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AXERA-TECH
/
Qwen2.5-VL-3B-Instruct

Image-Text-to-Text
Transformers
Safetensors
English
Chinese
Qwen2.5-VL
Qwen2.5-VL-3B-Instruct
Int8
VLM
Model card Files Files and versions
xet
Community

Instructions to use AXERA-TECH/Qwen2.5-VL-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AXERA-TECH/Qwen2.5-VL-3B-Instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="AXERA-TECH/Qwen2.5-VL-3B-Instruct")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("AXERA-TECH/Qwen2.5-VL-3B-Instruct", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use AXERA-TECH/Qwen2.5-VL-3B-Instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AXERA-TECH/Qwen2.5-VL-3B-Instruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "AXERA-TECH/Qwen2.5-VL-3B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AXERA-TECH/Qwen2.5-VL-3B-Instruct
  • SGLang

    How to use AXERA-TECH/Qwen2.5-VL-3B-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 "AXERA-TECH/Qwen2.5-VL-3B-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": "AXERA-TECH/Qwen2.5-VL-3B-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 "AXERA-TECH/Qwen2.5-VL-3B-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": "AXERA-TECH/Qwen2.5-VL-3B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AXERA-TECH/Qwen2.5-VL-3B-Instruct with Docker Model Runner:

    docker model run hf.co/AXERA-TECH/Qwen2.5-VL-3B-Instruct
Qwen2.5-VL-3B-Instruct / python
48.9 kB
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  • 4 contributors
History: 1 commit
qqc1989's picture
qqc1989
initial this repo
a3b1a17 verified over 1 year ago
  • cv_resize.py
    285 Bytes
    initial this repo over 1 year ago
  • infer_image.py
    8.96 kB
    initial this repo over 1 year ago
  • infer_text.py
    8.03 kB
    initial this repo over 1 year ago
  • infer_video.py
    8.91 kB
    initial this repo over 1 year ago
  • preprocess.py
    7.68 kB
    initial this repo over 1 year ago
  • utils.py
    15 kB
    initial this repo over 1 year ago