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teximal
/
fort-1-2b

Zero-Shot Classification
Transformers
Safetensors
MLX
English
qwen3_5
image-text-to-text
decision-model
calibration
teximal
fort
Model card Files Files and versions
xet
Community

Instructions to use teximal/fort-1-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use teximal/fort-1-2b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-classification", model="teximal/fort-1-2b")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("teximal/fort-1-2b")
    model = AutoModelForMultimodalLM.from_pretrained("teximal/fort-1-2b", device_map="auto")
  • MLX

    How to use teximal/fort-1-2b with MLX:

    # Download the model from the Hub
    pip install huggingface_hub[hf_xet]
    
    hf download teximal/fort-1-2b --local-dir fort-1-2b
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • LM Studio
  • Atomic Chat
fort-1-2b
3.78 GB
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  • 1 contributor
History: 2 commits
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mixpeal
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ed9da44 verified 27 days ago
  • .gitattributes
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  • LICENSE
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  • NOTICE
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  • README.md
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  • SHA256SUMS
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  • chat_template.jinja
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  • config.json
    2.06 kB
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  • model.safetensors
    3.76 GB
    xet
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  • teximal.json
    353 Bytes
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  • tokenizer.json
    20 MB
    xet
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  • tokenizer_config.json
    1.21 kB
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