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AXERA-TECH
/
InternVL3-1B

Image-Text-to-Text
Transformers
ONNX
English
internvl_chat
feature-extraction
InternVL3
InternVL3-1B
Int8
VLM
custom_code
Model card Files Files and versions
xet
Community

Instructions to use AXERA-TECH/InternVL3-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AXERA-TECH/InternVL3-1B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="AXERA-TECH/InternVL3-1B", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("AXERA-TECH/InternVL3-1B", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use AXERA-TECH/InternVL3-1B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AXERA-TECH/InternVL3-1B"
    # 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/InternVL3-1B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AXERA-TECH/InternVL3-1B
  • SGLang

    How to use AXERA-TECH/InternVL3-1B 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/InternVL3-1B" \
        --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/InternVL3-1B",
    		"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/InternVL3-1B" \
            --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/InternVL3-1B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AXERA-TECH/InternVL3-1B with Docker Model Runner:

    docker model run hf.co/AXERA-TECH/InternVL3-1B
InternVL3-1B
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  • 2 contributors
History: 8 commits
yongqiang
add vit_slim-onnx model
797dd3d 9 months ago
  • internvl3_1b_ax650
    add vit_slim-onnx model 9 months ago
  • internvl3_tokenizer
    Upload 47 files 11 months ago
  • .gitattributes
    1.77 kB
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  • README.md
    6.14 kB
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  • config.json
    3.87 kB
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  • gradio_demo.py
    3.95 kB
    Upload 47 files 11 months ago
  • internvl3_tokenizer.py
    6.67 kB
    Upload 47 files 11 months ago
  • main_api_ax650
    6.67 MB
    xet
    Upload 47 files 11 months ago
  • main_api_axcl_x86
    1.93 MB
    xet
    Upload 47 files 11 months ago
  • main_ax650
    6.57 MB
    xet
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  • main_axcl_x86
    1.84 MB
    xet
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  • post_config.json
    277 Bytes
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  • run_internvl_3_1b_448_api_ax650.sh
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  • run_internvl_3_1b_448_api_axcl_x86.sh
    516 Bytes
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  • run_internvl_3_1b_448_ax650.sh
    506 Bytes
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  • run_internvl_3_1b_448_axcl_x86.sh
    527 Bytes
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  • ssd_car.jpg
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