/291] Writing tensor blk.1.ffn_down.weight                  | size   4096 x  14336  | type F16  | T+   5
INFO:convert:[ 13/291] Writing tensor blk.1.ffn_gate.weight                  | size  14336 x   4096  | type F16  | T+   5
python复制代码root@master:~/work/llama.cpp# ll models -h
-rw-r--r--  1 root root  15G May 17 07:47 Llama3-FP16.gguf
-rw-r--r--  1 root root  15G May 17 08:02 Llama3-FP16.bin

模型量化

模型量化使用quantize命令，其具体可用参数与允许量化的类型如下：

python复制代码root@master:~/work/llama.cpp# ./quantize
usage: ./quantize [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]--allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit--leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing--pure: Disable k-quant mixtures and quantize all tensors to the same type--imatrix file_name: use data in file_name as importance matrix for quant optimizations--include-weights tensor_name: use importance matrix for this/these tensor(s)--exclude-weights tensor_name: use importance matrix for this/these tensor(s)--output-tensor-type ggml_type: use this ggml_type for the output.weight tensor--token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor--keep-split: will generate quatized model in the same shards as input  --override-kv KEY=TYPE:VALUEAdvanced option to override model metadata by key in the quantized model. May be specified multiple times.
Note: --include-weights and --exclude-weights cannot be used togetherAllowed quantization types:2  or  Q4_0    :  3.56G, +0.2166 ppl @ LLaMA-v1-7B3  or  Q4_1    :  3.90G, +0.1585 ppl @ LLaMA-v1-7B8  or  Q5_0    :  4.33G, +0.0683 ppl @ LLaMA-v1-7B9  or  Q5_1    :  4.70G, +0.0349 ppl @ LLaMA-v1-7B19  or  IQ2_XXS :  2.06 bpw quantization20  or  IQ2_XS  :  2.31 bpw quantization28  or  IQ2_S   :  2.5  bpw quantization29  or  IQ2_M   :  2.7  bpw quantization24  or  IQ1_S   :  1.56 bpw quantization31  or  IQ1_M   :  1.75 bpw quantization10  or  Q2_K    :  2.63G, +0.6717 ppl @ LLaMA-v1-7B21  or  Q2_K_S  :  2.16G, +9.0634 ppl @ LLaMA-v1-7B23  or  IQ3_XXS :  3.06 bpw quantization26  or  IQ3_S   :  3.44 bpw quantization27  or  IQ3_M   :  3.66 bpw quantization mix12  or  Q3_K    : alias for Q3_K_M22  or  IQ3_XS  :  3.3 bpw quantization11  or  Q3_K_S  :  2.75G, +0.5551 ppl @ LLaMA-v1-7B12  or  Q3_K_M  :  3.07G, +0.2496 ppl @ LLaMA-v1-7B13  or  Q3_K_L  :  3.35G, +0.1764 ppl @ LLaMA-v1-7B25  or  IQ4_NL  :  4.50 bpw non-linear quantization30  or  IQ4_XS  :  4.25 bpw non-linear quantization15  or  Q4_K    : alias for Q4_K_M14  or  Q4_K_S  :  3.59G, +0.0992 ppl @ LLaMA-v1-7B15  or  Q4_K_M  :  3.80G, +0.0532 ppl @ LLaMA-v1-7B17  or  Q5_K    : alias for Q5_K_M16  or  Q5_K_S  :  4.33G, +0.0400 ppl @ LLaMA-v1-7B17  or  Q5_K_M  :  4.45G, +0.0122 ppl @ LLaMA-v1-7B18  or  Q6_K    :  5.15G, +0.0008 ppl @ LLaMA-v1-7B7  or  Q8_0    :  6.70G, +0.0004 ppl @ LLaMA-v1-7B1  or  F16     : 14.00G, -0.0020 ppl @ Mistral-7B32  or  BF16    : 14.00G, -0.0050 ppl @ Mistral-7B0  or  F32     : 26.00G              @ 7BCOPY    : only copy tensors, no quantizing

