Amazon SageMaker JumpStart
ML hub with curated foundation models, pretrained algorithms, and solution templates you can deploy and fine-tune in SageMaker
Unsloth
Accelerate LLM fine‑tuning with up to 2× speed and 70% less VRAM
Train state‑of‑the‑art LLMs, vision and TTS models faster using Unsloth’s optimized kernels, free notebooks, and Docker image, while cutting memory needs dramatically.
Overview
Overview
Unsloth provides a collection of optimized training kernels, flexible quantization, and ready‑to‑run notebooks that let you fine‑tune large language, vision, and text‑to‑speech models on modest GPUs. By reducing VRAM consumption up to 80% and delivering 1.5‑2.2× speed improvements, it makes state‑of‑the‑art models accessible to researchers, developers, and educators.
Getting Started
Install with a single pip install unsloth on Linux/WSL or via the official unsloth/unsloth Docker image for a reproducible environment. Windows users follow the Windows Guide after installing PyTorch. The catalog offers free notebooks for models such as GPT‑OSS, Qwen3, Gemma, Llama 3.1, Mistral, and Orpheus‑TTS, with one‑click export to GGUF, Ollama, vLLM, or Hugging Face.
Capabilities
Features include Flex Attention, Dynamic 4‑bit quantization, GRPO/GSPO reasoning kernels, and support for ultra‑long context windows (up to 342K tokens). Multi‑GPU scaling is forthcoming, and the library integrates with Blackwell, DGX Spark, and other high‑end GPU platforms.
Highlights
Flex Attention delivers up to 2× faster training
Dynamic 4‑bit quantization retains near‑full precision accuracy
Supports LLM, vision, TTS, and audio models in a single toolkit
Free end‑to‑end notebooks and official Docker image for zero‑setup
Pros
- Significant speedup (1.5‑2.2×) over standard pipelines
- Memory reduction up to 80%, enabling larger models on modest GPUs
- Broad model coverage (Llama, Gemma, Qwen, Mistral, etc.)
- Simple installation via pip or Docker
Considerations
- Windows setup requires pre‑installed PyTorch and compatible CUDA
- Advanced kernels (GRPO, GSPO) need GPUs with ≥14 GB VRAM
- Primary focus is training; inference acceleration is not guaranteed
- Community support is informal (Discord/Reddit) without enterprise SLA
Managed products teams compare with
When teams consider Unsloth, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Researchers needing rapid prototyping on limited GPU resources
- Developers wanting turnkey notebooks for fine‑tuning
- Teams deploying custom LLM, vision, or TTS models in production
- Educators teaching model fine‑tuning without expensive hardware
Not ideal when
- Production environments requiring certified enterprise support
- Users limited to CPU‑only machines
- Projects needing exhaustive hyperparameter sweeps beyond provided notebooks
- Organizations with strict compliance needing closed‑source tooling
How teams use it
Domain‑specific chatbot
Fine‑tune GPT‑OSS 20B on a 14 GB GPU to match baseline quality with half the training time
Image‑text retrieval model
Adapt Qwen‑VL 8B using GRPO, training on 5 GB VRAM and achieving state‑of‑the‑art retrieval scores
Custom voice generation
Create a bespoke TTS voice with Orpheus‑TTS 3B and export to GGUF for edge deployment
Long‑context reasoning
Extend Llama 3.1 8B to 342K token context on an 80 GB GPU for complex reasoning tasks
Tech snapshot
Python100%
Shell1%
Frequently asked questions
How do I install Unsloth on Windows?
Install PyTorch first, then run `pip install unsloth`; see the Windows Guide for CUDA compatibility details.
Which GPUs are supported for the memory‑efficient kernels?
Any NVIDIA GPU supported by PyTorch; RTX 50x, B200, 6000, and DGX Spark are explicitly mentioned.
Can I use Unsloth for inference acceleration?
Unsloth focuses on training speed and memory; inference can benefit from the same quantizations but separate inference tools are recommended.
Where can I find the free fine‑tuning notebooks?
All notebooks are linked in the repository README and can be launched directly from the Unsloth catalog page.
Is there a Docker image for reproducible environments?
Yes, the official `unsloth/unsloth` image provides a ready‑to‑run environment; see the Docker Guide.
Project at a glance
Active- Stars
- 50,954
- Watchers
- 50,954
- Forks
- 4,202
LicenseApache-2.0
Repo age2 years old
Last commityesterday
Primary languagePython
Last synced 12 hours ago