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
Download NOTICE from jonathankleiman/lev: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/jonathankleiman/lev/resolve/main/NOTICE
- Command line
-
hf download hf://jonathankleiman/lev/NOTICE
-
curl -L -o NOTICE https://huggingface.co/jonathankleiman/lev/resolve/main/NOTICE
287 Bytes
| Lev | |
| 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). | |