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AXERA-TECH
/
Qwen3-VL-4B-Instruct

Image-Text-to-Text
Transformers
English
Chinese
Qwen3-VL
Qwen3-VL-2B-Instruct
Qwen3-VL-4B-Instruct
Int8
VLM
Model card Files Files and versions
xet
Community

Instructions to use AXERA-TECH/Qwen3-VL-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AXERA-TECH/Qwen3-VL-4B-Instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="AXERA-TECH/Qwen3-VL-4B-Instruct")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("AXERA-TECH/Qwen3-VL-4B-Instruct", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use AXERA-TECH/Qwen3-VL-4B-Instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AXERA-TECH/Qwen3-VL-4B-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/Qwen3-VL-4B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AXERA-TECH/Qwen3-VL-4B-Instruct
  • SGLang

    How to use AXERA-TECH/Qwen3-VL-4B-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/Qwen3-VL-4B-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/Qwen3-VL-4B-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/Qwen3-VL-4B-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/Qwen3-VL-4B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AXERA-TECH/Qwen3-VL-4B-Instruct with Docker Model Runner:

    docker model run hf.co/AXERA-TECH/Qwen3-VL-4B-Instruct
Qwen3-VL-4B-Instruct
6.56 GB
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  • 2 contributors
History: 7 commits
lihongjie
update
c18b793 6 months ago
  • Qwen3-VL-4B-Instruct-AX650-c128_p1152
    first commit 7 months ago
  • images
    first commit 7 months ago
  • qwen3-vl-tokenizer
    first commit 7 months ago
  • video
    first commit 7 months ago
  • .gitattributes
    7.04 kB
    first commit 7 months ago
  • README.md
    12.1 kB
    update 6 months ago
  • config.json
    0 Bytes
    first commit 7 months ago
  • main_ax650
    6.65 MB
    xet
    update 6 months ago
  • main_axcl_x86
    1.9 MB
    xet
    update 6 months ago
  • post_config.json
    275 Bytes
    first commit 7 months ago
  • qwen3_tokenizer.py
    10.3 kB
    update 6 months ago
  • requirements.txt
    89 Bytes
    first commit 7 months ago
  • run_image_ax650.sh
    785 Bytes
    first commit 7 months ago
  • run_image_axcl_aarch64.sh
    725 Bytes
    first commit 7 months ago
  • run_image_axcl_x86.sh
    721 Bytes
    first commit 7 months ago
  • run_video_ax650.sh
    785 Bytes
    first commit 7 months ago
  • run_video_axcl_aarch64.sh
    725 Bytes
    first commit 7 months ago
  • run_video_axcl_x86.sh
    721 Bytes
    first commit 7 months ago