Instructions to use Hashhasapi/Gemopus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Hashhasapi/Gemopus with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Hashhasapi/Gemopus", dtype=torch.bfloat16, device_map="cuda") prompt = "I like you. I love you" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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datasets:
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- Modotte/CodeX-2M-Thinking
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- Roman1111111/claude-opus-4.6-10000x
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- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
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language:
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- en
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metrics:
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- wiki_split
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- Drunper/metrica_tesi
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new_version: kd13/RoPERT-MLM-mini
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pipeline_tag: text-classification
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library_name: diffusers
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tags:
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- Smart
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