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
metadata
license: apache-2.0
datasets:
- Modotte/CodeX-2M-Thinking
- Roman1111111/claude-opus-4.6-10000x
- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
language:
- en
metrics:
- wiki_split
- Drunper/metrica_tesi
new_version: kd13/RoPERT-MLM-mini
pipeline_tag: text-classification
library_name: diffusers
tags:
- Smart