使用quantize量化模型，它提供各种量化位数的模型：Q2、Q3、Q4、Q5、Q6、Q8、F16。

量化模型的命名方法遵循: Q + 量化比特位 + 变种。量化位数越少，对硬件资源的要求越低，但是模型的精度也越低。

模型经过量化之后，可以发现模型的大小从15G降低到8G，但模型精度从16位浮点数降低到8位整数。

python复制代码root@master:~/work/llama.cpp# ./quantize ./models/Llama3-FP16.gguf  ./models/Llama3-q8.gguf q8_0
main: build = 2908 (359cbe3f)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: quantizing '/root/work/models/Llama3-FP16.gguf' to '/root/work/models/Llama3-q8.gguf' as Q8_0
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /root/work/models/Llama3-FP16.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              = Llama3-Chinese-8B-Instruct
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 128256
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.block_count u32              = 32
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   8:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   9:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  10:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  11:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  12:                          general.file_type u32              = 1
llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,128256]  = [0.000000, 0.000000, 0.000000, 0.0000...
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              = 128001
llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type  f16:  226 tensors
[   1/ 291]                    token_embd.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q8_0 .. size =  1002.00 MiB ->   532.31 MiB
[   2/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB
[   3/ 291]                blk.0.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[   4/ 291]                blk.0.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[   5/ 291]                  blk.0.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[   6/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB
[   7/ 291]                  blk.0.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB
[   8/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB
[   9/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB
[  10/ 291]                  blk.0.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB
[  11/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB
[  12/ 291]                blk.1.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[  13/ 291]                blk.1.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[  14/ 291]                  blk.1.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q8_0 .. size =   112.00 MiB ->    59.50 MiB
[  15/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB
[  16/ 291]                  blk.1.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB
[  17/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB
[  18/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB
[  19/ 291]                  blk.1.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB
python复制代码root@master:~/work/llama.cpp# ll -h models/
-rw-r--r--  1 root root 8.0G May 17 07:54 Llama3-q8.gguf

模型加载与推理

模型加载与推理使用main命令，其支持如下可用参数：

python复制代码root@master:~/work/llama.cpp# ./main -husage: ./main [options]options:-h, --help            show this help message and exit--version             show version and build info-i, --interactive     run in interactive mode--interactive-specials allow special tokens in user text, in interactive mode--interactive-first   run in interactive mode and wait for input right away-cnv, --conversation  run in conversation mode (does not print special tokens and suffix/prefix)-ins, --instruct      run in instruction mode (use with Alpaca models)-cml, --chatml        run in chatml mode (use with ChatML-compatible models)--multiline-input     allows you to write or paste multiple lines without ending each in '\'-r PROMPT, --reverse-prompt PROMPThalt generation at PROMPT, return control in interactive mode(can be specified more than once for multiple prompts).--color               colorise output to distinguish prompt and user input from generations-s SEED, --seed SEED  RNG seed (default: -1, use random seed for < 0)-t N, --threads N     number of threads to use during generation (default: 30)-tb N, --threads-batch Nnumber of threads to use during batch and prompt processing (default: same as --threads)-td N, --threads-draft N                        number of threads to use during generation (default: same as --threads)-tbd N, --threads-batch-draft Nnumber of threads to use during batch and prompt processing (default: same as --threads-draft)-p PROMPT, --prompt PROMPTprompt to start generation with (default: empty)

可以加载预训练模型或者经过量化之后的模型，这里选择加载量化后的模型进行推理。

在llama.cpp项目的根目录，执行如下命令，加载模型进行推理。

python复制代码root@master:~/work/llama.cpp# ./main -m models/Llama3-q8.gguf --color -f prompts/alpaca.txt -ins -c 2048 --temp 0.2 -n 256 --repeat_penalty 1.1
Log start
main: build = 2908 (359cbe3f)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed  = 1715935175
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from models/Llama3-q8.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              = Llama3-Chinese-8B-Instruct
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 128256
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.block_count u32              = 32
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128== 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 '\'.<|begin_of_text|>Below is an instruction that describes a task. Write a response that appropriately completes the request.
> hi
Hello! How can I help you today?<|eot_id|>>

在提示符>之后输入prompt，使用ctrl+c中断输出，多行信息以\作为行尾。执行./main -h命令查看帮助和参数说明，以下是一些常用的参数： `

命令	描述
-m	指定 LLaMA 模型文件的路径
-mu	指定远程 http url 来下载文件
-i	以交互模式运行程序，允许直接提供输入并接收实时响应。
-ins	以指令模式运行程序，这在处理羊驼模型时特别有用。
-f	指定prompt模板，alpaca模型请加载prompts/alpaca.txt
-n	控制回复生成的最大长度（默认：128）
-c	设置提示上下文的大小，值越大越能参考更长的对话历史（默认：512）
-b	控制batch size（默认：8），可适当增加
-t	控制线程数量（默认：4），可适当增加
`--`repeat_penalty	控制生成回复中对重复文本的惩罚力度
`--`temp	温度系数，值越低回复的随机性越小，反之越大
`--`top_p, top_k	控制解码采样的相关参数
`--`color	区分用户输入和生成的文本

