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unsloth

Optimize LLM parameters and reduce VRAM usage during fine-tuning.

Updated May 13, 2026
Search Tags:#fine-tuning#LLM#memory optimization#training efficiency#model acceleration
One-click install
npx skills add https://github.com/skkeoriw/hermes-brain --skill unsloth-skkeoriw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/skkeoriw/hermes-brain/tree/main/brain/hermes-home/skills/mlops/training/unsloth
Command: npx skills add https://github.com/skkeoriw/hermes-brain --skill unsloth-skkeoriw

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 fine-tuning process for LLMs, significantly reducing VRAM usage and increasing training speed, making it ideal for resource-constrained environments.

Core Features & Use Cases

  • Efficient Fine-Tuning: Achieve 2-5x faster fine-tuning of LLMs like Llama, QLoRA, with reduced VRAM.
  • Support for Multiple Models: Works with a wide range of models, including gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma, Qwen.
  • Use Case: For a machine learning engineer fine-tuning a large LLM like Llama-4 for a specific task like text summarization or code generation, this skill can reduce the time and computational resources required.

Quick Start

Run the unsloth skill with the following command: unsloth fine-tune <model_name> <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 an LLM with less VRAM on resource-constrained hardware?▼

To fine-tune an LLM with less VRAM, you can use memory optimization techniques that accelerate training and reduce the memory footprint, making it ideal for resource-constrained environments.

What is the best way to speed up LLM fine-tuning for models like Llama and Mistral?▼

The best way to speed up LLM fine-tuning for models like Llama and Mistral is to apply optimization techniques that achieve 2-5x faster training while significantly reducing VRAM usage.

Does this fine-tuning acceleration approach support Qwen and DeepSeek models?▼

Yes, this fine-tuning acceleration approach supports a wide range of models including Qwen, DeepSeek, Llama, Gemma, and Mistral for efficient training.

How do I start fine-tuning a model using torch and transformers?▼

To start fine-tuning a model using torch and transformers, run the command `unsloth fine-tune <model_name> <dataset_path>` to initiate the accelerated training process.

Can I use QLoRA for memory optimization during LLM training?▼

Yes, you can use QLoRA for memory optimization during LLM training to significantly reduce VRAM usage and increase training speed in resource-constrained environments.

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