指令精调

指令精调阶段的任务形式基本与Stanford Alpaca相同。训练方案也采用了LoRA进行高效精调，并进一步增加了可训练参数数量。在prompt设计上，精调以及预测时采用的都是原版Stanford Alpaca不带input的模版。对于包含input字段的数据，采用f"{instruction}+\n+{input}"的形式进行拼接。

其中，Stanford Alpaca 格式如下所示：

[{"instruction" : ...,"input" : ...,"output" : ...},...
]

首先，修改模型精调脚本run_sft.sh，需要修改的参数如下：

• --model_name_or_path: 模型经过词表扩充并完成预训练进行权重合并之后所在的目录
• --tokenizer_name_or_path: Chinese-Alpaca tokenizer 所在的目录
• --dataset_dir: 指令精调数据的目录，包含一个或多个以json结尾的Stanford Alpaca格式的指令精调数据文件
• --validation_file: 用作验证集的单个指令精调文件，以json结尾，同样遵循Stanford Alpaca格式
• --output_dir: 模型权重输出路径

dataset_dir=./sft_dataset/train = Chinese-LLaMA-Alpaca/data

其他参数（如：per_device_train_batch_size、training_steps等）是否修改视自身情况而定。

# 运行脚本前请仔细阅读wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki/sft_scripts_zh)
# Read the wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki/sft_scripts_zh) carefully before running the script
lr=1e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05pretrained_model=./merged_output_dir
chinese_tokenizer_path=./merged_output_dir
dataset_dir=./sft_dataset/train
per_device_train_batch_size=1
per_device_eval_batch_size=1
gradient_accumulation_steps=8
max_seq_length=512
output_dir=./sft_output_dir
validation_file=./sft_dataset/test/test.jsondeepspeed_config_file=ds_zero2_no_offload.jsontorchrun --nnodes 1 --nproc_per_node 1 run_clm_sft_with_peft.py \--deepspeed ${deepspeed_config_file} \--model_name_or_path ${pretrained_model} \--tokenizer_name_or_path ${chinese_tokenizer_path} \--dataset_dir ${dataset_dir} \--per_device_train_batch_size ${per_device_train_batch_size} \--per_device_eval_batch_size ${per_device_eval_batch_size} \--do_train \--do_eval \--seed $RANDOM \--fp16 \--num_train_epochs 1 \--lr_scheduler_type cosine \--learning_rate ${lr} \--warmup_ratio 0.03 \--weight_decay 0 \--logging_strategy steps \--logging_steps 10 \--save_strategy steps \--save_total_limit 3 \--evaluation_strategy steps \--eval_steps 100 \--save_steps 200 \--gradient_accumulation_steps ${gradient_accumulation_steps} \--preprocessing_num_workers 8 \--max_seq_length ${max_seq_length} \--output_dir ${output_dir} \--overwrite_output_dir \--ddp_timeout 30000 \--logging_first_step True \--lora_rank ${lora_rank} \--lora_alpha ${lora_alpha} \--trainable ${lora_trainable} \--lora_dropout ${lora_dropout} \--modules_to_save ${modules_to_save} \--torch_dtype float16 \--validation_file ${validation_file} \--load_in_kbits 16 \--gradient_checkpointing \--ddp_find_unused_parameters False

run_clm_sft_with_peft.py  添加如下两行:

为了测试，对数据进行了sample

# coding=utf-8
import jsonwith open("alpaca_data_zh_51k.json", encoding="UTF-8") as f:data = json.load(f)print(len(data))
print(data[0])import random# 设置要划分的测试集大小
sample_size = int(0.1 * (len(data)))# 随机选择测试集的元素
sample_set = random.sample(data, sample_size)data = sample_set
# 设置要划分的测试集大小
test_size = int(0.1 * (len(data)))# 随机选择测试集的元素
test_set = random.sample(data, test_size)# 构建训练集，即剩下的元素
train_set = [x for x in data if x not in test_set]print("训练集:", len(train_set))
print("测试集:", len(test_set))with open("train/train.json", "w", encoding="UTF-8") as f:json.dump(train_set, f, indent=2, ensure_ascii=False)with open("valid/test.json", "w", encoding="UTF-8") as f:json.dump(test_set, f, indent=2, ensure_ascii=False)

运行后输出：

中文LLaMA&Alpaca大语言模型词表扩充+预训练+指令精调 - 知乎 (zhihu.com)