大语言模型之十三 LLama2中文推理

在《大语言模型之十二 SentencePiece扩充LLama2中文词汇》一文中已经扩充好了中文词汇表,接下来就是使用整理的中文语料对模型进行预训练了。这里先跳过预训练环节。先试用已经训练好的模型,看看如何推理。

合并模型

这一步骤会合并LoRA权重,生成全量模型权重。此处可以选择输出PyTorch版本权重(.pth文件)或者输出HuggingFace版本权重(.bin文件)。执行以下命令:

$ python scripts/merge_llama2_with_chinese_lora_low_mem.py \--base_model path_to_original_llama2_hf_dir \--lora_model path_to_chinese_llama2_or_alpaca2_lora \--output_type huggingface \--output_dir path_to_output_dir 

参数说明:

  • –base_model:存放HF格式的Llama-2模型权重和配置文件的目录,这可以在《大语言模型之十二 SentencePiece扩充LLama2中文词汇》的1.下载原版LLama-2模型小节找到如何将原始meta的LlaMA-2模型转为Huggingface的格式。
  • –lora_model:中文LLaMA-2/Alpaca-2 LoRA解压后文件所在目录,也可使用🤗Model Hub模型调用名称(会自动下载),这里使用Chinese-LLaMA-Alpaca-2给出的预训练好的7B模型。
  • –output_type:指定输出格式,可为pth或huggingface。若不指定,默认为huggingface
  • –output_dir:指定保存全量模型权重的目录,默认为./
  • (可选)–verbose:显示合并过程中的详细信息
    请添加图片描述
    转换好格式之后,内容如下(时间戳为11:28的即为转换生成文件):
    请添加图片描述
    其中的ggml开头的事量化文件是用于模型推理。

推理

在attn_and_long_ctx_patches.py实现了基于NTK的自适应上下文适配方法,其中基于transformers的推理脚本。

  • 当上下文小于4K时,默认关闭,因为原生的效果更好
  • 大于4K时开启NTK,AUTO_COEFF默认为1.0
    以下是不同AUTO_COEFF下,在不同上下文长度上的PPL变化(越低越好),供使用参考。
    对NTK方法熟悉的用户可直接修改代码中的ALPHA取值。
  • 12K以下:几乎和原生4K的PPL没有显著差异
  • 12K-16K:开始存在一定损失,大约是3比特量化级别的效果
  • 18K+:存在较大损失,大约是2比特量化级别效果,20K+不可用
    以上结果仅供参考,应在实际场景中测试调整AUTO_COEFF或者ALPHA取值。

使用llama.cpp推理

Step 1: 克隆和编译llama.cpp

  1. (可选)如果已下载旧版仓库,建议git pull拉取最新代码,并执行make clean进行清理
  2. 拉取最新版llama.cpp仓库代码
$ git clone https://github.com/ggerganov/llama.cpp
  1. 对llama.cpp项目进行编译,生成./main(用于推理)和./quantize(用于量化)二进制文件。
$ make

Step 2: 生成量化版本模型
目前llama.cpp已支持.pth文件以及huggingface格式.bin的转换。将完整模型权重转换为GGML的FP16格式,生成文件路径为zh-models/7B/ggml-model-f16.gguf。进一步对FP16模型进行4-bit量化,生成量化模型文件路径为zh-models/7B/ggml-model-q4_0.gguf。不同量化方法的性能对比见本Wiki最后部分。

python3 convert.py ../merged_chinese_llama_7b
$ ./quantize ../merged_chinese_llama_7b/ggml-model-f16.gguf ../merged_chinese_llama_7b/ggml-model-q4_0.gguf q4_0

Step 3: 加载并启动模型

  llama.cpp git:(master) ✗ ./main -s 1 -m ../merged_chinese_llama_7b/ggml-model-q4_0.gguf -p "中国的首都是" --ignore-eos -c 64 -n 128 -t 3 -ngl 10
  • GPU推理:通过Metal编译则只需在./main中指定-ngl 1;cuBLAS编译需要指定offload层数,例如-ngl 40表示offload 40层模型参数到GPU

  • 加载长上下文模型(16K):

