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LLaMA Efficient Tuning

https://github.com/rancheng/LLaMA-Efficient-Tuning/blob/main/README_zh.md

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支持模型：
LLaMA、LLaMA-2、BLOOM、BLOOMZ、Falcon、Baichuan、Baichuan2、InternLM、Qwen、XVERSE、ChatGLM2

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• 默认模块是 --lora_target 参数的部分可选项。请使用 python src/train_bash.py -h 查看全部可选项。
• 对于所有“基座”（Base）模型，--template 参数可以是 default, alpaca, vicuna 等任意值。但“对话”（Chat）模型请务必使用对应的模板。

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安装

软件依赖

• Python 3.8+ 和 PyTorch 1.13.1+
• 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL
• sentencepiece 和 tiktoken
• jieba, rouge-chinese 和 nltk (用于评估)
• gradio 和 matplotlib (用于网页端交互)
• uvicorn, fastapi 和 sse-starlette (用于 API)
• 以及 强而有力的 GPU！

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git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
conda create -n llama_etuning python=3.10
conda activate llama_etuning
cd LLaMA-Efficient-Tuning
pip install -r requirements.txt

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如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 bitsandbytes 库, 支持 CUDA 11.1 到 12.1.

pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl

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数据准备

关于数据集文件的格式，请参考 data/example_dataset 文件夹的内容。
https://github.com/rancheng/LLaMA-Efficient-Tuning/tree/main/data/example_dataset

构建自定义数据集时，既可以使用单个 .json 文件，也可以使用一个数据加载脚本和多个文件。

注意：使用自定义数据集时，请更新 data/dataset_info.json 文件，该文件的格式请参考 data/README.md。
https://github.com/rancheng/LLaMA-Efficient-Tuning/blob/main/data/dataset_info.json
https://github.com/rancheng/LLaMA-Efficient-Tuning/blob/main/data/README.md

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浏览器一体化界面

CUDA_VISIBLE_DEVICES=0 python src/train_web.py

我们极力推荐新手使用浏览器一体化界面，因为它还可以自动生成运行所需的命令行脚本。

目前网页 UI 仅支持单卡训练。

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单 GPU 训练 train_bash

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1、预训练 pt

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage pt \--model_name_or_path path_to_llama_model \--do_train \--dataset wiki_demo \--template default \--finetuning_type lora \--lora_target q_proj,v_proj \--output_dir path_to_pt_checkpoint \--overwrite_cache \--per_device_train_batch_size 4 \--gradient_accumulation_steps 4 \--lr_scheduler_type cosine \--logging_steps 10 \--save_steps 1000 \--learning_rate 5e-5 \--num_train_epochs 3.0 \--plot_loss \--fp16

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2、指令监督微调 sft

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage sft \--model_name_or_path path_to_llama_model \--do_train \--dataset alpaca_gpt4_zh \--template default \--finetuning_type lora \--lora_target q_proj,v_proj \--output_dir path_to_sft_checkpoint \--overwrite_cache \--per_device_train_batch_size 4 \--gradient_accumulation_steps 4 \--lr_scheduler_type cosine \--logging_steps 10 \--save_steps 1000 \--learning_rate 5e-5 \--num_train_epochs 3.0 \--plot_loss \--fp16

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3、奖励模型训练 rm

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage rm \--model_name_or_path path_to_llama_model \--do_train \--dataset comparison_gpt4_zh \--template default \--finetuning_type lora \--lora_target q_proj,v_proj \--resume_lora_training False \--checkpoint_dir path_to_sft_checkpoint \--output_dir path_to_rm_checkpoint \--per_device_train_batch_size 2 \--gradient_accumulation_steps 4 \--lr_scheduler_type cosine \--logging_steps 10 \--save_steps 1000 \--learning_rate 1e-6 \--num_train_epochs 1.0 \--plot_loss \--fp16

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4、PPO 训练 ppo

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage ppo \--model_name_or_path path_to_llama_model \--do_train \--dataset alpaca_gpt4_zh \--template default \--finetuning_type lora \--lora_target q_proj,v_proj \--resume_lora_training False \--checkpoint_dir path_to_sft_checkpoint \--reward_model path_to_rm_checkpoint \--output_dir path_to_ppo_checkpoint \--per_device_train_batch_size 2 \--gradient_accumulation_steps 4 \--lr_scheduler_type cosine \--logging_steps 10 \--save_steps 1000 \--learning_rate 1e-5 \--num_train_epochs 1.0 \--plot_loss

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5、DPO 训练 dpo

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage dpo \--model_name_or_path path_to_llama_model \--do_train \--dataset comparison_gpt4_zh \--template default \--finetuning_type lora \--lora_target q_proj,v_proj \--resume_lora_training False \--checkpoint_dir path_to_sft_checkpoint \--output_dir path_to_dpo_checkpoint \--per_device_train_batch_size 2 \--gradient_accumulation_steps 4 \--lr_scheduler_type cosine \--logging_steps 10 \--save_steps 1000 \--learning_rate 1e-5 \--num_train_epochs 1.0 \--plot_loss \--fp16

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多 GPU 分布式训练

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1、使用 Huggingface Accelerate

accelerate config # 首先配置分布式环境
accelerate launch src/train_bash.py # 参数同上

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使用 DeepSpeed ZeRO-2 进行全参数微调的 Accelerate 配置示例

compute_environment: LOCAL_MACHINE
deepspeed_config:gradient_accumulation_steps: 4gradient_clipping: 0.5offload_optimizer_device: noneoffload_param_device: nonezero3_init_flag: falsezero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

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2、使用 DeepSpeed

deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \--deepspeed ds_config.json \... # 参数同上

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使用 DeepSpeed ZeRO-2 进行全参数微调的 DeepSpeed 配置示例

{"train_micro_batch_size_per_gpu": "auto","gradient_accumulation_steps": "auto","gradient_clipping": "auto","zero_allow_untested_optimizer": true,"fp16": {"enabled": "auto","loss_scale": 0,"initial_scale_power": 16,"loss_scale_window": 1000,"hysteresis": 2,"min_loss_scale": 1},  "zero_optimization": {"stage": 2,"allgather_partitions": true,"allgather_bucket_size": 5e8,"reduce_scatter": true,"reduce_bucket_size": 5e8,"overlap_comm": false,"contiguous_gradients": true}
}

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导出微调后的模型 export_model.py

python src/export_model.py \--model_name_or_path path_to_llama_model \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint \--output_dir path_to_export

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API 服务 api_demo.py

python src/api_demo.py \--model_name_or_path path_to_llama_model \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint

关于 API 文档请见 http://localhost:8000/docs。

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命令行测试 cli_demo.py

python src/cli_demo.py \--model_name_or_path path_to_llama_model \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint

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浏览器测试 web_demo.py

python src/web_demo.py \--model_name_or_path path_to_llama_model \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint

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指标评估（BLEU 分数和汉语 ROUGE 分数）

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage sft \--model_name_or_path path_to_llama_model \--do_eval \--dataset alpaca_gpt4_zh \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint \--output_dir path_to_eval_result \--per_device_eval_batch_size 8 \--max_samples 100 \--predict_with_generate

我们建议在量化模型的评估中使用 --per_device_eval_batch_size=1 和 --max_target_length 128。

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模型预测

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \--stage sft \--model_name_or_path path_to_llama_model \--do_predict \--dataset alpaca_gpt4_zh \--template default \--finetuning_type lora \--checkpoint_dir path_to_checkpoint \--output_dir path_to_predict_result \--per_device_eval_batch_size 8 \--max_samples 100 \--predict_with_generate

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