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Sagnik2003
/
Bridge-VQA

Text Generation
PEFT
PyTorch
Safetensors
Transformers
bridgevqa
feature-extraction
lora
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Sagnik2003/Bridge-VQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use Sagnik2003/Bridge-VQA with PEFT:

    Task type is invalid.
  • Transformers

    How to use Sagnik2003/Bridge-VQA with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Sagnik2003/Bridge-VQA", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Sagnik2003/Bridge-VQA", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Sagnik2003/Bridge-VQA with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Sagnik2003/Bridge-VQA"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Sagnik2003/Bridge-VQA",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Sagnik2003/Bridge-VQA
  • SGLang

    How to use Sagnik2003/Bridge-VQA with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "Sagnik2003/Bridge-VQA" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Sagnik2003/Bridge-VQA",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "Sagnik2003/Bridge-VQA" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Sagnik2003/Bridge-VQA",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Sagnik2003/Bridge-VQA with Docker Model Runner:

    docker model run hf.co/Sagnik2003/Bridge-VQA
Bridge-VQA
406 MB
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  • 1 contributor
History: 14 commits
Sagnik2003's picture
Sagnik2003
Fix missing import os
e4dad37 verified 3 months ago
  • phi2-lora-adapter
    Upload folder using huggingface_hub 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    5.54 kB
    Upload folder using huggingface_hub 3 months ago
  • config.json
    373 Bytes
    Upload folder using huggingface_hub 3 months ago
  • configuration_bridgevqa.py
    569 Bytes
    Upload folder using huggingface_hub 3 months ago
  • modeling_bridgevqa.py
    7.3 kB
    Fix missing import os 3 months ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    28.4 MB
    xet
    Upload folder using huggingface_hub 3 months ago