Train transformer language models with reinforcement learning.
A comprehensive library to post-train foundation models
## 🎉 What's New **📜 Training beyond 1M tokens:** A new [long context guide](https://huggingface.co/docs/trl/long_context_training) walks through the four things that break as sequences grow — the loss, the positions, the activations and the memory of a single GPU — and ends on an example that trains Qwen3-8B on million-token sequences on one 8-GPU node. ## Overview TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), and Direct Preference Optimization (DPO). Built on top of the [🤗 Transformers](https://github.com/huggingface/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`](https://huggingface.co/docs/trl/sft_trainer), [`GRPOTrainer`](https://huggingface.co/docs/trl/grpo_trainer), [`DPOTrainer`](https://huggingface.co/docs/trl/dpo_trainer), [`KTOTrainer`](https://huggingface.co/docs/trl/kto_trainer) and more. - **Efficient and scalable**: - Leverages [🤗 Accelerate](https://github.com/huggingface/accelerate) to scale from single GPU to multi-node clusters using methods like [DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) and [DeepSpeed](https://github.com/deepspeedai/DeepSpeed). - Full integration with [🤗 PEFT](https://github.com/huggingface/peft) enables training on large models with modest hardware via quantization and LoRA/QLoRA. - Integrates [🦥 Unsloth](https://github.com/unslothai/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`: ```bash pip install trl ``` ### From source If you want to use the latest features before an official release, you can install TRL from source: ```bash 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: ```bash 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`](https://huggingface.co/docs/trl/sft_trainer): ```python 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`](https://huggingface.co/docs/trl/grpo_trainer) implements the [Group Relative Policy Optimization (GRPO) algorithm](https://huggingface.co/papers/2402.03300) that is more memory-efficient than PPO and was used to train [Deepseek AI's R1](https://huggingface.co/deepseek-ai/DeepSeek-R1). ```python from datasets import load_dataset from trl import GRPOTrainer from trl.rewards import accuracy_reward dataset = load_dataset("trl-lib/DeepMath-103K", split="train") trainer = GRPOTrainer( model="Qwen/Qwen2.5-0.5B-Instruct", reward_funcs=accuracy_reward, train_dataset=dataset, ) trainer.train() ``` > [!NOTE] > For reasoning models, use the `reasoning_accuracy_reward()` function for better results. ### `DPOTrainer` [`DPOTrainer`](https://huggingface.co/docs/trl/dpo_trainer) implements the popular [Direct Preference Optimization (DPO) algorithm](https://huggingface.co/papers/2305.18290) that was used to post-train [Llama 3](https://huggingface.co/papers/2407.21783) and many other models. Here is a basic example of how to use the `DPOTrainer`: ```python from datasets import load_dataset from trl import DPOTrainer dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train") trainer = DPOTrainer( model="Qwen/Qwen3-0.6B", train_dataset=dataset, ) trainer.train() ``` ### `KTOTrainer` [`KTOTrainer`](https://huggingface.co/docs/trl/kto_trainer) implements the [Kahneman-Tversky Optimization (KTO) algorithm](https://huggingface.co/papers/2402.01306), which aligns models from simple binary (desirable / undesirable) feedback rather than paired preferences. Here is a basic example of how to use the `KTOTrainer`: ```python from datasets import load_dataset from trl import KTOTrainer dataset = load_dataset("trl-lib/kto-mix-14k", split="train") trainer = KTOTrainer( model="Qwen/Qwen3-0.6B", train_dataset=dataset, ) trainer.train() ``` ### `RewardTrainer` Here is a basic example of how to use the [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer): ```python 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:** ```bash trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/Capybara \ --output_dir Qwen2.5-0.5B-SFT ``` **DPO:** ```bash trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ --dataset_name argilla/Capybara-Preferences \ --output_dir Qwen2.5-0.5B-DPO ``` **KTO:** ```bash trl kto --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ --dataset_name trl-lib/kto-mix-14k \ --output_dir Qwen2.5-0.5B-KTO ``` Read more about CLI in the [relevant documentation section](https://huggingface.co/docs/trl/clis) 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](https://github.com/huggingface/trl/blob/main/CONTRIBUTING.md) and make sure you make a dev install: ```bash 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: ```python from trl.experimental.new_trainer import NewTrainer ``` Read more in the [Experimental docs](https://huggingface.co/docs/trl/experimental_overview). ## Citation ```bibtex @software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020} } ``` ## License This repository's source code is available under the [Apache-2.0 License](LICENSE).