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hugging-face-model-trainer

Train language models on Hugging Face Jobs using TRL workflows.

9|2|Updated Mar 12, 2026
Search Tags:#training#GRPO#trackio#trl#SFT#DPO#hugging-face
One-click install
npx skills add https://github.com/hongmaple0820/agent-academy --skill hugging-face-model-trainer-hongmaple0820
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/hongmaple0820/agent-academy/tree/main/skills/ai-ml/hugging-face-model-trainer
Command: npx skills add https://github.com/hongmaple0820/agent-academy --skill hugging-face-model-trainer-hongmaple0820

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl>=0.12.0, peft>=0.7.0, transformers>=4.36.0, accelerate>=0.24.0, trackio, datasets, gguf, sentencepiece>=0.1.99, protobuf>=3.20.0, torch>=2.0.0, numpy, huggingface_hub>=0.20.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables end-to-end Transformer Reinforcement Learning (TRL) training of language models on Hugging Face Jobs, covering SFT, DPO, GRPO, trackio monitoring, hub pushes, and GGUF conversion.

Core Features & Use Cases

  • End-to-end TRL training on Hugging Face Jobs (SFT, DPO, GRPO)
  • Trackio monitoring, hub pushing, and GGUF conversion workflows
  • Includes dataset validation, cost estimation, and multi-method alignment use cases

Quick Start

Train a small TRL job by submitting a ready-to-run SFT example to HF Jobs.

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 train a language model with SFT and DPO using TRL on Hugging Face?▼

This Skill enables end-to-end TRL training on Hugging Face Jobs, supporting SFT, DPO, and GRPO workflows to train language models directly without managing local infrastructure.

Can I monitor my TRL training metrics and push the model to the Hugging Face Hub?▼

Yes, you can monitor TRL training metrics and push models to the Hugging Face Hub. The workflow integrates Trackio for real-time monitoring and automates Hub pushes alongside GGUF conversion.

Do I need to configure datasets and Hub authentication before starting a GRPO training job?▼

Yes, you need properly configured training scripts, prepared datasets, and Hugging Face Hub authentication before starting SFT, DPO, or GRPO training jobs to ensure seamless execution.

Does this workflow support exporting trained models to GGUF format for inference?▼

Yes, the TRL training workflow supports exporting trained models to GGUF format. It includes a GGUF conversion step after training and Hub push to prepare models for efficient inference.

What Python dependencies are required to run TRL-based training with Trackio monitoring?▼

Running TRL-based training with Trackio monitoring requires Python dependencies including trl, peft, transformers, accelerate, torch, datasets, and huggingface_hub to execute the end-to-end workflows.

Are there ready-to-run examples for submitting a small SFT job to Hugging Face Jobs?▼

Yes, the workflow includes ready-to-run SFT examples allowing you to quickly submit a small TRL training job to Hugging Face Jobs, complete with dataset validation and cost estimation.

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