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trl-training

Train transformer language models with SFT, DPO, GRPO, and RLOO via TRL.

1|Updated Jul 12, 2026
Search Tags:#llm#fine-tuning#huggingface#transformers#dpo#trl#rlhf
One-click install
npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill trl-training-tyler-r-kendrick
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trl-training
Source: https://github.com/Tyler-R-Kendrick/slm-training/tree/main/.agents/skills/trl-training
Command: npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill trl-training-tyler-r-kendrick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, accelerate, peft, datasets.

What problem does it solve?

This skill simplifies the complex process of post-training foundation models, removing the friction from implementing state-of-the-art alignment techniques like SFT, DPO, and GRPO.

Core Features & Use Cases

  • Multi-Method Training: Execute SFT, DPO, GRPO, RLOO, and Reward Model training via a unified CLI.
  • Resource Optimization: Leverage LoRA/PEFT and Accelerate integrations to train models on consumer-grade hardware.
  • Use Case: Fine-tune a base model on a custom instruction dataset using SFT, then align it with human preferences using DPO to improve conversational quality.

Quick Start

Use the trl-training skill to perform supervised fine-tuning on the Qwen2-0.5B model using the Capybara dataset with LoRA adapters enabled.

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 Hugging Face transformer model for instruction following?▼

You can fine-tune Hugging Face transformer models for instruction following using Supervised Fine-Tuning (SFT), aligning base models with custom instruction datasets to improve response accuracy.

What's the best way to align LLM outputs with human preferences using RLHF?▼

Align LLM outputs with human preferences using DPO, GRPO, or RLOO techniques for preference optimization, which refines conversational quality and response safety without complex reward modeling.

Can I train large language models on consumer-grade hardware with limited GPU memory?▼

Yes, you can train large language models on consumer-grade hardware by leveraging LoRA and PEFT integrations alongside Accelerate, which enables parameter-efficient distributed training under tight memory constraints.

Does this tool support both DPO and GRPO training methodologies for language model alignment?▼

Yes, it supports both DPO and GRPO training methodologies, providing a unified interface to execute diverse preference optimization algorithms alongside SFT and Reward Model training.

Do I need Accelerate and PEFT to run parameter-efficient fine-tuning on custom datasets?▼

Yes, you need Accelerate and PEFT dependencies to manage distributed training and parameter-efficient fine-tuning, alongside the TRL and Transformers libraries for processing custom datasets.

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