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unsloth-core

Accelerate LLM fine-tuning and inference with optimized kernels and gradient checkpointing.

Updated Aug 23, 2026
Search Tags:#optimization#llm#fine-tuning#inference#vram#unsloth
One-click install
npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-core
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth-core
Source: https://github.com/cuba6112/skillfactory/tree/main/skills/unsloth-core
Command: npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-core

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill significantly speeds up the process of fine-tuning Large Language Models (LLMs) and enhances their inference performance, while also reducing VRAM consumption.

Core Features & Use Cases

  • Faster Fine-tuning: Achieve up to 2x faster training with optimized kernels and gradient checkpointing.
  • VRAM Optimization: Reduce memory usage with specialized gradient checkpointing techniques.
  • Native Inference Acceleration: Enable 2x faster inference speeds for deployed models.
  • Use Case: Fine-tune Llama-3 models for a specific task with greater speed and less hardware, then deploy for low-latency responses.

Quick Start

Initialize a model for fine-tuning using the unsloth-core skill with a specified sequence length.

Frequently Asked Questions about unsloth-core

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

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

LLM fine-tuning is accelerated using specialized kernels and optimized gradient checkpointing to reduce VRAM consumption and achieve up to 2x faster training speeds.

Does inference acceleration for Llama-3 and Mistral require explicit code calls?▼

Yes, native inference acceleration for Llama-3 and Mistral requires explicit calls to FastLanguageModel.for_inference(model) to enable 2x faster inference speeds.

How do I initialize a model for fine-tuning with reduced memory?▼

Initialize a model for fine-tuning by specifying a sequence length, which enables optimized gradient checkpointing to significantly reduce memory usage during training.

Can I use torch and transformers with Unsloth for LLM optimization?▼

Yes, Unsloth operates alongside dependencies including torch, transformers, peft, and trl to optimize large language model training and inference performance.

What is the best way to achieve 2x faster native LLM inference?▼

The best way to achieve 2x faster native LLM inference is by applying specialized kernels and explicitly calling FastLanguageModel.for_inference(model) after fine-tuning.

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