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unsloth

Accelerate LLM fine-tuning with Unsloth for faster training and reduced memory usage.

539|39|Updated May 1, 2026
Search Tags:#optimization#fine-tuning#LLM#memory efficiency#unsloth
One-click install
npx skills add https://github.com/Tommy-yw/RunbookHermes --skill unsloth-tommy-yw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/Tommy-yw/RunbookHermes --skill unsloth-tommy-yw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the process of fine-tuning Large Language Models (LLMs) with Unsloth, providing significant speed and memory efficiency improvements.

Core Features & Use Cases

  • Fast Fine-Tuning: Achieve 2-5x faster training with up to 50-80% less memory usage.
  • LoRA/QLoRA Optimization: Utilizes Low-Rank Adaptation and Quantized LoRA for efficient fine-tuning.
  • Support for Multiple Models: Compatible with a wide range of LLMs such as gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma, Qwen, and more.
  • Use Case: If you are working on a fine-tuning project with a large LLM and want to accelerate the training process while reducing computational costs, this Skill can be very beneficial.

Quick Start

To start fine-tuning a model with Unsloth, run the following command:

unsloth fine-tune --model <model_name> --data <data_directory>

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I speed up LLM fine-tuning and reduce memory usage?▼

To speed up LLM fine-tuning and reduce memory usage, you can use Unsloth to achieve 2-5x faster training with 50-80% less memory. It works by optimizing Low-Rank Adaptation and Quantized LoRA processes.

Does Unsloth work with models like Llama, DeepSeek, and Mistral?▼

Yes, Unsloth works with a wide range of LLMs including Llama, DeepSeek, Mistral, Qwen, and Gemma. It accelerates the fine-tuning process for these models while optimizing training speed and memory usage.

What Python libraries do I need to optimize LLM training with Unsloth?▼

To optimize LLM training with Unsloth, you need Python libraries including torch, transformers, trl, datasets, and peft. These dependencies support the framework's fast fine-tuning and memory efficiency capabilities.

How do I start fine-tuning a large language model using Unsloth?▼

To start fine-tuning a large language model using Unsloth, run the command `unsloth fine-tune --model <model_name> --data <data_directory>`. This initiates the optimized training process for your specified model and data.

What is the best way to use LoRA and QLoRA for efficient LLM fine-tuning?▼

The best way to use LoRA and QLoRA for efficient LLM fine-tuning is through Unsloth, which utilizes Low-Rank Adaptation and Quantized LoRA. This approach delivers 2-5x faster training and reduces memory usage by 50-80%.

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