skills.rest
frankxai
frankxaiCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

HuggingFace Model Trainer

Fine-tune large language models with HuggingFace TRL, Transformers, and PEFT.

2|1|Updated Sep 1, 2025
Search Tags:#fine-tuning#lora#huggingface#dpo#trl#sft#grpo
One-click install
npx skills add https://github.com/frankxai/ai-architect-academy --skill huggingface-model-trainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: HuggingFace Model Trainer
Source: https://github.com/frankxai/ai-architect-academy/tree/main/claude-ai-architect/skills/huggingface-trainer
Command: npx skills add https://github.com/frankxai/ai-architect-academy --skill huggingface-model-trainer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines the end-to-end process of training and fine-tuning large language models using HuggingFace's TRL, Transformers, and PEFT libraries, reducing setup time, complexity, and debugging effort.

Core Features & Use Cases

  • Supports Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and PEFT-based methods like LoRA for parameter-efficient training.
  • Provides training configuration templates, dataset preparation guidance, and evaluation tips for model alignment, instruction tuning, and domain adaptation.
  • Use cases include adapting models to a specific domain, aligning model outputs to human preferences, and deploying fine-tuned models for downstream tasks.

Quick Start

Start with a base model id and a dataset; choose an SFT workflow for instruction tuning or a RLHF-style workflow (DPO/GRPO) for preference alignment, then run a training script and export the final model.

Frequently Asked Questions about HuggingFace Model Trainer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a HuggingFace model for instruction tuning?▼

Fine-tuning a HuggingFace model for instruction tuning involves using the Supervised Fine-Tuning (SFT) workflow from TRL. You start by preparing your dataset and configuring the training parameters to adapt the base model to your specific instructions.

What's the difference between SFT and DPO for large language model alignment?▼

SFT (Supervised Fine-Tuning) adapts models using instruction datasets, while DPO (Direct Preference Optimization) aligns model outputs to human preferences. This skill supports both workflows, allowing you to choose based on whether you need instruction following or preference alignment.

Can I use LoRA for parameter-efficient training with limited GPU resources?▼

Yes, LoRA (Low-Rank Adaptation) through PEFT enables parameter-efficient training, significantly reducing GPU memory requirements. This skill provides training configuration templates that incorporate LoRA to streamline the fine-tuning process on constrained hardware.

How do I prepare datasets for GRPO and preference alignment workflows?▼

Preparing datasets for GRPO involves formatting data to support Group Relative Policy Optimization for preference alignment. This skill documents the dataset preparation steps required to structure your data correctly before running the RLHF-style training scripts.

Does this skill support exporting fine-tuned models for inference frameworks?▼

Yes, the skill documents model export and conversion options for inference frameworks. After completing the fine-tuning process, it guides you through exporting the final model to ensure compatibility with your chosen deployment targets.

What's the best way to adapt a large language model to a specific domain?▼

The best way to adapt a large language model to a specific domain is through supervised fine-tuning with domain-specific datasets. This skill provides end-to-end guidance on dataset preparation, training configuration, and evaluation for domain adaptation tasks.

Related Skills

Explore More Software Engineering Skills→
xiaoquqixiaoquqi

fine-tuning-with-trl

Implement RLHF fine-tuning workflows with TRL for instruction and preference alignment.

Community
Advanced
Marwan78888Marwan78888
16

fine-tuning-expert

Configure LoRA/QLoRA fine-tuning workflows with JSONL datasets and hyperparameters.

Community
Advanced
VKirillVKirill
15

transformers

Load and run HuggingFace Transformers models for inference and fine-tuning.

Community
Advanced
webdevtodayjasonwebdevtodayjason

fine-tuning-with-trl

Fine-tune language models with TRL for RLHF and preference alignment.

Community
Advanced