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trl

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Transformer Reinforcement Learning library (TRL). Supervised fine-tuning (SFT), reward modeling, PPO, DPO, KTO, GRPO for RLHF. Process reward models and language model alignment.

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How to use this skill

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Source SKILL.md: https://github.com/mkurman/zorai/blob/HEAD/skills/scientific-skills/trl/SKILL.md

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Overview

TRL (Transformer Reinforcement Learning) is Hugging Face's library for RLHF — SFT, reward modeling, PPO, DPO, KTO, and GRPO. It's the standard post-training toolkit for aligning language models with human preferences.

Installation

uv pip install trl

SFT

from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")

trainer = SFTTrainer(
    model=model, tokenizer=tokenizer,
    train_dataset=dataset,
    args=dict(per_device_train_batch_size=4, learning_rate=2e-5, max_seq_length=2048),
)
trainer.train()

DPO

from trl import DPOTrainer

dpo = DPOTrainer(
    model=model, ref_model=ref_model, tokenizer=tokenizer,
    train_dataset=preference_dataset,
    args=dict(per_device_train_batch_size=4, max_length=2048),
)
dpo.train()

References