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 PACKED_WEIGHT_AUDIT.json from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 375 Bytes
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/PACKED_WEIGHT_AUDIT.json
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/PACKED_WEIGHT_AUDIT.json
-
curl -L -o PACKED_WEIGHT_AUDIT.json https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/PACKED_WEIGHT_AUDIT.json
375 Bytes
| { | |
| "source_packed_build_audit": "/shared/yue/jev-nvfp4-20261007/artifacts/all/BUILD_AUDIT.json", | |
| "source_packed_build_audit_sha256": "920a804a1013ab4c0d8113e95b3727233de610804c6955e44a9abdc8fd02b311", | |
| "packed_weight_files_identical": true, | |
| "runtime_changes": "Only W4A16 loading and per-matrix dequantization; weight bytes unchanged", | |
| "no_gradient_training": true | |
| } | |