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hugging-face-model-trainer

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

1|Updated Jan 29, 2026
Search Tags:#llm#fine-tuning#cloud#gpu#training#trl#hugging face
One-click install
npx skills add https://github.com/yuxia214/-22 --skill hugging-face-model-trainer-yuxia214
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/yuxia214/-22/tree/main/.cursor/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/yuxia214/-22 --skill hugging-face-model-trainer-yuxia214

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 enables users to train or fine-tune language models using TRL on Hugging Face infrastructure without needing local GPUs, simplifying cloud-based model development.

Core Features & Use Cases

  • Cloud GPU Training: Run SFT, DPO, GRPO, and reward modeling jobs on Hugging Face Jobs.
  • GGUF Conversion: Convert trained models to GGUF for local deployment.
  • Automated Hub Saving: Ensure all trained models are saved to the Hugging Face Hub.
  • Use Case: A user wants to fine-tune a Qwen model on a custom dataset using DPO. They can use this Skill to submit a training job directly to Hugging Face Jobs, monitor its progress, and have the final model automatically pushed to the Hub.

Quick Start

Use the hugging-face-model-trainer skill to fine-tune the Qwen/Qwen2.5-0.5B model using the 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 without a local GPU?▼

You can fine-tune language models on Hugging Face Jobs without local GPUs by using TRL. This Skill handles SFT, DPO, GRPO, and reward modeling jobs directly on cloud infrastructure.

Can I use TRL to run DPO and SFT training jobs on cloud GPUs?▼

Yes, TRL supports running SFT, DPO, GRPO, and reward modeling jobs on cloud GPUs. This Skill facilitates submitting these training jobs directly to Hugging Face Jobs infrastructure.

How do I convert a fine-tuned Hugging Face model to GGUF format for local deployment?▼

You can convert fine-tuned models to GGUF format for local deployment using this Skill. It includes automated GGUF conversion as part of the cloud GPU training and fine-tuning workflow.

Do I need to manually push my trained model to the Hugging Face Hub after training?▼

No, manual pushing is unnecessary because this Skill features automated Hub saving. All models trained or fine-tuned using TRL on Hugging Face Jobs are automatically saved to the Hub.

How does Trackio monitor cloud GPU training workflows for TRL models?▼

Trackio monitors cloud GPU training workflows by tracking progress during TRL fine-tuning. This Skill integrates Trackio monitoring to provide visibility into SFT, DPO, and GRPO jobs on Hugging Face infrastructure.

What are the limitations of using Hugging Face Jobs for GRPO and reward modeling?▼

Hugging Face Jobs requires specific dependencies including TRL, PEFT, Transformers, and Accelerate. Users must validate datasets and estimate hardware costs before submitting GRPO and reward modeling jobs.

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