Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
legal
decision-model
system-one
classification
citator
case-law
custom_code
Instructions to use jonathankleiman/lev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonathankleiman/lev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="jonathankleiman/lev", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("jonathankleiman/lev", trust_remote_code=True) model = AutoModel.from_pretrained("jonathankleiman/lev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 287 Bytes
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Copyright 2026 Jonathan Kleiman
Lev is a fine-tune of d3-flash. It contains the d3-flash runtime and weights with merged adapters and a tuned readout.
--- Upstream notice ---
d3-flash (Decision 3.0)
Copyright 2026 vLLM Semantic Router Team
Built on Qwen/Qwen3.5-9B (Apache-2.0).
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