Instructions to use m-a-p/MIO-7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use m-a-p/MIO-7B-Base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("m-a-p/MIO-7B-Base", 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
- Xet hash:
- 15f0f261c80822b8c847a37acfadae7b575d3ffdd1694aa6f6ec6fc95f546bc6
- Size of remote file:
- 4.86 GB
- SHA256:
- 28b36eef34a65a939b0877d49a610a48924142a1643968a3f03f3aedbaee8e25
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