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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 / assets
164 kB
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  • 1 contributor
History: 5 commits
sxh-lexmount's picture
sxh-lexmount
Model card: evaluation link, simplified live-web figure and table
844d4f7 verified 5 days ago
  • liveweb.png
    97.5 kB
    xet
    Model card: evaluation link, simplified live-web figure and table 5 days ago
  • overview.png
    66.9 kB
    Model card: eight benchmarks, links to code, dataset and training recipe 5 days ago