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unsloth-finetuning

Fine-tune Llama, Mistral, Phi, or Gemma models with LoRA and 4-bit quantization.

1|1|Updated Sep 21, 2025
Search Tags:#LLM#training#LoRA#export#4-bit#finetuning#Unsloth
One-click install
npx skills add https://github.com/ScientiaCapital/unsloth-mcp-server --skill unsloth-finetuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth-finetuning
Source: https://github.com/ScientiaCapital/unsloth-mcp-server/tree/main/.claude/skills/unsloth-finetuning
Command: npx skills add https://github.com/ScientiaCapital/unsloth-mcp-server --skill unsloth-finetuning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fine-tuning large language models efficiently with Unsloth reduces memory usage and training time.

Core Features & Use Cases

  • LoRA/QLoRA setup: Configure LoRA/QLoRA for efficient fine-tuning on custom datasets.
  • Memory optimization: 4-bit quantization and gradient checkpointing to cut memory usage.
  • Export options: Save fine-tuned models to GGUF, Ollama, vLLM, or Hugging Face formats.
  • Workflow guidance: Step-by-step guidance for loading models, preparing data, training, and exporting.
  • Use Case: A team fine-tunes an LLM on proprietary data with minimal hardware resources.

Quick Start

Follow these steps to start a quick fine-tuning run: prepare environment, load base model with 4-bit quantization, apply LoRA, run training with a small dataset, and export the trained model.

Frequently Asked Questions about unsloth-finetuning

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

FAQPage Schema
How do I fine-tune an LLM with reduced memory usage and training time?▼

Fine-tune large language models efficiently using Unsloth with 4-bit quantization and LoRA to cut memory consumption and accelerate training. The framework optimizes gradient checkpointing and model loading for models like Llama, Mistral, Phi, and Gemma on custom datasets.

Can I use LoRA and 4-bit quantization together for LLM fine-tuning?▼

Yes. Unsloth combines LoRA/QLoRA with 4-bit quantization to enable efficient fine-tuning on limited hardware. This pairing reduces both memory footprint and training time while maintaining model quality on proprietary or custom datasets.

What formats can I export a fine-tuned model to?▼

Export fine-tuned models to GGUF, Ollama, vLLM, or Hugging Face formats. Unsloth supports multi-format export so you can deploy your trained model across different inference platforms and tools.

Do I need special hardware to fine-tune LLMs with Unsloth?▼

Unsloth is designed for minimal hardware requirements through memory optimization and 4-bit quantization. You can fine-tune models like Llama and Mistral on standard GPUs with significantly less VRAM than traditional approaches demand.

How do I prepare data and set up the training workflow?▼

Load your base model with 4-bit quantization, apply LoRA configuration to your custom dataset, run training, then export the fine-tuned model. Unsloth provides step-by-step guidance for each stage from environment setup through deployment.

What's the difference between LoRA and QLoRA for fine-tuning?▼

LoRA adapts model weights through low-rank updates, while QLoRA combines this with quantization for even greater efficiency. Unsloth supports both approaches; QLoRA uses less memory but LoRA offers flexibility depending on your hardware and accuracy needs.

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