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enghamdiali
/
idfc-m1

Visual Question Answering
Transformers
Safetensors
idefics
image-text-to-text
text-generation-inference
4-bit precision
bitsandbytes
Model card Files Files and versions
xet
Community

Instructions to use enghamdiali/idfc-m1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use enghamdiali/idfc-m1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("visual-question-answering", model="enghamdiali/idfc-m1")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("enghamdiali/idfc-m1")
    model = AutoModelForMultimodalLM.from_pretrained("enghamdiali/idfc-m1", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
idfc-m1
5 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
enghamdiali's picture
enghamdiali
Update README.md
ca8359b verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    5.21 kB
    Update README.md about 2 years ago
  • added_tokens.json
    61 Bytes
    Upload tokenizer about 2 years ago
  • config.json
    1.73 kB
    Upload IdeficsForVisionText2Text about 2 years ago
  • generation_config.json
    132 Bytes
    Upload IdeficsForVisionText2Text about 2 years ago
  • model.safetensors
    5 GB
    xet
    Upload IdeficsForVisionText2Text about 2 years ago
  • special_tokens_map.json
    637 Bytes
    Upload tokenizer about 2 years ago
  • tokenizer.json
    1.84 MB
    Upload tokenizer about 2 years ago
  • tokenizer.model
    500 kB
    xet
    Upload tokenizer about 2 years ago
  • tokenizer_config.json
    1.44 kB
    Upload tokenizer about 2 years ago