skills.rest
hochoa13
hochoa13COMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-with-trl

Fine-tune LLMs with SFT, DPO, PPO/GRPO, and reward model training.

Updated Mar 13, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/hochoa13/Asistente-Harlest --skill fine-tuning-with-trl-hochoa13
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill fine-tuning-with-trl-hochoa13

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch, and includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of aligning Large Language Models (LLMs) with human preferences and instructions, making them more helpful, harmless, and honest.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Instruction tuning for better response generation.
  • Direct Preference Optimization (DPO): Aligning models with human preferences using pairwise data.
  • Reinforcement Learning (PPO/GRPO): Optimizing models based on a reward signal, often from a reward model.
  • Reward Model Training: Creating models that can score the quality of LLM generations.
  • Use Case: You have a base LLM that generates factually correct but sometimes unhelpful or biased responses. Use this Skill to fine-tune it using human feedback data to produce outputs that are more aligned with desired behavior.

Quick Start

Use the fine-tuning-with-trl skill to perform supervised fine-tuning on the Qwen/Qwen2.5-0.5B model using the Capybara dataset.

Frequently Asked Questions about fine-tuning-with-trl

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

FAQPage Schema
How do I align LLMs with human preferences using reinforcement learning?▼

Aligning LLMs with human preferences uses techniques like DPO and PPO to optimize model behavior. This Skill applies TRL library methods to fine-tune base models using human feedback data, producing responses that are more helpful, harmless, and honest.

What's the best way to perform direct preference optimization on a HuggingFace model?▼

Direct Preference Optimization (DPO) aligns models with human preferences using pairwise comparison data. This Skill leverages the TRL library to apply DPO directly to HuggingFace Transformers models, bypassing the need for an explicit reward model during the preference alignment phase.

Does this TRL fine-tuning workflow require separate reward model training?▼

Reward model training is supported but optional depending on the alignment strategy chosen. While PPO and GRPO require a reward signal often derived from a trained reward model, DPO directly optimizes the policy using pairwise preference data without needing a separate reward model.

Can I use PEFT and accelerate for LLM alignment on limited hardware?▼

PEFT and accelerate are integrated dependencies for optimizing LLM alignment on limited hardware. These libraries enable parameter-efficient fine-tuning and distributed processing, allowing reinforcement learning workflows from the TRL library to run efficiently across constrained environments.

When do I need GRPO instead of PPO for reward optimization?▼

GRPO and PPO are both reinforcement learning techniques for reward optimization supported by TRL. GRPO is often used as an alternative to PPO for optimizing models based on a reward signal, varying primarily in how the advantage function is estimated during the fine-tuning process.

Related Skills

Explore More Software Engineering Skills→
zhuangbiaoweizhuangbiaowei
2

fine-tuning-with-trl

Fine-tune LLMs with SFT, DPO, PPO, and GRPO using HuggingFace Transformers.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
Tyler-R-KendrickTyler-R-Kendrick
1

trl-training

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

Community
Advanced
carterwayneskhizeinecarterwayneskhizeine
19

fine-tuning-with-trl

Fine-tunes language models with TRL for RLHF-style alignment using SFT, DPO, GRPO, and PPO workflows.

Community
Advanced