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lbasile
/
llava-2000

Image-Text-to-Text
Transformers
Safetensors
llava
Model card Files Files and versions
xet
Community

Instructions to use lbasile/llava-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lbasile/llava-2000 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="lbasile/llava-2000")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("lbasile/llava-2000")
    model = AutoModelForImageTextToText.from_pretrained("lbasile/llava-2000")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use lbasile/llava-2000 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "lbasile/llava-2000"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lbasile/llava-2000",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/lbasile/llava-2000
  • SGLang

    How to use lbasile/llava-2000 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 "lbasile/llava-2000" \
        --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": "lbasile/llava-2000",
    		"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 "lbasile/llava-2000" \
            --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": "lbasile/llava-2000",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use lbasile/llava-2000 with Docker Model Runner:

    docker model run hf.co/lbasile/llava-2000
llava-2000
27.6 GB
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  • 1 contributor
History: 3 commits
lbasile's picture
lbasile
Upload processor
2bdf914 verified 6 months ago
  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    5.17 kB
    Upload LlavaForConditionalGeneration 6 months ago
  • added_tokens.json
    41 Bytes
    Upload processor 6 months ago
  • config.json
    964 Bytes
    Upload LlavaForConditionalGeneration 6 months ago
  • generation_config.json
    136 Bytes
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  • model-00001-of-00006.safetensors
    4.9 GB
    xet
    Upload LlavaForConditionalGeneration 6 months ago
  • model-00002-of-00006.safetensors
    4.86 GB
    xet
    Upload LlavaForConditionalGeneration 6 months ago
  • model-00003-of-00006.safetensors
    4.86 GB
    xet
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  • model-00004-of-00006.safetensors
    4.86 GB
    xet
    Upload LlavaForConditionalGeneration 6 months ago
  • model-00005-of-00006.safetensors
    4.86 GB
    xet
    Upload LlavaForConditionalGeneration 6 months ago
  • model-00006-of-00006.safetensors
    3.31 GB
    xet
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  • model.safetensors.index.json
    70.1 kB
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  • preprocessor_config.json
    505 Bytes
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  • special_tokens_map.json
    552 Bytes
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  • tokenizer.json
    1.84 MB
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  • tokenizer.model
    500 kB
    xet
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  • tokenizer_config.json
    1.32 kB
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