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weathon
/
smiles_llava_ft

Image-Text-to-Text
Transformers
TensorBoard
Safetensors
blip
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use weathon/smiles_llava_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use weathon/smiles_llava_ft with Transformers:

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

    How to use weathon/smiles_llava_ft with vLLM:

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

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

    How to use weathon/smiles_llava_ft with Docker Model Runner:

    docker model run hf.co/weathon/smiles_llava_ft
smiles_llava_ft / runs
358 kB
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  • 1 contributor
History: 1 commit
weathon's picture
weathon
End of training
db5a1b2 verified over 1 year ago
  • Feb03_08-00-24_1994991c6162
    End of training over 1 year ago
  • Feb03_08-06-51_1994991c6162
    End of training over 1 year ago
  • Feb03_08-16-22_1994991c6162
    End of training over 1 year ago