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Daniel Han 2023-12-01 17:50:40 +11:00 • committed by GitHub
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@ -8,9 +8,9 @@
* Manual autograd engine - hand derived backprop steps.
* QLoRA / LoRA 80% faster, 50% less memory.
* All kernels written in OpenAI's Triton language.
* 0% loss in accuracy.
* No change of hardware necessary. Supports Tesla T4, RTX 20, 30, 40 series, A100, H100s
* Flash Attention support
* 0% loss in accuracy - no approximation methods - all exact.
* No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. Tesla T4, RTX 20, 30, 40 series, A100, H100s
* Flash Attention support via Xformers.
* Supports 4bit and 16bit LoRA finetuning.
* Train Slim Orca **fully locally in 260 hours from 1301 hours (5x faster).**
* Check out [Unsloth Pro and Max](https://unsloth.ai/) codepaths for **30x faster training**!
@ -131,3 +131,5 @@ trainer = .... Use Huggingface's Trainer and dataset loading
2. Windows is not supported as of yet - we rely on Xformers and Triton support, so until both packages support Windows officially, Unsloth will then support Windows.
<img src="./images/unsloth loading page render.png" width="300" />
3. If it doesn't install - maybe try updating `pip`.