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Andrew-Girgis
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huggingface-llm-trainer

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

Updated Jun 15, 2026
Search Tags:#model training#Hugging Face Jobs#language models#vision models#GGUF conversion
One-click install
npx skills add https://github.com/Andrew-Girgis/token-holdem --skill huggingface-llm-trainer-andrew-girgis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-llm-trainer
Source: https://github.com/Andrew-Girgis/token-holdem/tree/main/.agents/skills/huggingface-llm-trainer
Command: npx skills add https://github.com/Andrew-Girgis/token-holdem --skill huggingface-llm-trainer-andrew-girgis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hf-jobs, huggingface_hub, transformers, peft, trackio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill allows users to train or fine-tune language and vision models using Hugging Face Jobs infrastructure, eliminating the need for local GPU setup and providing scalable training capabilities.

Core Features & Use Cases

  • Cloud GPU Training: Train models on fully managed cloud GPUs with no local infrastructure required.
  • TRL Methods: Supports various training methods including SFT, DPO, GRPO, and reward modeling.
  • GGUF Conversion: Convert trained models to GGUF format for local deployment on llama.cpp, Ollama, LM Studio, etc.
  • Use Case: For a user who wants to fine-tune a language model on Hugging Face Jobs without having a local GPU setup.

Quick Start

Run the 'huggingface-llm-trainer' skill to train a language model on Hugging Face Jobs using a specific dataset and base model.

Frequently Asked Questions about huggingface-llm-trainer

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

FAQPage Schema
How do I fine-tune a language model without a local GPU?▼

You can fine-tune language models on fully managed cloud GPUs using Hugging Face Jobs infrastructure. This approach eliminates local GPU setup by providing scalable training capabilities directly through the Hugging Face Hub environment.

What training methods does Hugging Face Jobs support for language models?▼

Hugging Face Jobs supports Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), GRPO, and reward modeling. These TRL methods allow you to train or fine-tune language and vision models using scalable cloud infrastructure.

Can I convert a trained Hugging Face model to GGUF format for local deployment?▼

Yes, you can convert trained models to GGUF format. This conversion allows you to deploy your fine-tuned models locally on compatible platforms like llama.cpp, Ollama, or LM Studio after the cloud training completes.

Do I need a Hugging Face Hub account to train models on Hugging Face Jobs?▼

Yes, you need access to Hugging Face Jobs and the Hugging Face Hub to train models. These platforms manage the cloud GPU allocation and the storage of your datasets, base models, and trained checkpoints.

What is the best way to train vision models using Hugging Face infrastructure?▼

The best way to train vision models is using Hugging Face Jobs infrastructure, which automates the training process on managed cloud GPUs. This method supports various TRL training methods without requiring any local hardware setup.

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