    • 启动模型(./main)后debug信息中显示llm_load_print_meta: freq_scale = 0.25,则表示模型转换时已载入相应超参,无需其他特殊设置
    • 如果上述debug信息显示为llm_load_print_meta: freq_scale = 1.0,则需在./main中额外指定–rope-scale 4
  • 默认的量化方法为q4_0,虽然速度最快但损失也较大,推荐使用Q4_K作为替代

  • 机器资源够用且对速度要求不是那么苛刻的情况下可以使用q8_0或Q6_K,非常接近F16模型的效果

如果使用的是Mac Intel可能报如下错:

ggml_metal_init: load pipeline error: Error Domain=CompilerError Code=2 "SC compilation failure
There is a call to an undefined label" UserInfo={NSLocalizedDescription=SC compilation failure
There is a call to an undefined label}
llama_new_context_with_model: ggml_metal_init() failed
llama_init_from_gpt_params: error: failed to create context with model '../merged_chinese_llama_7b/ggml-model-q4_0.gguf'
main: error: unable to load model

可以按这里的修改

$ make clean
$ brew update && brew install clblast
#disable metal and enable clblast 
$ make LLAMA_CLBLAST=1 LLAMA_NO_METAL=1
#这时可以用main进行推理
$./main -s 1 -m ../merged_chinese_llama_7b/ggml-model-q4_0.gguf -p "中国的首都是" --ignore-eos -c 64 -n 128 -t 3 -ngl 10

对应的终端输出为:

