Unsloth

Unsloth is an open-source training accelerator that makes fine-tuning large language models “2-5× faster, 50 % less memory, and 0 % accuracy loss” on the same hardware. The homepage claims it is “the easiest way to fine-tune Llama-3, Mistral, Phi-4 & Gemma” with a single GPU.

Key features extracted verbatim
1. Plug-and-play open-source pip install: `pip install unsloth` instantly replaces Hugging Face TRL & Trainer with drop-in compatibility.
2. Automatic hardware optimization: dynamic CUDA kernel fusion, manual gradient checkpoint removal, and 4-bit/16-bit hybrid quantization that together cut memory use by half and double training speed on NVIDIA, AMD and Intel GPUs.
3. Zero accuracy loss: the site displays a bar chart showing identical eval scores against baseline TRL while using 57 % less VRAM.
4. Ready-made notebooks: one-click Colab, Kaggle and Paperspace notebooks for Llama-3-8B, Gemma-2B, Phi-4, Mistral-7B, and a 1-hour “$5 cloud GPU” tutorial.
5. Extended context support: built-in RoPE scaling and Flash Attention-2 for 16k-100k context lengths without OOM.
6. Safe & reproducible: deterministic training seed, Apache 2.0 license, and on-prem/offline mode for enterprise compliance.
7. Community & enterprise tiers: free GitHub repo, Discord support, and a paid Pro plan that adds multi-GPU, advanced kernels, and priority help-desk.

The landing page ends with a green “Get started” button that links to the GitHub repository and a live speed benchmark that refreshes nightly.