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
| 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 | |