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ironbcc
/
Seb-9B-v2

Text Classification
Transformers
Safetensors
qwen3_5
image-text-to-text
decision-model
typed-decisions
classification
routing
calibrated-probabilities
Model card Files Files and versions
xet
Community

Instructions to use ironbcc/Seb-9B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ironbcc/Seb-9B-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="ironbcc/Seb-9B-v2")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("ironbcc/Seb-9B-v2")
    model = AutoModelForMultimodalLM.from_pretrained("ironbcc/Seb-9B-v2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Seb-9B-v2 / serve
47.1 kB
Ctrl+K
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  • 1 contributor
History: 4 commits
ironbcc's picture
ironbcc
add serve/kev_convert.py
8bb25a9 verified 3 days ago
  • kev_convert.py
    4.56 kB
    add serve/kev_convert.py 3 days ago
  • system1_client.py
    22.5 kB
    add serve/system1_client.py 3 days ago
  • system1_server.py
    8.93 kB
    add serve/system1_server.py 3 days ago
  • system1_vllm_backend.py
    11.1 kB
    add serve/system1_vllm_backend.py 3 days ago