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unsloth

Fine-tune large language models with memory-efficient LoRA and QLoRA techniques.

1|Updated Jul 31, 2026
Search Tags:#optimization#llm#machine learning#fine-tuning#lora#qlora
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill unsloth-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/icyzh/hermes-web --skill unsloth-icyzh

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the high computational cost and memory requirements of fine-tuning large language models, enabling efficient training on consumer-grade hardware.

Core Features & Use Cases

  • Memory-Efficient Training: Utilizes LoRA and QLoRA techniques to reduce VRAM usage by up to 70% without sacrificing accuracy.
  • Broad Model Support: Provides optimized workflows for Llama, Mistral, Gemma, Qwen, and DeepSeek models.
  • Use Case: Fine-tune a 7B parameter model on a single GPU for domain-specific tasks like legal document analysis or custom brand voice generation without needing enterprise-grade clusters.

Quick Start

Use the unsloth skill to initiate a QLoRA fine-tuning process for a Llama-3.1-8B model using your provided dataset.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune a Llama or Mistral model with minimal VRAM?▼

You can fine-tune Llama or Mistral models with minimal VRAM by using QLoRA and LoRA techniques. This approach reduces memory consumption by up to 70% without sacrificing accuracy, enabling training on consumer-grade hardware.

Can I use QLoRA to train a 7B parameter model on a single GPU?▼

Yes, you can use QLoRA to train a 7B parameter model on a single GPU. This memory-efficient approach allows you to perform domain-specific tasks like custom brand voice generation without needing enterprise-grade clusters.

What is the best way to reduce VRAM usage during large language model fine-tuning?▼

The best way to reduce VRAM usage during large language model fine-tuning is implementing memory-efficient LoRA techniques. This optimizes the training process to achieve a 70% reduction in memory consumption with zero accuracy loss.

Does memory-efficient fine-tuning support Qwen and DeepSeek architectures?▼

Yes, memory-efficient fine-tuning supports Qwen and DeepSeek architectures. It provides optimized workflows for a wide range of models including Llama, Mistral, and Gemma for both local and cloud-based training environments.

Do I need enterprise-grade hardware for reinforcement learning on large language models?▼

No, you do not need enterprise-grade hardware for reinforcement learning on large language models. By implementing memory-efficient QLoRA techniques, you can optimize the reinforcement learning process using standard consumer-grade GPUs.

Why does fine-tuning large language models require so much memory?▼

Fine-tuning large language models requires significant memory due to the computational cost of updating billions of parameters. Memory-efficient LoRA techniques solve this by optimizing the training process to reduce VRAM usage by up to 70%.

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