Alpaca-LoRA-RLHF-PyTorch

a full pipeline to finetune Alpaca LLM with LoRA and RLHF on consumer hardware

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Table of Contents

• Alpaca-LoRA-RLHF-PyTorch
  • Table of Contents
  • Environment Setup
  • Todo List
  • Run
    • Supervised Finetune
    • Merge PEFT adapter into Model
    • Train Reward Model
      • Merge Reward adapter into Model
    • Tuning LM with PPO
  • Notes
  • Reference
  • Star-History
  • Donation
  • License

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Environment Setup

穷人卡：2080Ti 12G
torch==2.0.0
cuda==11.8

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Todo List

• SFT: Supervised Finetune
• Merge Adapter into Model
• RLHF
  • train reward model
  • tuning with RL

Run

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Supervised Finetune

 check src/peft/utils/save_and_load.py , Only comment the line 52 to # #to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}

python supervised_finetune.py --base_model 'decapoda-research/llama-7b-hf' --data_path 'yahma/alpaca-cleaned' --output_dir './lora-alpaca' --num_epochs 1

Merge PEFT adapter into Model

pip uninstall peft -y
pip install peft==0.2.0  # 0.3.0.dev0 raise many errors
python merge_peft_adapter.py --model_name ./alpaca-lora

Train Reward Model

python train_reward_model.py --model_name 'decapoda-research/llama-7b-hf' --gradient_accumulation_steps 32 --per_device_train_batch_size 1 --train_subset 100 --eval_subset 10 --local_rank 0 --bf16 False

Merge Reward adapter into Model

python merge_peft_adapter.py --model_name ./alpaca-lora-reward-model

Tuning LM with PPO

python tuning_lm_with_rl.py --model_name './lora-alpaca-adapter-merged' --reward_model_name './lora-alpaca-reward-model-adapter-merged' --adafactor False --tokenizer_name 'decapoda-research/llama-7b-hf' --save_freq 100 --output_max_length 128 --batch_size 1 --gradient_accumulation_steps 1 --batched_gen True --ppo_epochs 1 --seed 0 --learning_rate 1.4e-5 --early_stopping True --output_dir './checkpoints/tuning_llama_rl'

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Notes

1. 第一步SFT之前，切记有个注意事项，需要检查下 安装的peft代码， src/peft/utils/save_and_load.py , 如果 line 52 有这行代码 #to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}，需要将其注释掉，否则在finetune完之后，保存不了 adapter model 的参数。切记！
2. PEFT的版本，目前从git上安装的是 0.3.0.dev0 版本，在merge_peft_adapter的时候有问题，需要切换到peft==0.2.0 (0.3.0.dev0 没有 _get_submodules()这个函数)
3. train reward model的时候 会发生另一个问题： ValueError: weight is on the meta device, we need a value to put in on 0. 需要参看 transformer 在github上的最新代码，我在发现这个问题的时候，隔天发现在transformer的github上 8小时前才刚刚修复了这个问题。
4. 最后一步，代码上基本是ok的，但是本人只有2080Ti的卡，加载完finetune model之后，再加载Reward model的时候 直接CUDA out of memory了，所以并未执行。

Reference

utils & templates 来自 alpaca-lora 。

requirements 主要是按照 [alpaca-lora](https://github.com/tloen/ alpaca-lora) 来配环境。

• https://github.com/tloen/alpaca-lora
• https://github.com/lvwerra/trl
• https://github.com/jasonvanf/llama-trl

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Star-History

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Donation

If this project help you reduce time to develop, you can give me a cup of coffee :)

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License

MIT © Kun