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Lexmount
/
WebJev-35B-A3B

Text Classification
Transformers
Safetensors
English
Chinese
qwen3_5_moe_text
text-generation
decision-model
web-agent
browser-agent
typed-decisions
structured-output
one-pass
mixture-of-experts
Model card Files Files and versions
xet
Community

Instructions to use Lexmount/WebJev-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Lexmount/WebJev-35B-A3B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Lexmount/WebJev-35B-A3B")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Lexmount/WebJev-35B-A3B")
    model = AutoModelForCausalLM.from_pretrained("Lexmount/WebJev-35B-A3B", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
WebJev-35B-A3B / decider
14.9 kB
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  • 1 contributor
History: 1 commit
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sxh-lexmount
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  • __init__.py
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  • prompt.py
    7.65 kB
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  • systemone.py
    7.22 kB
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