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model-trainer

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

18|8|Updated Apr 2, 2026
Search Tags:#Hugging Face Jobs#TRL#language models#cloud GPU training#GGUF conversion
One-click install
npx skills add https://github.com/honysyang/skill-security-scanner --skill model-trainer-honysyang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-trainer
Source: https://github.com/honysyang/skill-security-scanner/tree/main/malicious-skills-research/hf-llm-trainer
Command: npx skills add https://github.com/honysyang/skill-security-scanner --skill model-trainer-honysyang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, peft, accelerate, huggingface_hub, sentencepiece, protobuf, numpy, gguf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of training and fine-tuning language models using Transformer Reinforcement Learning (TRL) on Hugging Face Jobs infrastructure, without the need for local GPU setup.

Core Features & Use Cases

  • Cloud GPU Training: Leverages Hugging Face Jobs for training language models on cloud GPUs.
  • TRL Methods: Supports various TRL training methods including SFT, DPO, GRPO, and reward modeling.
  • GGUF Conversion: Converts trained models to GGUF format for local deployment with llama.cpp, Ollama, and LM Studio.
  • Use Case: Ideal for users who need to train language models on large datasets and require the flexibility of cloud-based training and local deployment options.

Quick Start

Run the model-trainer skill with the following command: model-trainer train --model_name_or_path Qwen/Qwen2.5-0.5B --dataset_name trl-lib/Capybara

Frequently Asked Questions about 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 a language model on Hugging Face Jobs by running the model-trainer command with your target model and dataset, leveraging cloud GPUs for Transformer Reinforcement Learning without needing local hardware.

What TRL training methods are supported for cloud GPU training?▼

Supported TRL training methods include Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and reward modeling for language models.

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

Yes, you can convert trained models to GGUF format for local deployment with llama.cpp, Ollama, and LM Studio using the built-in GGUF conversion functionality.

Do I need to install transformers and peft to use Hugging Face Jobs for training?▼

Yes, you need Python with transformers, peft, accelerate, huggingface_hub, sentencepiece, and gguf libraries installed to run TRL training jobs on Hugging Face infrastructure.

What is the best way to start training a language model using TRL?▼

The best way to start TRL training is by running the model-trainer skill with a base model and dataset, such as using Qwen2.5-0.5B with the Capybara dataset.

Why use Hugging Face Jobs for Transformer Reinforcement Learning instead of local training?▼

Hugging Face Jobs provides cloud GPUs for Transformer Reinforcement Learning, eliminating the need for local GPU setup and enabling training on large datasets with flexible deployment options.

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