# **Vicuna-LoRA-RLHF-PyTorch**
a full pipeline to finetune Vicuna LLM with LoRA and RLHF on consumer hardware
---
## **Table of Contents**
- [**Vicuna-LoRA-RLHF-PyTorch**](#vicuna-lora-rlhf-pytorch)
- [**Table of Contents**](#table-of-contents)
- [**Environment Setup**](#environment-setup)
- [**Todo List**](#todo-list)
- [**Run**](#run)
- [**Download Vicuna Weights**](#download-vicuna-weights)
- [**Supervised Finetune**](#supervised-finetune)
- [**Merge PEFT adapter into Model**](#merge-peft-adapter-into-model)
- [**Train Reward Model**](#train-reward-model)
- [**Merge Reward adapter into Model**](#merge-reward-adapter-into-model)
- [**Tuning LM with PPO**](#tuning-lm-with-ppo)
- [**Topics**](#topics)
- [**Reference**](#reference)
- [**Star-History**](#star-history)
- [Donation](#donation)
- [**License**](#license)
---
## **Environment Setup**
```
穷人卡：2080Ti 12G
torch==2.0.0
cuda==11.8
```
---
## **Todo List**
- [x] Download Vicuna Weights
- [x] SFT: Supervised Finetune
- [x] Merge Adapter into Model
- [x] RLHF
- [x] train reward model
- [x] tuning with RL
## **Run**
---
### **Download Vicuna Weights**
```bash
python apply_delta.py --base 'decapoda-research/llama-7b-hf' --target './weights/vicuna-7b' --delta lmsys/vicuna-7b-delta-v1.1
```
### **Supervised Finetune**
check **src/peft/utils/save_and_load.py** first, Only comment the line 52 to
```python
# #to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}
```
then run
```bash
python supervised_finetune.py --data_path './data/merge_sample.json' --output_path 'lora-Vicuna' --model_path './weights/vicuna-7b' --eval_steps 200 --save_steps 200 --test_size 1
```
### **Merge PEFT adapter into Model**
check peft version first, if peft not 0.2.0, should install peft==0.2.0
```bash
pip uninstall peft -y
pip install peft==0.2.0 # 0.3.0.dev0 has many errors
```
```bash
python merge_peft_adapter.py --model_name 'lora-Vicuna'
pip uninstall peft -y
pip install git+https://github.com/huggingface/peft.git # then comments peft/utis/save_and_load.py line 52.
```
### **Train Reward Model**
```bash
python train_reward_model.py --model_name './weights/vicuna-7b' --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**
```bash
python merge_peft_adapter.py --model_name ./reward_model_vicuna-7b
```
### **Tuning LM with PPO**
```bash
python tuning_lm_with_rl.py --model_name './lora-Vicuna-adapter-merged' --reward_model_name './reward_model_vicuna-7b-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 './tuning_llama_rl_checkpoints'
```
---
## **Topics**
1. Vicuna model weight not on HuggingFace hub, so you need download first by runing [apply_delta.py](./apply_delta.py) scripts.
2. 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**
[apply_delta.py](apply_delta.py) 来自 [FastChat](https://github.com/lm-sys/FastChat) 。
requirements 主要是按照 [alpaca-lora](https://github.com/tloen/alpaca-lora) 来配环境。
* [https://github.com/lm-sys/FastChat](https://github.com/lm-sys/FastChat)
* [https://github.com/tloen/alpaca-lora](https://github.com/tloen/alpaca-lora)
* [https://github.com/lvwerra/trl](https://github.com/lvwerra/trl)
* [https://github.com/jasonvanf/llama-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 :)
AliPay(支付宝)
WechatPay(微信)
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## **License**
[MIT](LICENSE) © Kun
