unsloth-finetuning

What problem does it solve?

Fine-tuning large language models efficiently with Unsloth reduces memory usage and training time.

Core Features & Use Cases

• LoRA/QLoRA setup: Configure LoRA/QLoRA for efficient fine-tuning on custom datasets.
• Memory optimization: 4-bit quantization and gradient checkpointing to cut memory usage.
• Export options: Save fine-tuned models to GGUF, Ollama, vLLM, or Hugging Face formats.
• Workflow guidance: Step-by-step guidance for loading models, preparing data, training, and exporting.
• Use Case: A team fine-tunes an LLM on proprietary data with minimal hardware resources.

Quick Start

Follow these steps to start a quick fine-tuning run: prepare environment, load base model with 4-bit quantization, apply LoRA, run training with a small dataset, and export the trained model.