Feature Extraction
Transformers
Safetensors
English
matilda_jev
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/matilda-jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/matilda-jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements-runtime.txt from Maincode/matilda-jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 220 Bytes
-
https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/requirements-runtime.txt
- Command line
-
hf download hf://Maincode/matilda-jev-v1/requirements-runtime.txt
-
curl -L -o requirements-runtime.txt https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/requirements-runtime.txt
220 Bytes
| transformers==5.17.0 | |
| flash-linear-attention==0.5.2 | |
| safetensors==0.8.0 | |
| pillow==12.3.0 | |
| numpy==2.5.3 | |
| fastapi==0.141.1 | |
| uvicorn==0.52.4 | |
| httpx==0.28.1 | |
| # Also install torch==2.14.0 and torchvision==0.29.0 for your accelerator. | |