MacM1芯片系统基于大语言模型LLaMA部署本地版的ChatGPT

　　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：https://huggingface.co/nyanko7/LLaMA-7B/tree/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  Processing part 0  Processing variable: tok_embeddings.weight with shape: torch.Size([32000, 4096]) and type: torch.float16 Processing variable: norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: output.weight with shape: torch.Size([32000, 4096]) and type: torch.float16 Processing variable: layers.0.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.0.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.0.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.0.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.0.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.0.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.0.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.0.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.0.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.1.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.1.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.1.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.1.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.1.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.1.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.1.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.1.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.1.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.2.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.2.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.2.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.2.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.2.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.2.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.2.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.2.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.2.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.3.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.3.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.3.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.3.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.3.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.3.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.3.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.3.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.3.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.4.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.4.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.4.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.4.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.4.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.4.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.4.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.4.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.4.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.5.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.5.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.5.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.5.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.5.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.5.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.5.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.5.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.5.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.6.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.6.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.6.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.6.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.6.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.6.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.6.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.6.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.6.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.7.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.7.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.7.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.7.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.7.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.7.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.7.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.7.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.7.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.8.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.8.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.8.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.8.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.8.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.8.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.8.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.8.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.8.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.9.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.9.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.9.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.9.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.9.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.9.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.9.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.9.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.9.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.10.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.10.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.10.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.10.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.10.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.10.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.10.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.10.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.10.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.11.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.11.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.11.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.11.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.11.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.11.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.11.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.11.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.11.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.12.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.12.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.12.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.12.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.12.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.12.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.12.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.12.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.12.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.13.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.13.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.13.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.13.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.13.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.13.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.13.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.13.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.13.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.14.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.14.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.14.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.14.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.14.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.14.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.14.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.14.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.14.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.15.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.15.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.15.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.15.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.15.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.15.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.15.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.15.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.15.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.16.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.16.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.16.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.16.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.16.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.16.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.16.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.16.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.16.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.17.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.17.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.17.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.17.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.17.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.17.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.17.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.17.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.17.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.18.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.18.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.18.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.18.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.18.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.18.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.18.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.18.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.18.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.19.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.19.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.19.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.19.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.19.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.19.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.19.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.19.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.19.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.20.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.20.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.20.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.20.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.20.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.20.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.20.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.20.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.20.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.21.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.21.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.21.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.21.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.21.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.21.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.21.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.21.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.21.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.22.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.22.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.22.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.22.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.22.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.22.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.22.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.22.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.22.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.23.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.23.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.23.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.23.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.23.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.23.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.23.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.23.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.23.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.24.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.24.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.24.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.24.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.24.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.24.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.24.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.24.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.24.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.25.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.25.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.25.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.25.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.25.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.25.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.25.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.25.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.25.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.26.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.26.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.26.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.26.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.26.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.26.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.26.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.26.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.26.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.27.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.27.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.27.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.27.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.27.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.27.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.27.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.27.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.27.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.28.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.28.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.28.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.28.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.28.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.28.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.28.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.28.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.28.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.29.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.29.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.29.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.29.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.29.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.29.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.29.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.29.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.29.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.30.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.30.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.30.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.30.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.30.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.30.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.30.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.30.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.30.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.31.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.31.attention.wk.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.31.attention.wv.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.31.attention.wo.weight with shape: torch.Size([4096, 4096]) and type: torch.float16 Processing variable: layers.31.feed_forward.w1.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.31.feed_forward.w2.weight with shape: torch.Size([4096, 11008]) and type: torch.float16 Processing variable: layers.31.feed_forward.w3.weight with shape: torch.Size([11008, 4096]) and type: torch.float16 Processing variable: layers.31.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 Processing variable: layers.31.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16   Converting to float32 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。