How much faster is training with this skill?

By implementing Unsloth kernels, you can achieve training speeds that are 2-5 times faster than standard Hugging Face implementations.

Does this skill help with memory issues?

Yes, it focuses on memory-efficient training techniques that can reduce VRAM requirements by 50-80% using LoRA and QLoRA.

What models does this Unsloth skill support?

It provides optimized guidance for major architectures including Llama (including 3.1), Mistral, Gemma, and Qwen models.

Can I use this for 4-bit quantization?

Absolutely. The skill includes specific patterns for 4-bit QLoRA training to help you run larger models on smaller GPUs.

Unsloth Fine-Tuning

Name: Unsloth Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Accelerates LLM fine-tuning workflows with Unsloth to achieve up to 5x faster training speeds and 80% reduced memory consumption.

About

This skill empowers Claude to provide expert-level assistance in fine-tuning large language models using the Unsloth framework, specifically targeting popular architectures like Llama, Mistral, Gemma, and Qwen. It offers comprehensive guidance on implementing memory-efficient LoRA and QLoRA techniques, debugging training scripts, and applying performance-enhancing kernels. Whether you are a researcher or a software engineer, this skill streamlines the process of training custom models by providing access to official documentation patterns and best practices for high-speed AI optimization on limited hardware.

Key Features

Automated LoRA and QLoRA optimization patterns
Native support for Llama, Mistral, Gemma, and Qwen architectures
50-80% memory reduction for efficient GPU utilization
384 GitHub stars
Expert troubleshooting for training scripts and VRAM management
2-5x faster training speeds using optimized Unsloth kernels

Use Cases

Optimizing dataset formatting and trainer setup for memory efficiency
Fine-tuning large language models on consumer-grade hardware
Converting standard Transformers training code to high-performance Unsloth code

About

Key Features

Automated LoRA and QLoRA optimization patterns
Native support for Llama, Mistral, Gemma, and Qwen architectures
50-80% memory reduction for efficient GPU utilization
384 GitHub stars
Expert troubleshooting for training scripts and VRAM management
2-5x faster training speeds using optimized Unsloth kernels

Use Cases

Optimizing dataset formatting and trainer setup for memory efficiency
Fine-tuning large language models on consumer-grade hardware
Converting standard Transformers training code to high-performance Unsloth code