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
Download tokenizer.model from m-a-p/MIO-7B-Base: direct link, hf CLI and curl.
- Browser
- Download file 1.03 MB
-
https://huggingface.co/m-a-p/MIO-7B-Base/resolve/main/tokenizer.model
- Command line
-
hf download hf://m-a-p/MIO-7B-Base/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/m-a-p/MIO-7B-Base/resolve/main/tokenizer.model
1.03 MB
- Xet hash:
- 515fb3e0fe5f36cdd967a91eace081f39f379f869c95afff03a52be0fed5da6a
- Size of remote file:
- 1.03 MB
- SHA256:
- 386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
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