hugging-face-model-trainer

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

Updated Mar 12, 2026

Search Tags:#llm #fine-tuning #training #gguf #trl #hugging face jobs

Ready to run

npx skills add https://github.com/3kpro/aiarsenal --skill hugging-face-model-trainer-3kpro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/3kpro/aiarsenal/tree/main/hugging-face-skills/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/3kpro/aiarsenal --skill hugging-face-model-trainer-3kpro

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 simplifies the process of training and fine-tuning large language models (LLMs) by leveraging Hugging Face's managed cloud infrastructure, eliminating the need for local GPU setup and complex environment management.

Core Features & Use Cases

Cloud-Based Training: Train models on Hugging Face Jobs infrastructure without requiring local GPUs.
Multiple Training Methods: Supports SFT, DPO, GRPO, and reward modeling using the TRL library.
GGUF Conversion: Converts trained models to GGUF format for local deployment with tools like Ollama and LM Studio.
Use Case: You want to fine-tune a Qwen2.5 model on a custom dataset using DPO. This Skill allows you to submit the training job directly to Hugging Face Jobs, monitor its progress, and automatically save the resulting model to the Hugging Face Hub.

Quick Start

Use the hugging face model trainer skill to fine-tune the Qwen2.5-0.5B model using SFT on the trl-lib/Capybara dataset for 3 epochs.

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 an LLM on Hugging Face Jobs without a local GPU?▼

You can fine-tune an LLM on Hugging Face Jobs without a local GPU by submitting training tasks to Hugging Face's managed cloud infrastructure. This Skill handles hardware selection and cost estimation automatically using the TRL library.

What training methods does the TRL library support for LLM fine-tuning?▼

The TRL library supports SFT, DPO, GRPO, and reward modeling for LLM fine-tuning. This Skill facilitates these methods on Hugging Face Jobs, managing dataset preparation and Hub authentication for seamless cloud-based model development.

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

Yes, you can convert a fine-tuned Hugging Face model to GGUF format for local deployment. This Skill integrates GGUF conversion, allowing you to deploy trained models locally using tools like Ollama and LM Studio.

Do I need to manage environment dependencies like peft and transformers for Hugging Face Jobs?▼

No, you do not need to manually manage environment dependencies like peft, transformers, and accelerate for Hugging Face Jobs. This Skill handles the environment setup and Hub authentication to streamline cloud-based model training.

What is the best way to train a Qwen2.5 model with DPO using Hugging Face infrastructure?▼

The best way to train a Qwen2.5 model with DPO using Hugging Face infrastructure is to submit the training job directly to Hugging Face Jobs. This Skill manages the DPO process, monitors progress, and saves the resulting model to the Hub.