trl-training

Train and fine-tune transformer language models via TRL CLI.

19.0k|2.9k|Updated Mar 27, 2020
One-click install
npx skills add https://github.com/huggingface/trl --skill trl-training-huggingface
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trl-training
Source: https://github.com/huggingface/trl/tree/main/trl/skills/trl-training
Command: npx skills add https://github.com/huggingface/trl --skill trl-training-huggingface

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TRL provides a unified CLI-based workflow to train and fine-tune transformer language models across reinforcement learning and alignment paradigms, reducing setup friction and keeping experiments reproducible.

Core Features & Use Cases

  • SFT, DPO, GRPO, KTO, RLOO, and Reward Model training via CLI for scalable post-training and alignment workflows.
  • Ecosystem integration with Hugging Face Transformers, Accelerate, and PEFT to enable training on diverse hardware and model sizes.
  • Use Case: Fine-tune a base model on a preference dataset using TRL to improve alignment with human feedback.

Quick Start

Start training a model with TRL by running a TRL sft command using your model and dataset.

Frequently Asked Questions about trl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a transformer model using a CLI?▼

You can fine-tune transformer models using a CLI by running TRL training commands. It provides a unified, reproducible workflow for post-training tasks like SFT and DPO across Hugging Face projects.

What training methods are available for RLHF and alignment?▼

Available training methods for RLHF and alignment include SFT, DPO, GRPO, KTO, RLOO, and reward-model training. These workflows are executed via CLI to improve human feedback alignment.

Can I use Accelerate and PEFT for scalable transformer training?▼

Yes, you can use Accelerate and PEFT for scalable transformer training. TRL integrates with the Hugging Face ecosystem to enable training across diverse hardware configurations and model sizes.

Do I need a specific Python environment to run TRL training commands?▼

Yes, you need a Python environment with TRL dependencies and CLI access. This setup is required to execute training commands and manage post-training workflows for transformer language models.

What is the best way to improve model alignment with a preference dataset?▼

The best way to improve alignment is fine-tuning a base model on a preference dataset using TRL. This applies preference-based training methods like DPO via CLI to align outputs with human feedback.