skills.rest
hhhi21g
hhhi21gCOMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-with-trl

Fine-tune language models with SFT, DPO, PPO, and GRPO.

Updated Apr 11, 2026
Search Tags:#fine-tuning#reinforcement learning#RLHF#transformers#language models
One-click install
npx skills add https://github.com/hhhi21g/HealthCenter --skill fine-tuning-with-trl-hhhi21g
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/hhhi21g/HealthCenter/tree/main/.codex/skills/trl-fine-tuning
Command: npx skills add https://github.com/hhhi21g/HealthCenter --skill fine-tuning-with-trl-hhhi21g

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of aligning language models with human preferences and optimizing their performance through reinforcement learning.

Core Features & Use Cases

  • Post-Training Fine-Tuning: Use SFT, DPO, PPO, and GRPO for various reinforcement learning tasks.
  • RLHF Pipeline: Full RLHF pipeline from SFT to PPO for human-aligned models.
  • Use Case: Train a language model to generate coherent summaries from technical documents using a combination of SFT and PPO.

Quick Start

To start fine-tuning your model with TRL, run the following command:

python -m trl.scripts.sft

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 fine-tune a language model with reinforcement learning using TRL?▼

You can fine-tune language models with reinforcement learning by executing TRL scripts to run SFT, DPO, PPO, or GRPO algorithms. This optimizes models for RLHF pipelines and preference alignment tasks using trl and transformers.

What's the difference between SFT, DPO, PPO, and GRPO for preference alignment?▼

SFT supervised-tunes base models, DPO directly optimizes preferences without a reward model, PPO uses reinforcement learning with rewards, and GRPO optimizes with group-relative advantages for RLHF pipelines.

Do I need PEFT and Accelerate to run TRL for RLHF pipelines?▼

Yes, PEFT and Accelerate are required dependencies for running TRL. You also need trl, transformers, datasets, and torch installed to execute the reinforcement learning fine-tuning scripts properly.

Can I use TRL to train a model for generating technical document summaries?▼

Yes, you can train a model to generate coherent technical document summaries using TRL. Combine SFT for supervised fine-tuning and PPO for reinforcement learning optimization to achieve human-aligned outputs.

How does the TRL SFT script work for post-training language model optimization?▼

The TRL SFT script executes supervised fine-tuning to post-train language models. It leverages transformers and datasets to process inputs, serving as the foundational step before applying PPO or DPO.

Related Skills

Explore More Software Engineering Skills→
blueskies1818blueskies1818
1

simpo-training

Optimize LLM preferences without a reference model using SimPO.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

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

Community
Advanced
jonnabiojonnabio
6

peft-fine-tuning

Fine-tune large language models up to 70B parameters using PEFT techniques.

Community
Advanced
CHENyiru3CHENyiru3

trl

Train language models with reinforcement learning workflows including SFT, reward modeling, RLHF, and DPO.

Community
Advanced