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
Commit History
16 days ago
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Technical report, final
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NOTICE for the 9B release
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Model card, figures and technical report for the 9B release
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Lev 9B: frozen weights (rounds 1-6)
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17 days ago
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Second-generation weights
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Model card: wording and one limitation
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Update model card and report
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Update model card and report
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Model card, figures and technical report
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Add excerpts.py
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Add questions.py
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Add NOTICE
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Lev: merged weights, readout and d3 runtime
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18 days ago
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initial commit
Jonathan KleimanVerified signature77a81ad