unsloth-training

Name: unsloth-training
Availability: InStock
Author: duyet

Fine-tune LLMs with Unsloth using GRPO and SFT methods.

Updated Mar 13, 2026

Search Tags:#optimization #llm #fine-tuning #fp8 #sft #grpo #unsloth

Ready to run

npx skills add https://github.com/duyet/skills --skill unsloth-training-duyet

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: unsloth-training
Source: https://github.com/duyet/skills/tree/main/unsloth-training
Command: npx skills add https://github.com/duyet/skills --skill unsloth-training-duyet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of fine-tuning Large Language Models (LLMs), making advanced techniques like GRPO and SFT more accessible and efficient by reducing computational resource requirements.

Core Features & Use Cases

Efficient Fine-Tuning: Achieve faster training times and significantly lower VRAM usage compared to standard methods.
Multiple Training Methods: Supports GRPO (Reinforcement Learning from Human Feedback) and SFT (Supervised Fine-Tuning).
Advanced Optimizations: Leverages FP8 training, 3x packing for mixed-length data, and supports deployment via Docker, mobile (ExecuTorch), and various export formats (GGUF, Ollama, vLLM).
Use Case: Fine-tune a large language model for a specific task (e.g., customer support, code generation) on consumer-grade hardware or with reduced cloud costs.

Quick Start

Use the unsloth-training skill to fine-tune an LLM using GRPO with FP8 precision.

Frequently Asked Questions about unsloth-training

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

FAQPage Schema

How do I fine-tune an LLM with less VRAM on consumer-grade hardware?▼

You can fine-tune large language models with 50% less VRAM by leveraging FP8 training and 3x packing for mixed-length data. This approach enables efficient SFT and GRPO workflows directly on consumer-grade hardware.

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

SFT, or Supervised Fine-Tuning, trains models on labeled datasets, while GRPO applies reinforcement learning from human feedback. Both methods are supported to streamline efficient fine-tuning for specific tasks.

Can I export a fine-tuned LLM to run on mobile and serverless environments?▼

Yes, you can deploy fine-tuned models across mobile and serverless environments. Supported export formats include Docker, ExecuTorch for mobile, GGUF, Ollama, and vLLM for versatile deployment.

How does FP8 training reduce VRAM usage for large language models?▼

FP8 training reduces VRAM usage by utilizing lower precision computations during the fine-tuning process. Combined with data packing techniques, it achieves 2x faster training times and 50% less memory consumption.

What's the best way to speed up SFT for a custom customer support LLM?▼

The best way to speed up SFT is applying 3x packing for mixed-length data alongside FP8 precision. This optimization reduces computational resource requirements while doubling training speed.

Does Unsloth support deploying models via Docker and Ollama?▼

Yes, deployment via Docker and Ollama is fully supported. After fine-tuning, you can export models into various formats including GGUF and vLLM to integrate seamlessly into existing infrastructure.