TRL - Transformer Reinforcement Learning

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.

Efficient and scalable :

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

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

AutoModels : Use pre-defined model classes like AutoModelForCausalLMWithValueHead to simplify reinforcement learning (RL) with LLMs.

Install the library using pip :

pip install trl

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

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

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

You can use the TRL Command Line Interface (CLI) to quickly get started with Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO), or vibe check your model with the chat CLI:

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

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

Chat:

trl chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct

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

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.

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

from trl import SFTConfig, SFTTrainer
from datasets import load_dataset
dataset = load_dataset("trl-lib/Capybara", split="train")
training_args = SFTConfig(output_dir="Qwen/Qwen2.5-0.5B-SFT")
trainer = SFTTrainer(
    args=training_args,
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
trainer.train()

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

from trl import RewardConfig, RewardTrainer
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct", num_labels=1
model.config.pad_token_id = tokenizer.pad_token_id
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = RewardConfig(output_dir="Qwen2.5-0.5B-Reward", per_device_train_batch_size=2)
trainer = RewardTrainer(
    args=training_args,
    model=model,
    processing_class=tokenizer,
    train_dataset=dataset,
trainer.train()

RLOOTrainer implements a REINFORCE-style optimization for RLHF that is more performant and memory-efficient than PPO. Here is a basic example of how to use the RLOOTrainer :

from trl import RLOOConfig, RLOOTrainer, apply_chat_template
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
reward_model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct", num_labels=1
ref_policy = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
policy = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback-prompt")
dataset = dataset.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer})
dataset = dataset.map(lambda x: tokenizer(x["prompt"]), remove_columns="prompt")
training_args = RLOOConfig(output_dir="Qwen2.5-0.5B-RL")
trainer = RLOOTrainer(
    config=training_args,
    processing_class=tokenizer,
    policy=policy,
    ref_policy=ref_policy,
    reward_model=reward_model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
trainer.train()

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()

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/
make dev

@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.