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
Second-generation weights
Browse files- NOTICE +0 -7
- decision_config.json +1 -1
- model-00002-of-00002.safetensors +1 -1
- readout.safetensors +1 -1
NOTICE
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Lev
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Copyright 2026 Jonathan Kleiman
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Lev is a fine-tune of d3-mini. It contains the d3-mini runtime and weights with merged adapters and a tuned readout.
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--- Upstream notice ---
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d3-mini (Decision 3.0)
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Copyright 2026 vLLM Semantic Router Team
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d3-mini (Decision 3.0)
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Copyright 2026 vLLM Semantic Router Team
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decision_config.json
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12185,
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"temperature": 1.
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"attention_mode": "noncausal_full_attention",
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"pooling": "last",
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"max_length": null,
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12185,
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"temperature": 1.25,
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"attention_mode": "noncausal_full_attention",
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"pooling": "last",
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"max_length": null,
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model-00002-of-00002.safetensors
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readout.safetensors
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size 2611280
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