Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/Matilda-Jev-v1-FP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1-FP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/Matilda-Jev-v1-FP4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/Matilda-Jev-v1-FP4") model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1-FP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 375 Bytes
c69aaec | 1 2 3 4 5 6 7 8 | {
"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
}
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