skills.rest
twjarviszyra-web
twjarviszyra-webCOMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-with-trl

Train and align language models with TRL using supervised fine-tuning and preference optimization.

3|Updated Apr 4, 2026
Search Tags:#fine-tuning#ppo#dpo#trl#rlhf#llm alignment#reward modeling
One-click install
npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill fine-tuning-with-trl-twjarviszyra-web
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/twjarviszyra-web/hermes-unbound/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill fine-tuning-with-trl-twjarviszyra-web

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you turn a base language model into an instruction-following or preference-aligned model without building the training pipeline from scratch.

Core Features & Use Cases

  • Supervised fine-tuning: Train on prompt-completion, chat, or text datasets for instruction following and domain adaptation.
  • Preference alignment: Use DPO and its variants to improve responses from chosen and rejected examples.
  • RLHF workflows: Train reward models and run PPO or GRPO for full reinforcement-learning-based alignment.
  • Use case: Fine-tune an open model for a support chatbot, align it to preferred answer styles, or build a reward-driven training loop for a custom assistant.

Quick Start

Use this Skill to fine-tune an LLM with TRL by selecting the training method that matches your dataset and alignment goal.

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 for instruction following and preference alignment?▼

You can fine-tune a language model for instruction following and preference alignment by applying supervised fine-tuning, DPO, or PPO using TRL-compatible training configurations with prompt-completion datasets.

What is the difference between DPO and RLHF for LLM alignment?▼

DPO improves responses directly from chosen and rejected examples for preference alignment, whereas RLHF workflows train a separate reward model and run PPO or GRPO for reinforcement-learning-based alignment.

Can I use TRL with Hugging Face Transformers and PEFT for memory-efficient training?▼

Yes, TRL supports memory-efficient model training workflows by integrating with Hugging Face Transformers, datasets, peft, accelerate, and torch for domain adaptation and alignment.

What datasets do I need for supervised fine-tuning and preference optimization?▼

Supervised fine-tuning requires prompt-completion, chat, or text datasets, while preference optimization needs datasets containing chosen and rejected examples to train the model effectively.

How do I train a reward model for a custom assistant RLHF pipeline?▼

Train a reward model for a custom assistant by using TRL to process preference datasets, then running PPO or GRPO to build a reward-driven training loop for reinforcement-learning-based alignment.

When should I use GRPO instead of PPO for online reinforcement learning?▼

Use GRPO or PPO for online reinforcement learning when building reward-driven training loops, selecting the method based on your specific RLHF pipeline and custom assistant alignment requirements.

Related Skills

Explore More Education & Research Skills→
ricablericable

fine-tuning-with-trl

Fine-tune large language models with TRL for supervised and preference alignment.

Community
Advanced
superfhpsuperfhp

serving-llms-vllm

Serve LLMs with OpenAI-compatible endpoints using vLLM.

Community
Advanced
huggingfacehuggingface
10.9k

hugging-face-model-trainer

Train and fine-tune language models on Hugging Face Jobs using TRL methods.

Official
Advanced
lenadlmlenadlm

fine-tuning-with-trl

Fine-tune language models with TRL for reinforcement learning and alignment.

Community
Advanced