如何用c++调用大模型——关于使用llama.cpp的lib库的简易教程
一、如何编译
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON
| 参数 | 解释 |
|---|---|
| -B build | build 是生成的构建目录，这是 CMake 将生成的文件存放的地方。 |
| -DGGML_CUDA=ON | 启用 CUDA 支持 |
| -DBUILD_SHARED_LIBS=OFF | 生成 静态库 |
| -DLLAMA_CURL=ON | 启用 CURL 库支持以便支持网络请求 |
| -DCMAKE_POSITION_INDEPENDENT_CODE=ON | 为了确保编译出的目标文件（即使是静态库）具备「地址无关性」 |
cmake --build build --config Release -j --clean-first
| 参数 | 解释 |
|---|---|
| –build build | 表示使用build 目录中的构建文件来执行构建过程 |
| –config Release | 表示指定构建的配置为 Release 配置 |
| -j | 表示并行编译，默认用的所有 CPU 核心数，也可指定作业数，例如 -j 8 表示使用 8 个进程并行编译 |
| –clean-first | 表示在构建之前先清理掉之前的构建结果。 |
二、下载例子
git clone https://github.com/hit56/simple-example-for-llama.cpp.git
cd simple-example-for-llama.cpp
生成makefile
cmake -B build
运行结果：
-- The CXX compiler identification is GNU 9.4.0
-- The CUDA compiler identification is NVIDIA 12.2.91
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/ccache - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Detecting CUDA compiler ABI info
-- Detecting CUDA compiler ABI info - done
-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped
-- Detecting CUDA compile features
-- Detecting CUDA compile features - done
-- Found OpenMP_CXX: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
-- Configuring done (21.8s)
-- Generating done (0.2s)
-- Build files have been written to: /root/private_data/work_space/simple-example-for-llama.cpp/build
开始make
cmake --build build --config Release -j --clean-first
运行结果：
[ 50%] Building CXX object CMakeFiles/llama-simple.dir/simple.cpp.o
[100%] Linking CXX executable llama-simple
[100%] Built target llama-simple
执行
./build/llama-simple -m ~/private_data/models/Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf "您好!"
运行结果：
llama_kv_cache: CUDA0 KV buffer size = 28.00 MiB
llama_kv_cache: size = 28.00 MiB ( 256 cells, 28 layers, 1/1 seqs), K (f16): 14.00 MiB, V (f16): 14.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 2
llama_context: max_nodes = 2488
llama_context: reserving full memory module
llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 1
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_context: Flash Attention was auto, set to enabled
graph_reserve: reserving a graph for ubatch with n_tokens = 64, n_seqs = 1, n_outputs = 64
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
graph_reserve: reserving a graph for ubatch with n_tokens = 64, n_seqs = 1, n_outputs = 64
llama_context: CUDA0 compute buffer size = 37.34 MiB
llama_context: CUDA_Host compute buffer size = 0.31 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 2
您好! 今天天气怎么样？我需要准备什么？ 今天天气怎么样？我需要准备什么？看起来你是在询问今天的天气情况和所需准备的
main: decoded 32 tokens in 0.45 s, speed: 71.21 t/sllama_perf_sampler_print: sampling time = 5.15 ms / 32 runs ( 0.16 ms per token, 6219.63 tokens per second)
llama_perf_context_print: load time = 11569.00 ms
llama_perf_context_print: prompt eval time = 115.84 ms / 2 tokens ( 57.92 ms per token, 17.26 tokens per second)
llama_perf_context_print: eval time = 318.55 ms / 31 runs ( 10.28 ms per token, 97.32 tokens per second)
llama_perf_context_print: total time = 11902.59 ms / 33 tokens
llama_perf_context_print: graphs reused = 30
参考文献：
- 基于llama.cpp的QwQ32B模型推理