{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Unsloth Zoo provides utilities for fine-tuning large language models with reduced memory usage and faster training speeds.","skillfed_tags":["model-optimization","gpu-memory-efficient","fine-tuning"],"use_cases":["Fine-tune language models on consumer GPUs using quantization and memory-efficient kernels.","Train reasoning models with reduced VRAM by combining gradient checkpointing and optimized operations.","Adapt vision models for custom image-text tasks with reduced memory footprint.","Export fine-tuned models to GGUF or Ollama format for local deployment after training.","Implement text-to-speech model fine-tuning using memory-efficient infrastructure.","Perform long-context training on standard GPUs by combining optimization techniques."],"what_it_does":"Unsloth Zoo is a utility package that provides optimized training recipes for large language models, integrating with PyTorch, transformers, and peft to enable memory-efficient fine-tuning. It applies techniques like quantization and gradient checkpointing to reduce GPU memory requirements and accelerate training. The package supports various model architectures and works with both consumer and enterprise GPUs.\n\nUsers typically load a pretrained model, apply optimization techniques, and train on custom datasets using the trl library for instruction tuning or reasoning tasks. The package includes support for distributed training through accelerate and can export fine-tuned models to various formats. It depends on 27 runtime packages including torch, transformers, trl, peft, and accelerate.","worth_installing":"Yes, if you are fine-tuning large language models and want to reduce GPU memory usage and training time. The package is actively maintained with no known vulnerabilities and integrates with the standard Hugging Face ecosystem. The LGPL-3.0-or-later license is permissive for research and commercial use provided you distribute source code. Install friction is low. Verify that the specific models and hardware you plan to use are supported before committing to a large training run."},"id":"unsloth-zoo","links":{"html":"https://skillfed.io/packages/unsloth-zoo","md":"https://skillfed.io/packages/unsloth-zoo.md","pypi":"https://pypi.org/project/unsloth-zoo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"LGPL-3.0-or-later","license_treatment":"copyleft","name":"unsloth-zoo","python_support":"supports_current","summary":"Utils for Unsloth"},"popularity":{"monthly_downloads":1352315,"position":4012,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2026.8.12"}