Documentation
README
TRL - Transformer Reinforcement Learning
Quick start
TRL provides post-training methods for aligning language models with human preferences.
Installation:
pip install trl transformers datasets peft accelerate
Supervised Fine-Tuning (instruction tuning):
from trl import SFTTrainer
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset, # Prompt-completion pairs
)
trainer.train()
DPO (align with preferences):
from trl import DPOTrainer, DPOConfig
config = DPOConfig(output_dir="model-dpo", beta=0.1)
trainer = DPOTrainer(
model=model,
args=config,
train_dataset=preference_dataset, # chosen/rejected pairs
processing_class=tokenizer
)
trainer.train()
Common workflows
This is the opening of the README. Read the full README on GitHub.