TRL - Transformer Reinforcement Learning

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A comprehensive library to post-train foundation models

🎉 What's New

OpenEnv Integration: TRL now supports OpenEnv, the open-source framework from Meta for defining, deploying, and interacting with environments in reinforcement learning and agentic workflows.

Explore how to seamlessly integrate TRL with OpenEnv in our dedicated documentation.

Overview

TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). Built on top of the 🤗 Transformers ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.

Highlights

• Trainers: Various fine-tuning methods are easily accessible via trainers like SFTTrainer, GRPOTrainer, DPOTrainer, RewardTrainer and more.

• Efficient and scalable:

  • Leverages 🤗 Accelerate to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed.
  • Full integration with 🤗 PEFT enables training on large models with modest hardware via quantization and LoRA/QLoRA.
  • Integrates 🦥 Unsloth for accelerating training using optimized kernels.

• Command Line Interface (CLI): A simple interface lets you fine-tune with models without needing to write code.

Installation

Python Package

Install the library using pip:

pip install trl

From source

If you want to use the latest features before an official release, you can install TRL from source:

pip install git+https://github.com/huggingface/trl.git

Repository

If you want to use the examples you can clone the repository with the following command:

git clone https://github.com/huggingface/trl.git

Quick Start

For more flexibility and control over training, TRL provides dedicated trainer classes to post-train language models or PEFT adapters on a custom dataset. Each trainer in TRL is a light wrapper around the 🤗 Transformers trainer and natively supports distributed training methods like DDP, DeepSpeed ZeRO, and FSDP.

SFTTrainer

Here is a basic example of how to use the SFTTrainer:

from trl import SFTTrainer
from datasets import load_dataset

dataset = load_dataset("trl-lib/Capybara", split="train")

trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
)
trainer.train()

GRPOTrainer

GRPOTrainer implements the Group Relative Policy Optimization (GRPO) algorithm that is more memory-efficient than PPO and was used to train Deepseek AI's R1.

from datasets import load_dataset
from trl import GRPOTrainer

dataset = load_dataset("trl-lib/tldr", split="train")

# Dummy reward function: count the number of unique characters in the completions
def reward_num_unique_chars(completions, **kwargs):
    return [len(set(c)) for c in completions]

trainer = GRPOTrainer(
    model="Qwen/Qwen2-0.5B-Instruct",
    reward_funcs=reward_num_unique_chars,
    train_dataset=dataset,
)
trainer.train()

DPOTrainer

DPOTrainer implements the popular Direct Preference Optimization (DPO) algorithm that was used to post-train Llama 3 and many other models. Here is a basic example of how to use the DPOTrainer:

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
trainer = DPOTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    processing_class=tokenizer
)
trainer.train()

RewardTrainer

Here is a basic example of how to use the RewardTrainer:

from trl import RewardTrainer
from datasets import load_dataset

dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")

trainer = RewardTrainer(
    model="Qwen/Qwen2.5-0.5B-Instruct",
    train_dataset=dataset,
)
trainer.train()

Command Line Interface (CLI)

You can use the TRL Command Line Interface (CLI) to quickly get started with post-training methods like Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO):

SFT:

trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
    --dataset_name trl-lib/Capybara \
    --output_dir Qwen2.5-0.5B-SFT

DPO:

trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
    --dataset_name argilla/Capybara-Preferences \
    --output_dir Qwen2.5-0.5B-DPO 

Read more about CLI in the relevant documentation section or use --help for more details.

Development

If you want to contribute to trl or customize it to your needs make sure to read the contribution guide and make sure you make a dev install:

git clone https://github.com/huggingface/trl.git
cd trl/
pip install -e .[dev]

Experimental

A minimal incubation area is available under trl.experimental for unstable / fast-evolving features. Anything there may change or be removed in any release without notice.

Example:

from trl.experimental.new_trainer import NewTrainer

Read more in the Experimental docs.

Citation

@misc{vonwerra2022trl,
  author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
  title = {TRL: Transformer Reinforcement Learning},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

License

This repository's source code is available under the Apache-2.0 License.