OpenAI公司基于GPT模型的ChatGPT风光无两，眼看它起朱楼，眼看它宴宾客，FaceBook终于坐不住了，发布了同样基于LLM的人工智能大语言模型LLaMA，号称包含70亿、130亿、330亿和650亿这4种参数规模的模型，参数是指神经网络中的权重和偏置等可调整的变量，用于训练和优化神经网络的性能，70亿意味着神经网络中有70亿个参数，由此类推。
在一些大型神经网络中，每个参数需要使用32位或64位浮点数进行存储，这意味着每个参数需要占用4字节或8字节的存储空间。因此，对于包含70亿个参数的神经网络，其存储空间将分别为8 GB或12GB。
此外，神经网络的大小不仅取决于参数的数量，还取决于神经元的数目，层数和其他结构参数等。因此，70亿的神经网络可能会占用更多的存储空间，具体取决于网络的结构和实现细节。
因此这种体量的模型单机跑绝对够我们喝一壶，所以本次使用最小的LLaMA 7B模型进行测试。
LLaMA项目安装和模型配置
和Stable-Diffusion项目如出一辙，FaceBook开源的LLaMA项目默认写死使用cuda模式，这也就意味着必须有 NVIDIA 的 GPU来训练和运行，不过好在大神GeorgiGerganov 用 C++ 基于 LLaMA 项目重写了一个跑在 CPU 上的移植版本 llama.cpp应用。
llama.cpp首先适配的就是苹果的M系列芯片，这对于果粉来说无疑是一个重大利好，首先通过命令拉取C++版本的LLaMA项目：
git clone https://github.com/ggerganov/llama.cpp
随后进入项目目录：
llama.cpp
在项目中，需要单独建立一个模型文件夹models:
mkdir models
随后去huggingface官网下载LLaMA的7B模型文件：nyanko7/LLaMA-7B at main
是的，主模型文件已经达到了13.5gb之巨，如果本地硬盘空间告急，请谨慎下载。
随后在models目录建立模型子目录7B:
mkdir 7B
将tokenizer.model和tokenizer_checklist.chk放入和7B平行的目录中：
➜ models git:(master) ✗ ls
7B tokenizer.model tokenizer_checklist.chk
随后将checklist.chk consolidated.00.pth和params.json放入7B目录中：
➜ 7B git:(master) ✗ ls
checklist.chk consolidated.00.pth params.json
至此，模型就配置好了。
LLaMA模型转换
由于我们没有使用FaceBook的原版项目，所以它的模型还需要进行转换，也就是转换为当前C++版本的LLaMA可以运行的模型。
这里通过Python脚本进行转换操作：
python3 convert-pth-to-ggml.py models/7B/ 1
第一个参数是模型所在目录，第二个参数为转换时使用的浮点类型，使用 float32，转换的结果文件会大一倍，当该参数值为 1时，则使用 float16 这个默认值，这里我们使用默认数据类型。
程序输出：
➜ llama.cpp git:(master) ✗ python convert-pth-to-ggml.py models/7B/ 1
{'dim': 4096, 'multiple_of': 256, 'n_heads': 32, 'n_layers': 32, 'norm_eps': 1e-06, 'vocab_size': -1}
n_parts = 1
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Processing variable: layers.21.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.22.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.22.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.23.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.23.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.24.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.25.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.25.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.25.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.26.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.26.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.26.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.27.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.27.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.27.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.28.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.28.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.28.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.29.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.29.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.29.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.30.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.30.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.30.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.31.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16
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Processing variable: layers.31.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Processing variable: layers.31.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16
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Done. Output file: models/7B//ggml-model-f16.bin, (part 0)
可以看到，如果转换成功，会在models/7B/目录生成一个C++可以调用的ggml-model-f16.bin模型文件。
LLaMA模型调用
接下来就可以调用转换后的模型了，首先在编译C++项目：
make
程序返回：
➜ llama.cpp git:(master) ✗ make
I llama.cpp build info:
I UNAME_S: Darwin
I UNAME_P: arm
I UNAME_M: arm64
I CFLAGS: -I. -O3 -DNDEBUG -std=c11 -fPIC -pthread -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread
I LDFLAGS: -framework Accelerate
I CC: Apple clang version 14.0.0 (clang-1400.0.29.202)
I CXX: Apple clang version 14.0.0 (clang-1400.0.29.202)
cc -I. -O3 -DNDEBUG -std=c11 -fPIC -pthread -DGGML_USE_ACCELERATE -c ggml.c -o ggml.o
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread -c utils.cpp -o utils.o
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread main.cpp ggml.o utils.o -o main -framework Accelerate
./main -h
usage: ./main [options]
options:
-h, --help show this help message and exit
-i, --interactive run in interactive mode
-ins, --instruct run in instruction mode (use with Alpaca models)
-r PROMPT, --reverse-prompt PROMPT
in interactive mode, poll user input upon seeing PROMPT (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)
-t N, --threads N number of threads to use during computation (default: 4)
-p PROMPT, --prompt PROMPT
prompt to start generation with (default: empty)
--random-prompt start with a randomized prompt.
-f FNAME, --file FNAME
prompt file to start generation.
-n N, --n_predict N number of tokens to predict (default: 128)
--top_k N top-k sampling (default: 40)
--top_p N top-p sampling (default: 0.9)
--repeat_last_n N last n tokens to consider for penalize (default: 64)
--repeat_penalty N penalize repeat sequence of tokens (default: 1.3)
-c N, --ctx_size N size of the prompt context (default: 512)
--ignore-eos ignore end of stream token and continue generating
--memory_f16 use f16 instead of f32 for memory key+value
--temp N temperature (default: 0.8)
-b N, --batch_size N batch size for prompt processing (default: 8)
-m FNAME, --model FNAME
model path (default: models/llama-7B/ggml-model.bin)
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread quantize.cpp ggml.o utils.o -o quantize -framework Accelerate
编译成功后，本地会生成一个main.cpp文件。
随后根据编译后输出的说明文档直接调用模型即可：
./main -m ./models/7B/ggml-model-f16.bin -p 'Hi i am '
程序输出：
➜ llama.cpp git:(master) ✗ ./main -m ./models/7B/ggml-model-f16.bin -p 'hi i am'
main: seed = 1679400707
llama_model_load: loading model from './models/7B/ggml-model-f16.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx = 512
llama_model_load: n_embd = 4096
llama_model_load: n_mult = 256
llama_model_load: n_head = 32
llama_model_load: n_layer = 32
llama_model_load: n_rot = 128
llama_model_load: f16 = 1
llama_model_load: n_ff = 11008
llama_model_load: n_parts = 1
llama_model_load: ggml ctx size = 13365.09 MB
llama_model_load: memory_size = 512.00 MB, n_mem = 16384
llama_model_load: loading model part 1/1 from './models/7B/ggml-model-f16.bin'
llama_model_load: .................................... done
llama_model_load: model size = 12853.02 MB / num tensors = 291
system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
main: prompt: ' hi i am'
main: number of tokens in prompt = 6
1 -> ''
13450 -> ' hi'
423 -> 'i'
25523 -> ' am'
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
hi i am a pythoner, but sunk to become a ruby
说实话，推理速度实在不敢恭维，也可能是因为笔者的电脑配置太渣导致。
结语
LLaMA 7B模型总体上需要纯英文的提示词（prompt），对中文的理解能力还不够，优势是确实可以单机跑起来，当然本地跑的话，减少了网络传输数据的环节，推理效率自然也就更高，对于普通的AI爱好者来说，足矣。