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hugging-face-model-trainer

Train language models with TRL on Hugging Face Jobs using SFT, DPO, and GRPO workflows.

2|Updated Apr 2, 2026
Search Tags:#llm#training#huggingface#dpo#trl#sft#grpo
One-click install
npx skills add https://github.com/kaone31056789/KritakaPrajna --skill hugging-face-model-trainer-kaone31056789
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/kaone31056789/KritakaPrajna/tree/main/.verdent/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/kaone31056789/KritakaPrajna --skill hugging-face-model-trainer-kaone31056789

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl>=0.12.0, peft>=0.7.0, transformers>=4.36.0, accelerate>=0.24.0, trackio, datasets, unsloth, tensorboard, torch>=2.0.0, huggingface_hub>=0.20.0, sentencepiece>=0.1.99, protobuf>=3.20.0, numpy, gguf, transformers==4.57.3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides production-ready templates and guidance to train language models using TRL on Hugging Face Jobs, addressing the complexity of wiring together training methods, dataset validation, hub persistence, monitoring, and deployment workflows.

Core Features & Use Cases

  • Production-ready TRL templates for SFT, DPO, and GRPO workflows including hub push and tracking
  • Built-in dataset validation, cost estimation, and Trackio monitoring for reliable experiments
  • GGUF conversion guidance and tooling for local deployment and edge inference

Quick Start

Submit a TRL training job using the inline UV templates to fine-tune a language model on Hugging Face 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 fine-tune a language model using TRL on Hugging Face Jobs?▼

Fine-tune a language model using TRL by submitting inline UV templates to Hugging Face Jobs, applying SFT, DPO, or GRPO workflows with production-ready scripts, dataset validation, and hub integration.

What is the difference between SFT, DPO, and GRPO for LLM training?▼

SFT, DPO, and GRPO are distinct TRL training workflows for language models: SFT applies supervised fine-tuning, DPO uses direct preference optimization, and GRPO implements group relative policy optimization.

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

Yes, you can convert a fine-tuned Hugging Face model to GGUF format for local deployment and edge inference using the GGUF conversion guidance and tooling included in the training skill.

Does TRL training on Hugging Face support cost tracking and experiment monitoring?▼

TRL training on Hugging Face supports cost tracking and experiment monitoring through built-in Trackio monitoring, cost estimation helpers, and TensorBoard integration for reliable experiments.

What dependencies do I need to run TRL training workflows with Hugging Face?▼

Running TRL training workflows requires trl, peft, transformers, accelerate, torch, datasets, and huggingface_hub, with inline dependency headers enforced by the training templates.

Why use Hugging Face Jobs instead of other platforms for LLM training?▼

Hugging Face Jobs provides integrated hub persistence, built-in dataset validation, cost estimation, and monitoring, reducing the complexity of wiring together training methods and deployment workflows.

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