---
id: unsloth-zoo
version: "2026.8.12"
license: LGPL-3.0-or-later
license_treatment: copyleft
maintenance: active
---

# unsloth-zoo — Utils for Unsloth

License: copyleft · Maintenance: active · Downloads: 1.4M/mo

## What it is and 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.

Users 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.

Use it for:

- 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.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Unsloth Zoo provides utilities for fine-tuning large language models with reduced memory usage and faster training speeds.

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.

## Install

pip install unsloth-zoo
uv add unsloth-zoo
poetry add unsloth-zoo

## Installing unsloth-zoo

Before you install: Low install friction with a pure Python wheel. Active maintenance with recent releases and a large repository (71439 stars). Depends on 27 runtime packages including torch, transformers, and trl, which are standard in the ML ecosystem.

License in practice: Licensed under LGPL-3.0-or-later (copyleft). Derivative works and modifications must be distributed under compatible terms; commercial use is permitted if source code and license are provided.

Quickstart:

pip install unsloth-zoo

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/model",
    load_in_4bit=True,
)

model = FastLanguageModel.get_peft_model(
    model,
    target_modules=["q_proj", "v_proj"],
    bias="none",
    use_gradient_checkpointing="unsloth",
)

Requires PyTorch and CUDA-capable GPU or compatible accelerator; on Windows, PyTorch must be pre-installed before installing unsloth-zoo.

Verify before relying:

- Exact speedup and memory reduction percentages claimed in description for specific model/hardware combinations.
- Whether all listed model families (gpt-oss, Gemma 3n, Qwen3, Llama 4, Mistral) are fully supported in version 2026.8.12.
- Compatibility with non-NVIDIA GPUs beyond the Blackwell mention.
- Specific LoRA configuration parameters and default values supported by this version.

## Package facts

- License: LGPL-3.0-or-later (copyleft)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm fine-tuning optimization, reduce vram gpu training, language model memory efficient, faster model training utils, quantization fine-tune, model optimization, gpu memory reduction training, model-optimization, gpu-memory-efficient, fine-tuning

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