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hugging-face-model-trainer

Train and fine-tune LLMs on Hugging Face Jobs using TRL.

Updated Mar 8, 2026
Search Tags:#llm#fine-tuning#cloud#training#gguf#trl#hugging face
One-click install
npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill hugging-face-model-trainer-brivaro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/brivaro/brivaro-ai-wizard/tree/main/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill hugging-face-model-trainer-brivaro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl>=0.12.0, peft>=0.7.0, transformers>=4.36.0, accelerate>=0.24.0, trackio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of training and fine-tuning large language models (LLMs) on Hugging Face's cloud infrastructure, eliminating the need for local GPU setups and complex environment management.

Core Features & Use Cases

  • Cloud-based Training: Train models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure.
  • Multiple Training Methods: Supports SFT, DPO, GRPO, and reward modeling.
  • GGUF Conversion: Convert trained models to GGUF format for local deployment (Ollama, LM Studio).
  • Automated Monitoring: Integrates with Trackio for real-time job progress visualization.
  • Use Case: A user wants to fine-tune a Qwen2.5-0.5B model on a custom dataset using DPO. This Skill handles script creation, job submission, monitoring, and saving the model to the Hugging Face Hub.

Quick Start

Use the hugging-face-model-trainer skill to fine-tune the Qwen/Qwen2.5-0.5B model on the trl-lib/Capybara dataset using SFT.

Frequently Asked Questions about hugging-face-model-trainer

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

FAQPage Schema
How do I fine-tune an LLM on Hugging Face cloud infrastructure without a local GPU?▼

Fine-tune LLMs on Hugging Face Jobs infrastructure using the TRL library, eliminating the need for local GPU setups and complex environment management. It handles script creation, job submission, and saves models to the Hub.

What training methods does TRL support for LLM fine-tuning on Hugging Face Jobs?▼

TRL supports supervised fine-tuning (SFT), direct preference optimization (DPO), group relative policy optimization (GRPO), and reward modeling for training large language models on Hugging Face infrastructure.

Can I convert fine-tuned models to GGUF format for local deployment with Ollama?▼

Yes, the training process includes automated GGUF conversion, enabling you to deploy fine-tuned models locally using tools like Ollama or LM Studio after the Hugging Face Job completes.

How do I monitor real-time progress of my Hugging Face Jobs training run?▼

Monitor real-time job progress visualization by integrating Trackio into your Hugging Face Jobs workflow, allowing you to track the status of your TRL training and fine-tuning tasks.

Do I need PEFT and accelerate to train models using Hugging Face Jobs?▼

Yes, training relies on several dependencies including PEFT for parameter-efficient fine-tuning, accelerate for distributed training, transformers, and TRL version 0.12.0 or higher.

What is the best way to run DPO on a custom dataset using Hugging Face Jobs?▼

Use the hugging-face-model-trainer skill to fine-tune models like Qwen2.5-0.5B on custom datasets using DPO. It manages the TRL script creation, cloud job submission, and model saving automatically.

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