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ninadkale98
/
dolphin-hf

Image-Text-to-Text
Transformers
Safetensors
Chinese
English
vision-encoder-decoder
document-parsing
document-understanding
document-intelligence
ocr
layout-analysis
table-extraction
multimodal
vision-language-model
Model card Files Files and versions
xet
Community

Instructions to use ninadkale98/dolphin-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ninadkale98/dolphin-hf with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="ninadkale98/dolphin-hf")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForImageTextToText
    
    tokenizer = AutoTokenizer.from_pretrained("ninadkale98/dolphin-hf")
    model = AutoModelForImageTextToText.from_pretrained("ninadkale98/dolphin-hf")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use ninadkale98/dolphin-hf with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "ninadkale98/dolphin-hf"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "ninadkale98/dolphin-hf",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/ninadkale98/dolphin-hf
  • SGLang

    How to use ninadkale98/dolphin-hf 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 "ninadkale98/dolphin-hf" \
        --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": "ninadkale98/dolphin-hf",
    		"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 "ninadkale98/dolphin-hf" \
            --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": "ninadkale98/dolphin-hf",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ninadkale98/dolphin-hf with Docker Model Runner:

    docker model run hf.co/ninadkale98/dolphin-hf
dolphin-hf
808 MB
Ctrl+K
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  • 2 contributors
History: 2 commits
Ninad Kale
Add dolphin model
d129912 5 months ago
  • .gitattributes
    1.52 kB
    initial commit 5 months ago
  • README.md
    3.51 kB
    Add dolphin model 5 months ago
  • config.json
    4.85 kB
    Add dolphin model 5 months ago
  • generation_config.json
    160 Bytes
    Add dolphin model 5 months ago
  • model.safetensors
    796 MB
    xet
    Add dolphin model 5 months ago
  • preprocessor_config.json
    477 Bytes
    Add dolphin model 5 months ago
  • special_tokens_map.json
    277 Bytes
    Add dolphin model 5 months ago
  • tokenizer.json
    7.86 MB
    Add dolphin model 5 months ago
  • tokenizer_config.json
    4.05 MB
    Add dolphin model 5 months ago