Instructions to use nix3s/CHSA_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nix3s/CHSA_Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen3-1.7B-Base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nix3s/CHSA_Model") prompt = "{ \"patient_id\": \"string\", \"symptoms\": \"Patient de 45 ans, douleur thoracique irradiant vers le bras gauche, sueurs froides\", \"age\": 120, \"antecedents\": \"string\", \"constantes\": { \"fc\": 110, \"spo2\": 94, \"ta\": \"145/90\", \"temperature\": 37.2 }, \"langue\": \"fr\" }'" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/Capture d'écran 2026-03-12 111651.png | |
| text: >- | |
| { "patient_id": "string", "symptoms": "Patient de 45 ans, douleur | |
| thoracique irradiant vers le bras gauche, sueurs froides", "age": 120, | |
| "antecedents": "string", "constantes": { "fc": 110, "spo2": | |
| 94, "ta": "145/90", "temperature": 37.2 }, "langue": "fr" }' | |
| base_model: Qwen/Qwen3-1.7B-Base | |
| instance_prompt: null | |
| license: apache-2.0 | |
| # CHSA_Model | |
| <Gallery /> | |
| ## Download model | |
| [Download](/nix3s/CHSA_Model/tree/main) them in the Files & versions tab. | |