Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/matilda-jev-fp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-fp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-fp4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/matilda-jev-fp4") model = AutoModel.from_pretrained("Maincode/matilda-jev-fp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download kev/__init__.py from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/__init__.py
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/kev/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/__init__.py
267 Bytes
| """Kev: one-pass decision model training.""" | |
| import os | |
| import sys | |
| if os.environ.get("KEV_DISABLE_FLA") == "1": | |
| # Blocking the import makes transformers fall back to its torch reference gated-delta path. | |
| sys.modules["fla"] = None # type: ignore[assignment] | |