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:
- Leverages 🤗 Accelerate to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed.
- Full integration with
PEFTenables 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 and interact with models without needing to write code.
-
Trainers: Various fine-tuning methods are easily accessible via trainers like
SFTTrainer,DPOTrainer,RewardTrainer,ORPOTrainerand more. -
AutoModels: Use pre-defined model classes like
AutoModelForCausalLMWithValueHeadto simplify reinforcement learning (RL) with LLMs.
Install the library using pip:
pip install trlIf 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.gitIf you want to use the examples you can clone the repository with the following command:
git clone https://github.com/huggingface/trl.gitYou 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:
SFT:
trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
--dataset_name trl-lib/Capybara \
--output_dir Qwen2.5-0.5B-SFTDPO:
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-InstructRead 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/
pip install -e .[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}}
}This repository's source code is available under the Apache-2.0 License.