What is TRL used for in this skill?

TRL (Transformer Reinforcement Learning) is used for post-training LLMs, specifically for instruction tuning and aligning models with human preferences using RLHF techniques.

Does this skill support Direct Preference Optimization (DPO)?

Absolutely. It provides complete workflows and CLI commands for DPO, allowing model alignment without the need for a separate reward model.

Do I need a high-end GPU for these workflows?

While LLM training is compute-intensive, this skill includes memory optimization strategies like gradient checkpointing and LoRA to help workflows run on mid-range enterprise GPUs.

Can I use this skill for memory-efficient training?

Yes, the skill includes configurations for GRPO and LoRA/QLoRA, which significantly reduce the VRAM required for reinforcement learning tasks.

What models are compatible with this skill?

It is designed to work with models supported by the Hugging Face Transformers library, such as Qwen, Llama, and Mistral, particularly those optimized for post-training.

TRL LLM Fine-Tuning & Alignment

Name: TRL LLM Fine-Tuning & Alignment
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Data Science & ML

Fine-tunes and aligns Large Language Models using Supervised Fine-Tuning and Reinforcement Learning from Human Feedback via the TRL library.

This skill provides a comprehensive framework for post-training Large Language Models using the Hugging Face TRL (Transformer Reinforcement Learning) library. It empowers developers to move beyond base models by implementing Supervised Fine-Tuning (SFT) for instruction following, Direct Preference Optimization (DPO) for alignment without complex reward models, and advanced reinforcement learning techniques like PPO and memory-efficient GRPO. Whether you are building a specialized assistant or aligning a model with specific human preferences, this skill provides the checklists, code patterns, and hardware-optimized configurations needed for production-grade AI research and engineering.

Key Features

01Direct Preference Optimization (DPO) for simplified model alignment

023,983 GitHub stars

03Pre-configured checklists and workflows for post-training scenarios

04Memory-efficient online reinforcement learning with GRPO

05Full RLHF pipelines including Reward Model training and PPO

06Supervised Fine-Tuning (SFT) for instruction-based training

Use Cases

01Aligning a base LLM to follow specific human instructions or safety guidelines

02Training specialized reward models for automated quality assessment and policy optimization

03Optimizing model behavior based on preference datasets using chosen/rejected pairs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills trl-fine-tuning

For use in Claude.ai and ChatGPT

Download Skill