Unsloth Zoo provides a collection of plug-and-play utilities and example modules designed to work with the Unsloth library for efficient LLM fine-tuning. It includes components for dataset loading, SFT (Supervised Fine-Tuning) trainers, tokenizer utilities, and metrics. As of version 2026.4.6, it aims to simplify and standardize common LLM fine-tuning workflows built on Unsloth, with frequent updates aligning with Unsloth's development.
pip install unsloth-zooVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to load a dataset using Unsloth Zoo's data loading utilities. It configures a dataset using a `DatasetConfig` and then loads it with `load_dataset`, showcasing a common first step in preparing data for fine-tuning.
Always install `unsloth-zoo` and `unsloth` together, preferably ensuring they are compatible. Refer to the `unsloth-zoo` README or `unsloth` documentation for recommended version pairings. Frequently update both libraries to their latest compatible versions.
Review the source code or documentation for the specific `DatasetConfig` template you are using to understand the expected format. Validate your custom dataset's structure before loading it with `unsloth_zoo.data.loader`.
Ensure your system meets `unsloth`'s GPU requirements. Verify `bitsandbytes` is installed for quantization. Follow `unsloth`'s official installation instructions for your specific CUDA/PyTorch setup before attempting to use `unsloth-zoo`.