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hugging-face-model-trainer

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

1|Updated Jan 22, 2026
Search Tags:#TRL#GGUF#Trackio#train#Hugging Face#Jobs
One-click install
npx skills add https://github.com/sitammeur/ml-agent-skills --skill hugging-face-model-trainer-sitammeur
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/sitammeur/ml-agent-skills/tree/main/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/sitammeur/ml-agent-skills --skill hugging-face-model-trainer-sitammeur

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl>=0.12.0, peft>=0.7.0, transformers>=4.36.0, accelerate>=0.24.0, trackio, torch>=2.0.0, 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 enables teams to train and fine-tune language models using Transformer Reinforcement Learning (TRL) on Hugging Face Jobs, eliminating the need for local GPUs and complex setup.

Core Features & Use Cases

  • SFT, DPO, GRPO, and Reward Modeling: supports multiple TRL methods and GGUF conversion for local deployment.
  • Trackio-enabled Monitoring: integrates Trackio for real-time training visibility.
  • Hub Persistence: ensures models and checkpoints are pushed to Hugging Face Hub for durability.

Quick Start

Use the included templates in scripts/train_sft_example.py as starting point, or submit a training job inline with hf_jobs to demonstrate a small TRL workflow.

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 language models with Hugging Face Jobs without local GPUs?▼

You can train language models with Hugging Face Jobs by using TRL scripts to execute SFT, DPO, GRPO, and Reward Modeling workflows remotely, eliminating the need for local GPU infrastructure.

What are TRL methods like SFT, DPO, and GRPO used for in fine-tuning?▼

TRL methods like SFT, DPO, GRPO, and Reward Modeling are used to train and fine-tune language models by applying transformer reinforcement learning techniques directly on Hugging Face Jobs.

Do I need an HF_TOKEN to submit TRL training jobs?▼

Yes, you need an HF_TOKEN for hub authentication to ensure secure execution when running TRL training jobs and to persist models and checkpoints to the Hugging Face Hub.

Can I convert a fine-tuned model to GGUF for local deployment?▼

Yes, you can convert your fine-tuned model to GGUF format for local deployment after completing the TRL training process on Hugging Face Jobs.

How can I monitor Hugging Face training jobs in real-time?▼

You can monitor Hugging Face training jobs in real-time by integrating Trackio, which provides visibility into your TRL training metrics as the language model fine-tuning progresses.

What's the best way to start a TRL fine-tuning workflow?▼

The best way to start a TRL fine-tuning workflow is by using the included templates in scripts/train_sft_example.py as a starting point, or submitting a small training job inline with hf_jobs.

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