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Aniket-a14
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hugging-face-model-trainer

Train language models with TRL on Hugging Face Jobs.

1|Updated Feb 13, 2025
Search Tags:#monitoring#HuggingFace#TRL#GGUF#train#SFT#DPO
One-click install
npx skills add https://github.com/Aniket-a14/Wizard-w1 --skill hugging-face-model-trainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/Aniket-a14/Wizard-w1/tree/main/.github/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/Aniket-a14/Wizard-w1 --skill hugging-face-model-trainer

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 provides end-to-end tooling and templates to train and fine-tune language models on Hugging Face Jobs using TRL (SFT, DPO, GRPO), including integration with Trackio, hub pushing, and GGUF conversion for local deployment.

Core Features & Use Cases

  • Production-ready training templates for SFT, DPO, and GRPO with inline scripts and dataset validation.
  • GGUF conversion workflow for deploying trained models locally with Ollama and llama.cpp.
  • Hub authentication and monitoring guidance, Trackio integration, and cost estimation workflows.
  • Use case: Quickly fine-tune a small Qwen/Qwen2.5-0.5B model on TRL-ready data and push to Hugging Face Hub.

Quick Start

Use the hugging-face-model-trainer skill to launch a TRL training job on HF Jobs with a small dataset and inline script.

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 LLM using TRL on Hugging Face Jobs?▼

You can fine-tune a LLM using TRL on Hugging Face Jobs by utilizing production-ready SFT, DPO, and GRPO training templates with inline scripts and dataset validation to execute cloud GPU training workflows.

Can I convert my trained Hugging Face model to GGUF for local deployment?▼

Yes, the skill includes a GGUF conversion workflow for deploying trained models locally, enabling you to use your fine-tuned models with Ollama and llama.cpp.

What is the best way to monitor TRL training runs and estimate GPU costs?▼

Monitor TRL training runs and estimate GPU costs using integrated Trackio monitoring and cost estimation workflows designed to track metrics and resource usage throughout cloud GPU training.

Do I need PEFT and accelerate to run SFT and DPO training scripts?▼

Yes, running SFT and DPO training scripts requires PEFT and accelerate alongside TRL, transformers, and torch to provide the necessary environment for efficient cloud GPU training workflows.

How do I push a fine-tuned Qwen model to the Hugging Face Hub?▼

You can push a fine-tuned Qwen model to the Hugging Face Hub by following the integrated Hub authentication and pushing guidance to upload your trained model once the TRL training job completes.

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