Feature Extraction
Transformers
Safetensors
vtx_jev
system-one
jev
decision-model
typed-decisions
4-bit precision
lf4
calibrated-probabilities
noul
choice
score
custom_code
8-bit precision
Instructions to use VTXAI/VTX-JEV-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VTXAI/VTX-JEV-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="VTXAI/VTX-JEV-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VTXAI/VTX-JEV-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
25 days ago
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Publish VTX-JEV-1 LF4 model, architecture, inference, and training code
Verified signatureSelf-contained Hugging Face custom model with AutoConfig/AutoModel remote code, Jev-style inference API, full JEV training and LF4 quantization pipeline, model card, and verified metrics.79abe55