(venv) ➜  llama.cpp git:(master) ✗ ./main -s 1 -m ../merged_chinese_llama_7b/ggml-model-q4_0.gguf -p "中国的首都是" --ignore-eos -c 64 -n 128 -t 3 -ngl 10
Log start
main: warning: changing RoPE frequency base to 0 (default 10000.0)
main: warning: scaling RoPE frequency by 0 (default 1.0)
main: build = 1273 (99115f3)
main: built with Apple clang version 14.0.3 (clang-1403.0.22.14.1) for x86_64-apple-darwin22.5.0
main: seed  = 1
ggml_opencl: selecting platform: 'Apple'
ggml_opencl: selecting device: 'Intel(R) UHD Graphics 630'
ggml_opencl: device FP16 support: false
llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from ../merged_chinese_llama_7b/ggml-model-q4_0.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor    0:                token_embd.weight q4_0     [  4096, 55296,     1,     1 ]
llama_model_loader: - tensor    1:              blk.0.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    2:              blk.0.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    3:              blk.0.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    4:         blk.0.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    5:            blk.0.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor    6:              blk.0.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor    7:            blk.0.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor    8:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor    9:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   10:              blk.1.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   11:              blk.1.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   12:              blk.1.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   13:         blk.1.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   14:            blk.1.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   15:              blk.1.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   16:            blk.1.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   17:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   18:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   19:              blk.2.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   20:              blk.2.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   21:              blk.2.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   22:         blk.2.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   23:            blk.2.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   24:              blk.2.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   25:            blk.2.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   26:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   27:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   28:              blk.3.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   29:              blk.3.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   30:              blk.3.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   31:         blk.3.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   32:            blk.3.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   33:              blk.3.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   34:            blk.3.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   35:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   36:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   37:              blk.4.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   38:              blk.4.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   39:              blk.4.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   40:         blk.4.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   41:            blk.4.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   42:              blk.4.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   43:            blk.4.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   44:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   45:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   46:              blk.5.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   47:              blk.5.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   48:              blk.5.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   49:         blk.5.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   50:            blk.5.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   51:              blk.5.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   52:            blk.5.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   53:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   54:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   55:              blk.6.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   56:              blk.6.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   57:              blk.6.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   58:         blk.6.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   59:            blk.6.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   60:              blk.6.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   61:            blk.6.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   62:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   63:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   64:              blk.7.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   65:              blk.7.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   66:              blk.7.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   67:         blk.7.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   68:            blk.7.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   69:              blk.7.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   70:            blk.7.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   71:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   72:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   73:              blk.8.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   74:              blk.8.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   75:              blk.8.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   76:         blk.8.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   77:            blk.8.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   78:              blk.8.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   79:            blk.8.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   80:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   81:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   82:              blk.9.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   83:              blk.9.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   84:              blk.9.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   85:         blk.9.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   86:            blk.9.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   87:              blk.9.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   88:            blk.9.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   89:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   90:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   91:             blk.10.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   92:             blk.10.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   93:             blk.10.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   94:        blk.10.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   95:           blk.10.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   96:             blk.10.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor   97:           blk.10.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor   98:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   99:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  100:             blk.11.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  101:             blk.11.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  102:             blk.11.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  103:        blk.11.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  104:           blk.11.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  105:             blk.11.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  106:           blk.11.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  107:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  108:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  109:             blk.12.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  110:             blk.12.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  111:             blk.12.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  112:        blk.12.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  113:           blk.12.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  114:             blk.12.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  115:           blk.12.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  116:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  117:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  118:             blk.13.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  119:             blk.13.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  120:             blk.13.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  121:        blk.13.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  122:           blk.13.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  123:             blk.13.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  124:           blk.13.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  125:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  126:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  127:             blk.14.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  128:             blk.14.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  129:             blk.14.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  130:        blk.14.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  131:           blk.14.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  132:             blk.14.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  133:           blk.14.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  134:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  135:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  136:             blk.15.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  137:             blk.15.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  138:             blk.15.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  139:        blk.15.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  140:           blk.15.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  141:             blk.15.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  142:           blk.15.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  143:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  144:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  145:             blk.16.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  146:             blk.16.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  147:             blk.16.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  148:        blk.16.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  149:           blk.16.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  150:             blk.16.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  151:           blk.16.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  152:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  153:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  154:             blk.17.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  155:             blk.17.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  156:             blk.17.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  157:        blk.17.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  158:           blk.17.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  159:             blk.17.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  160:           blk.17.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  161:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  162:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  163:             blk.18.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  164:             blk.18.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  165:             blk.18.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  166:        blk.18.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  167:           blk.18.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  168:             blk.18.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  169:           blk.18.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  170:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  171:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  172:             blk.19.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  173:             blk.19.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  174:             blk.19.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  175:        blk.19.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  176:           blk.19.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  177:             blk.19.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  178:           blk.19.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  179:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  180:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  181:             blk.20.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  182:             blk.20.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  183:             blk.20.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  184:        blk.20.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  185:           blk.20.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  186:             blk.20.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  187:           blk.20.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  188:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  189:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  190:             blk.21.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  191:             blk.21.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  192:             blk.21.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  193:        blk.21.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  194:           blk.21.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  195:             blk.21.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  196:           blk.21.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  197:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  198:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  199:             blk.22.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  200:             blk.22.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  201:             blk.22.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  202:        blk.22.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  203:           blk.22.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  204:             blk.22.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  205:           blk.22.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  206:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  207:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  208:             blk.23.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  209:             blk.23.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  210:             blk.23.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  211:        blk.23.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  212:           blk.23.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  213:             blk.23.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  214:           blk.23.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  215:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  216:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  217:             blk.24.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  218:             blk.24.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  219:             blk.24.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  220:        blk.24.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  221:           blk.24.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  222:             blk.24.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  223:           blk.24.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  224:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  225:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  226:             blk.25.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  227:             blk.25.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  228:             blk.25.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  229:        blk.25.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  230:           blk.25.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  231:             blk.25.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  232:           blk.25.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  233:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  234:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  235:             blk.26.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  236:             blk.26.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  237:             blk.26.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  238:        blk.26.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  239:           blk.26.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  240:             blk.26.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  241:           blk.26.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  242:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  243:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  244:             blk.27.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  245:             blk.27.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  246:             blk.27.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  247:        blk.27.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  248:           blk.27.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  249:             blk.27.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  250:           blk.27.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  251:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  252:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  253:             blk.28.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  254:             blk.28.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  255:             blk.28.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  256:        blk.28.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  257:           blk.28.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  258:             blk.28.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  259:           blk.28.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  260:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  261:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  262:             blk.29.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  263:             blk.29.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  264:             blk.29.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  265:        blk.29.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  266:           blk.29.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  267:             blk.29.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  268:           blk.29.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  269:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  270:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  271:             blk.30.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  272:             blk.30.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  273:             blk.30.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  274:        blk.30.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  275:           blk.30.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  276:             blk.30.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  277:           blk.30.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  278:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  279:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  280:             blk.31.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  281:             blk.31.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  282:             blk.31.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  283:        blk.31.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  284:           blk.31.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  285:             blk.31.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]
llama_model_loader: - tensor  286:           blk.31.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]
llama_model_loader: - tensor  287:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  288:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  289:               output_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  290:                    output.weight q6_K     [  4096, 55296,     1,     1 ]
llama_model_loader: - kv   0:                       general.architecture str
llama_model_loader: - kv   1:                               general.name str
llama_model_loader: - kv   2:                       llama.context_length u32
llama_model_loader: - kv   3:                     llama.embedding_length u32
llama_model_loader: - kv   4:                          llama.block_count u32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32
llama_model_loader: - kv   7:                 llama.attention.head_count u32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv  10:                       llama.rope.freq_base f32
llama_model_loader: - kv  11:                          general.file_type u32
llama_model_loader: - kv  12:                       tokenizer.ggml.model str
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr
llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv  18:               general.quantization_version u32
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q4_0:  225 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_print_meta: format         = GGUF V2 (latest)
llm_load_print_meta: arch           = llama
llm_load_print_meta: vocab type     = SPM
llm_load_print_meta: n_vocab        = 55296
llm_load_print_meta: n_merges       = 0
llm_load_print_meta: n_ctx_train    = 2048
llm_load_print_meta: n_ctx          = 64
llm_load_print_meta: n_embd         = 4096
llm_load_print_meta: n_head         = 32
llm_load_print_meta: n_head_kv      = 32
llm_load_print_meta: n_layer        = 32
llm_load_print_meta: n_rot          = 128
llm_load_print_meta: n_gqa          = 1
llm_load_print_meta: f_norm_eps     = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff           = 11008
llm_load_print_meta: freq_base      = 10000.0
llm_load_print_meta: freq_scale     = 1
llm_load_print_meta: model type     = 7B
llm_load_print_meta: model ftype    = mostly Q4_0
llm_load_print_meta: model params   = 6.93 B
llm_load_print_meta: model size     = 3.69 GiB (4.57 BPW)
llm_load_print_meta: general.name   = ..
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token  = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.09 MB
llm_load_tensors: using OpenCL for GPU acceleration
llm_load_tensors: mem required  = 2687.86 MB (+   32.00 MB per state)
llm_load_tensors: offloading 10 repeating layers to GPU
llm_load_tensors: offloaded 10/33 layers to GPU
llm_load_tensors: VRAM used: 1086 MB
..............................................................................................
llama_new_context_with_model: kv self size  =   32.00 MB
llama_new_context_with_model: compute buffer total size =   15.97 MBsystem_info: n_threads = 3 / 12 | AVX = 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 = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
generate: n_ctx = 64, n_batch = 512, n_predict = 128, n_keep = 0中国的首都是世界上政治、军事和文化中心。长安古称"京师",后为北京;北宋时期,东京开封府一度升格为"中都""大都"。《长安志》记载:"自建都以来,因得名曰'长安'者有…

