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unsloth

Fine-tune large language models with LoRA and QLoRA for memory efficiency.

1|1|Updated May 9, 2026
Search Tags:#fine-tuning#AI optimization#large language models#memory efficiency#UnSloth
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill unsloth-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/skills/mlops/training/unsloth
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill unsloth-ldzhhxx

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 streamlines the process of fine-tuning and training large language models, focusing on speed and memory efficiency, allowing users to optimize their AI workflows.

Core Features & Use Cases

  • Fine-Tuning and Training: Accelerates the process of fine-tuning and training LLMs.
  • Memory Efficiency: Utilizes advanced techniques like LoRA and QLoRA to reduce memory usage while maintaining accuracy.
  • Use Case: For example, a user can fine-tune the Llama-4 model on a custom dataset to improve its performance for a specific task, such as generating code or answering questions.

Quick Start

To fine-tune the Llama-4 model with UnSloth, use the following command: unsloth fine-tune Llama-4 model_path dataset_path

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune large language models without running out of memory?▼

You can optimize memory efficiency during large language model fine-tuning by applying techniques like LoRA and QLoRA, which reduce memory usage while maintaining accuracy during training.

What is the best way to accelerate LLM training on a custom dataset?▼

Accelerate LLM training on custom datasets by using automated fine-tuning workflows that optimize speed and memory efficiency, enabling faster customization for specific tasks like code generation or question answering.

Can I fine-tune the Llama-4 model using torch and transformers?▼

Yes, you can fine-tune the Llama-4 model using torch and transformers, as the automated training process integrates these frameworks alongside trl, datasets, and peft to streamline model customization.

Does this LLM fine-tuning process work on macOS and Linux?▼

Yes, the LLM fine-tuning process explicitly supports both Linux and macOS platforms, allowing you to optimize AI training workflows across these operating systems using torch and transformers.

How do I use LoRA and QLoRA to reduce memory usage during LLM training?▼

Reduce memory usage during LLM training by leveraging integrated LoRA and QLoRA techniques, which optimize memory efficiency while maintaining accuracy throughout the fine-tuning process.

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