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unsloth-sft

Fine-tune large language models on single GPUs with custom Triton kernels.

1|Updated Jun 4, 2026
Search Tags:#efficiency#LLM fine-tuning#single GPU#parameter-efficient training#LoRA/QLoRA
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill unsloth-sft-hung-phan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth-sft
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-training/unsloth-sft
Command: npx skills add https://github.com/hung-phan/ml-skills --skill unsloth-sft-hung-phan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of slow and VRAM-intensive fine-tuning of LLMs on single GPUs, providing a solution that accelerates the process without compromising accuracy.

Core Features & Use Cases

  • Speed and Efficiency: Achieves 2x speed and 60% less VRAM usage for fine-tuning LLMs.
  • Single GPU Support: Optimized for single GPU setups like T4, L4, RTX 3090/4090, A100.
  • Use Case: Ideal for quick prototyping and iteration on Llama/Qwen/Gemma/Mistral/Phi models on consumer GPUs.

Quick Start

Fine-tune the Llama-3 model on a single GPU using Unsloth SFT with the command: pip install unsloth and follow the provided instructions.

Frequently Asked Questions about unsloth-sft

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

FAQPage Schema
How do I fine-tune LLMs on a single GPU without running out of VRAM?▼

You can fine-tune LLMs on a single GPU by using custom Triton kernels that reduce VRAM usage by 60%. This approach enables parameter-efficient training on consumer GPUs like RTX 3090/4090 without compromising accuracy.

Can I use LoRA or QLoRA for parameter-efficient training on consumer GPUs?▼

Yes, LoRA and QLoRA are supported for parameter-efficient training on consumer GPUs. These methods work with custom Triton kernels to accelerate fine-tuning speed by 2x while significantly reducing memory requirements.

What's the best way to speed up LLM fine-tuning for rapid prototyping?▼

The best way to speed up LLM fine-tuning for rapid prototyping is using optimized kernels that achieve 2x faster training. This allows quick iteration on models like Llama, Qwen, Gemma, Mistral, and Phi directly on single GPU setups.

Does unsloth support fine-tuning Llama and Qwen models on single GPU setups?▼

Yes, it supports fine-tuning Llama and Qwen models, along with Gemma, Mistral, and Phi, on single GPU setups. It is optimized for hardware like T4, L4, RTX 3090/4090, and A100 to maximize training efficiency.

Why does LLM fine-tuning on a single GPU cause high memory usage and slow speeds?▼

Standard LLM fine-tuning on a single GPU causes high memory usage and slow speeds due to inefficient memory management. Using custom Triton kernels solves this by cutting VRAM usage by 60% and doubling processing speed without accuracy loss.

Are there limitations when fine-tuning large language models on consumer GPUs?▼

When fine-tuning large language models on consumer GPUs, standard methods face VRAM and speed limitations. Using parameter-efficient techniques like LoRA and QLoRA with custom kernels overcomes these limits for rapid prototyping.

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