How do I export my fine-tuned model for local use?

The skill provides automated snippets for saving LoRA adapters, merging weights, and exporting directly to GGUF format for use in Ollama.

Can this skill help create reasoning models?

Yes, it includes specific implementation patterns for formatting and training 'thinking' models like Qwen3-Thinking, complete with internal dialogue tag support.

Why should I use Unsloth for Supervised Fine-Tuning?

Unsloth provides specialized kernels that make SFT up to 2x faster and use significantly less VRAM, allowing you to train larger models on consumer-grade hardware.

What is the importance of the import order in this skill?

Unsloth must be imported before TRL or SFTTrainer to ensure the library's performance-enhancing patches are correctly applied to the training environment.

What should I do if I run out of GPU memory during training?

The skill recommends enabling Unsloth-native gradient checkpointing, reducing per-device batch sizes, and utilizing 4-bit quantization to fit models in memory.

Supervised Fine-Tuning (SFT) with Unsloth

Name: Supervised Fine-Tuning (SFT) with Unsloth
Author: atrawog

byatrawog

0•

Data Science & ML

Accelerates LLM instruction-tuning using Unsloth-optimized SFTTrainer for faster, memory-efficient model adaptation.

This skill provides a comprehensive framework for adapting pre-trained Large Language Models (LLMs) to follow specific instructions through Supervised Fine-Tuning (SFT). By leveraging Unsloth's optimized trainer, it enables 2x faster training speeds with significantly reduced memory footprints, making it ideal for domain-specific fine-tuning on consumer hardware. It includes advanced patterns for dataset preparation, chat template formatting, and the creation of 'thinking' or reasoning models using specialized internal dialogue tags.

Key Features

01Optimized SFTTrainer for 2x faster instruction tuning via Unsloth integration

02Built-in LoRA and QLoRA configuration for high-efficiency memory management

03Integrated reasoning patterns for models like Qwen3-Thinking using custom tags

040 GitHub stars

05Seamless export pathways to GGUF and Ollama for local model deployment

06Support for standard, chat, and reasoning-based 'thinking model' dataset formats

Use Cases

01Developing specialized reasoning models that utilize internal chain-of-thought processing

02Optimizing open-source models for high-performance chat applications with custom templates

03Fine-tuning a base LLM to adopt a specific enterprise brand voice or domain-specific terminology

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add atrawog/overthink-plugins sft

For use in Claude.ai and ChatGPT

Download Skill