Instructions to use rootonchair/MiniMax-H3-nunchaku-lite-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rootonchair/MiniMax-H3-nunchaku-lite-int4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rootonchair/MiniMax-H3-nunchaku-lite-int4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
correct example?
#1
by vladmandic - opened
example in readme downloads weights, instantiates transformer model from original config, patches transformer - but never actually uses downloaded weights?
Yeah, this repo originally started as a testbed for data-free quantization, but I later overrode it with a calibrated approach, so things are a bit mixed up at the moment. I’ve updated the README to clarify both approaches. Just note that these are my attempts to get Nunchaku working with MiniMax-H3, so the quality may not be optimal yet.
understood - thanks.
btw, why not prune the transformer weights (e.g. https://huggingface.co/multimodalart/MiniMax-H3-Pruned)?
It's a good recommendation. I will give it a try