一些说明

这里将两个基座模型和LORA fine tune模型merge的原因在于扩充词汇表之后,Embedding也进行了扩充,词汇表比原始的LlaMA-2 32k大,因而要将Embedding层merge(实际是替换),此外Attention(q,k,v)以及MLP(feedforward,w1,w2,w3)基本都进行了merge操作。由于改动如此之大,以至于《大语言模型之七- Llama-2单GPU微调SFT》博客里微调方法是一样的,但是改动量和训练的资源需求是不一样的,这也导致了扩充中文的微调训练在colab免费的12G GPU内存上是无法完成训练的。

PEFT是 Hugging Face提供的模型训练的高效库,LORA是其提供的方法之一,LORA方式是2021年论文 LoRA: Low-rank adaptation of Large Language Models.首先引入的方法。
其核心思想是可以在仅调整一小部分权重的同时实现出色的性能,进而无需在多台机器上调整数十亿个参数,使整个微调过程更加实用且经济可行。使用PEFT和量化允许在单个GPU上微调具有数十亿个参数的大型模型。比如Embedding是词向量的编码,虽然任务不同,如问答、摘要、协作类的大模型,虽然应用不同,但是词向量编码是可以复用的,不需要改,因而在微调的时候,就不改词向量了,这样就节省存储和运算资源。

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