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hugging-face-model-trainer

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

1|Updated Feb 24, 2026
Search Tags:#fine-tuning#trackio#trl#hugging face jobs#llm training#gguf conversion
One-click install
npx skills add https://github.com/FacuM/yolo-agent --skill hugging-face-model-trainer-facum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/FacuM/yolo-agent/tree/main/.claude/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/FacuM/yolo-agent --skill hugging-face-model-trainer-facum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers>=4.36.0, peft>=0.7.0, torch>=2.0.0, accelerate>=0.24.0, huggingface_hub>=0.20.0, sentencepiece>=0.1.99, protobuf>=3.20.0, numpy, gguf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies and automates the process of training and fine-tuning large language models (LLMs) using cloud GPUs on Hugging Face infrastructure, eliminating the need for local GPU setups.

Core Features & Use Cases

  • Cloud-based LLM Training: Train models using TRL (SFT, DPO, GRPO) on Hugging Face Jobs.
  • GGUF Conversion: Convert trained models to GGUF format for local deployment (Ollama, LM Studio).
  • Automated Monitoring: Integrates with Trackio for real-time training progress visualization.
  • Use Case: Fine-tune a Qwen2.5 model on a custom dataset using SFT on Hugging Face Jobs, monitor its progress via Trackio, and automatically convert the resulting model to GGUF for local use with Ollama.

Quick Start

Use the hugging-face-model-trainer skill to fine-tune the Qwen/Qwen2.5-0.5B model using the trl-lib/Capybara dataset with 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 a language model on Hugging Face Jobs using TRL?▼

You can fine-tune a model using TRL methods like SFT, DPO, and GRPO directly on Hugging Face Jobs. This Skill automates the cloud GPU orchestration, allowing you to submit training jobs without managing local hardware infrastructure.

Can I convert my fine-tuned Hugging Face model to GGUF for local deployment?▼

Yes, you can convert fine-tuned models to GGUF format for local deployment. This Skill includes GGUF conversion capabilities, allowing you to run the trained LLM locally using compatible platforms like Ollama or LM Studio after the cloud training completes.

What is the best way to monitor LLM training progress on Hugging Face Jobs?▼

The best way to monitor LLM training progress is using Trackio integration. This Skill incorporates Trackio to provide real-time visualization of your training metrics while the model runs on Hugging Face Jobs infrastructure.

Do I need Hugging Face Hub authentication to save my fine-tuned model results?▼

Yes, you need Hugging Face Hub authentication to save your fine-tuned model results. This Skill requires Hub credentials to securely store the trained checkpoints and push the resulting models back to your repository.

Does fine-tuning on Hugging Face Jobs support SFT, DPO, and GRPO methods?▼

Yes, fine-tuning on Hugging Face Jobs supports SFT, DPO, and GRPO methods. This Skill leverages the TRL library to execute these distinct training algorithms, accommodating various supervised and preference optimization workflows.

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