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Cloudriver
/
ViSpec-Qwen2.5-VL-7B-Instruct

Image-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct")
    model = AutoModelForImageTextToText.from_pretrained("Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct
  • SGLang

    How to use Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct 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 "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct" \
        --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": "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct",
    		"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 "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct" \
            --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": "Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct with Docker Model Runner:

    docker model run hf.co/Cloudriver/ViSpec-Qwen2.5-VL-7B-Instruct
ViSpec-Qwen2.5-VL-7B-Instruct
3.56 GB
Ctrl+K
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  • 1 contributor
History: 3 commits
Cloudriver's picture
Cloudriver
Update README: benchmark positioning and citations
7001cbd verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    2.53 kB
    Update README: benchmark positioning and citations 3 months ago
  • config.json
    703 Bytes
    Upload ViSpec-Qwen2.5-VL-7B-Instruct checkpoint 3 months ago
  • model.safetensors
    3.56 GB
    xet
    Upload ViSpec-Qwen2.5-VL-7B-Instruct checkpoint 3 months ago