模型API服务

llama.cpp提供了完全与OpenAI API兼容的API接口，使用经过编译生成的server可执行文件启动API服务。

python复制代码root@master:~/work/llama.cpp# ./server -m models/Llama3-q8.gguf --host 0.0.0.0 --port 8000
{"tid":"140018656950080","timestamp":1715936504,"level":"INFO","function":"main","line":2942,"msg":"build info","build":2908,"commit":"359cbe3f"}
{"tid":"140018656950080","timestamp":1715936504,"level":"INFO","function":"main","line":2947,"msg":"system info","n_threads":30,"n_threads_batch":-1,"total_threads":30,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | 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 | LLAMAFILE = 1 | "}
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from models/Llama3-q8.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              = Llama3-Chinese-8B-Instruct
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 128256
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.block_count u32              = 32
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 14336

启动API服务后，可以使用curl命令进行测试

python复制代码curl --request POST \--url http://localhost:8000/completion \--header "Content-Type: application/json" \--data '{"prompt": "Hi"}'

模型API服务(第三方)

在llamm.cpp项目中有提到各种语言编写的第三方工具包，可以使用这些工具包提供API服务，这里以Python为例，使用llama-cpp-python提供API服务。

安装依赖

python复制代码pip install llama-cpp-pythonpip install llama-cpp-python -i https://mirrors.aliyun.com/pypi/simple/

注意：可能还需要安装以下缺失依赖，可根据启动时的异常提示分别安装。

python复制代码pip install sse_starlette starlette_context pydantic_settings

启动API服务，默认运行在http://localhost:8000

python复制代码python -m llama_cpp.server --model models/Llama3-q8.gguf

安装openai依赖

python复制代码pip install openai

使用openai调用API服务

python复制代码import os
from openai import OpenAI  # 导入OpenAI库# 设置OpenAI的BASE_URL
os.environ["OPENAI_BASE_URL"] = "http://localhost:8000/v1"client = OpenAI()  # 创建OpenAI客户端对象# 调用模型
completion = client.chat.completions.create(model="llama3", # 任意填messages=[{"role": "system", "content": "你是一个乐于助人的助手。"},{"role": "user", "content": "你好!"}]
)# 输出模型回复
print(completion.choices[0].message)

在这里插入图片描述

GPU推理

如果编译构建了GPU执行环境，可以使用-ngl N或 --n-gpu-layers N参数，指定offload层数，让模型在GPU上运行推理

例如：-ngl 40表示offload 40层模型参数到GPU

未使用-ngl N或 --n-gpu-layers N参数，程序默认在CPU上运行

python复制代码root@master:~/work/llama.cpp# ./server -m models/Llama3-FP16.gguf  --host 0.0.0.0 --port 8000

可从以下关键启动日志看出，模型并没有在GPU上执行

python复制代码ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:Device 0: Tesla V100S-PCIE-32GB, compute capability 7.0, VMM: yes
llm_load_tensors: ggml ctx size =    0.15 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors:        CPU buffer size =  8137.64 MiB
.........................................................................................
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512

使用-ngl N或 --n-gpu-layers N参数，程序默认在GPU上运行

python复制代码root@master:~/work/llama.cpp# ./server -m models/Llama3-FP16.gguf  --host 0.0.0.0 --port 8000   --n-gpu-layers 1000

可从以下关键启动日志看出，模型在GPU上执行

python复制代码ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:Device 0: Tesla V100S-PCIE-32GB, compute capability 7.0, VMM: yes
llm_load_tensors: ggml ctx size =    0.30 MiB
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 =  1002.00 MiB
llm_load_tensors:      CUDA0 buffer size = 14315.02 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: flash_attn = 0

执行nvidia-smi命令，可以进一步验证模型已在GPU上运行。在这里插入图片描述

https://juejin.cn/theme/detail/7218019389664067621?contentType=1)

那么，我们该如何学习大模型？

作为一名热心肠的互联网老兵，我决定把宝贵的AI知识分享给大家。至于能学习到多少就看你的学习毅力和能力了。我已将重要的AI大模型资料包括AI大模型入门学习思维导图、精品AI大模型学习书籍手册、视频教程、实战学习等录播视频免费分享出来。

一、大模型全套的学习路线

学习大型人工智能模型，如GPT-3、BERT或任何其他先进的神经网络模型，需要系统的方法和持续的努力。既然要系统的学习大模型，那么学习路线是必不可少的，下面的这份路线能帮助你快速梳理知识，形成自己的体系。

L1级别:AI大模型时代的华丽登场

L2级别：AI大模型API应用开发工程

L3级别：大模型应用架构进阶实践

L4级别：大模型微调与私有化部署

一般掌握到第四个级别，市场上大多数岗位都是可以胜任，但要还不是天花板，天花板级别要求更加严格，对于算法和实战是非常苛刻的。建议普通人掌握到L4级别即可。