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huggingface-llm-trainer

Skill

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts

0Machine Learning

Install

git clone https://github.com/huggingface/skills.git ~/.claude/skills/huggingface-llm-trainer

What is huggingface-llm-trainer?

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~156 tokens of context used while enabled, before you invoke anything

Documentation

README · ~16 min read

TRL Training on Hugging Face Jobs

Overview

Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub.

TRL provides multiple training methods:

  • SFT (Supervised Fine-Tuning) - Standard instruction tuning
  • DPO (Direct Preference Optimization) - Alignment from preference data
  • GRPO (Group Relative Policy Optimization) - Online RL training
  • Reward Modeling - Train reward models for RLHF

For detailed TRL method documentation:

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