Transformer Reinforcement Learning X

trlX allows you to fine-tune 🤗 Hugging Face supported language models (gpt2, gpt-j, gpt-neo and gpt-neox based) up to 20B parameters using reinforcement learning via either a provided reward function or reward-labeled dataset. Proximal Policy Optimization (PPO) and Implicit Language Q-Learning (ILQL) are implemented.

You can read more about trlX in our documentation.

Want to collect human annotations for your RL application? Check out CHEESE!, our library for HiTL data collection.

Installation

git clone https://github.com/CarperAI/trlx.git
cd trlx
pip install torch --extra-index-url https://download.pytorch.org/whl/cu116 # for cuda
pip install -e .

How to Train

You can train a model using a reward function or a reward-labeled dataset.

Using a reward function

trainer = trlx.train('gpt2', reward_fn=lambda samples: [sample.count('cats') for sample in samples])

Using a reward-labeled dataset

trainer = trlx.train('EleutherAI/gpt-j-6B', dataset=[('dolphins', 'geese'), (1.0, 100.0)])

Trained model is a wrapper over a given autoregressive model

trainer.generate(**tokenizer('Q: Who rules the world? A:', return_tensors='pt'), do_sample=True)

Use 🤗 Accelerate to launch distributed training

accelerate config # choose DeepSpeed option
accelerate launch examples/simulacra.py

Use Ray Tune to launch hyperparameter sweep

python -m trlx.sweep --config configs/sweeps/ppo_sweep.yml examples/ppo_sentiments.py

For more usage see examples

Contributing

For development check out these guidelines and also read our docs

Acknowledgements

Many thanks to Leandro von Werra for contributing with trl, a library that initially inspired this repo.