unsloth
2-5X faster training, reinforcement learning & finetuning
Decision gist · record as of 2026-08-14
Yes, if you are training or fine-tuning models and have a compatible GPU (NVIDIA, AMD, Intel, or macOS). The permissive license, active maintenance, low install friction, and zero known vulnerabilities make it a sound choice. The 30-package dependency stack is heavy but standard for ML work. Not necessary if you only run inference or work exclusively with cloud-based training.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a compatible GPU (NVIDIA CUDA, AMD ROCm, Intel, or macOS Metal) or CPU; Python 3.9–3.14; torch and transformers pre-installed or installed as dependencies.
- Low friction installation via wheel; active maintenance with recent releases.
- Depends on 30 runtime packages including torch, transformers, and bitsandbytes—a heavy stack typical of ML frameworks.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 71,432 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,286,994 downloads/mo, #3,161 on PyPI
Alternatives
Verify before relying
pip install unsloth
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/model",
load_in_4bit=True,
)
# Fine-tune with your dataset
trainer.train()- Whether the 2× faster training and 70% VRAM reduction claims apply to all model types or specific configurations only
- Compatibility matrix across NVIDIA, AMD, Intel, and macOS GPU backends in practice
- Whether the desktop app and PyPI package share the same core training engine or differ significantly
What it is and what it does
Unsloth is a PyTorch-based framework for accelerating the training and fine-tuning of large language models, diffusion models, and embeddings on consumer-grade hardware. It combines quantization techniques, memory optimization, and hardware-specific kernels to reduce VRAM requirements and training time. The package integrates with the Hugging Face ecosystem—transformers, datasets, peft, trl, accelerate—and supports reinforcement learning workflows.
The project offers three interfaces: a native desktop app, a web UI called Unsloth Studio, and the PyPI package for programmatic use. The PyPI install targets developers who want to integrate optimized training into Python scripts or notebooks; it requires Python 3.9–3.14 and a compatible GPU or CPU backend. No known security vulnerabilities are recorded.
Use it for
- Fine-tune open-source LLMs on your own hardware without renting cloud GPUs
- Train diffusion models for image or video generation with reduced VRAM footprint
- Build reinforcement learning pipelines with lower memory overhead
- Integrate optimized training into data science workflows via transformers and peft
- Deploy locally trained models via OpenAI-compatible API or export to GGUF for inference
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are training or fine-tuning models and have a compatible GPU (NVIDIA, AMD, Intel, or macOS).
The permissive license, active maintenance, low install friction, and zero known vulnerabilities make it a sound choice. The 30-package dependency stack is heavy but standard for ML work. Not necessary if you only run inference or work exclusively with cloud-based training.
Install
unsloth on PyPI
Before you install
Low friction installation via wheel; active maintenance with recent releases. Depends on 30 runtime packages including torch, transformers, and bitsandbytes—a heavy stack typical of ML frameworks. Installation succeeds easily, but the dependency footprint is substantial.
Requires a compatible GPU (NVIDIA CUDA, AMD ROCm, Intel, or macOS Metal) or CPU; Python 3.9–3.14; torch and transformers pre-installed or installed as dependencies.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.
Quickstart
pip install unsloth
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/model",
load_in_4bit=True,
)
# Fine-tune with your dataset
trainer.train()Verify before relying
- Whether the 2× faster training and 70% VRAM reduction claims apply to all model types or specific configurations only
- Compatibility matrix across NVIDIA, AMD, Intel, and macOS GPU backends in practice
- Whether the desktop app and PyPI package share the same core training engine or differ significantly
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 30 packagesunsloth_zoowheelpackagingtorchtorchvisionnumpytqdmpsutiltyroprotobufxformersbitsandbytestritontriton-windowssentencepiecedatasetsacceleratepefthuggingface_hubhf_transferdiffuserstransformerstrltyperpydanticpyyamlnest-asynciostructlogclickrich |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,286,994 / month, #3,161 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPUEnvironment :: GPU :: NVIDIA CUDAProgramming Language :: PythonTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: unsloth-2026.8.18-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “llm fine-tuning acceleration”
- unslothUnsloth accelerates training and fine-tuning of large language models…
- ms-swiftms-swift is a framework for training, evaluating, quantizing, and…
- google-tunixTunix is a JAX-based library for post-training large language models…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also unsloth-zoo · ms-swift · torch · ipex-llm · transformer-engine · litdata · transformer-engine-cu12 · peft · torch-directml · transformer-engine-cu13