TRLX: Fine-Tuning Language Models with Reinforcement Learning

Overview

trlX is an open-source framework designed for distributed training of large language models using Reinforcement Learning with Human Feedback (RLHF). It allows researchers and developers to fine-tune models up to 20 billion parameters, such as Facebook's OPT-6.7B and Google’s FLAN-T5-XXL, utilizing advanced algorithms for optimal performance.

How to Use

To get started with trlX, simply clone the repository and install the necessary dependencies:

git clone https://github.com/CarperAI/trlx.git
cd trlx
pip install torch --extra-index-url https://download.pytorch.org/whl/cu118
pip install -e .

You can train models using either a provided reward function or a labeled dataset, with options for Proximal Policy Optimization (PPO) and Implicit Language Q-Learning (ILQL).

Purposes

trlX is ideal for:

• Fine-tuning language models for specific tasks.
• Implementing reinforcement learning strategies in NLP applications.
• Collecting human annotations through its integrated data collection library, CHEESE.

Benefits for Users

• Scalability: Efficiently train models beyond 20B parameters.
• Flexibility: Support for various RL algorithms.
• Community Support: Active contributions and feedback welcomed on GitHub.

Alternatives

Consider exploring other frameworks like Hugging Face’s Transformers or OpenAI’s Spinning Up in RL for different approaches to training language models.

User Reviews

Users have praised trlX for its robust architecture and ease of use, making it a valuable tool for both