Instructions to use InternRobotics/G2VLM-Qwen2-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternRobotics/G2VLM-Qwen2-VL-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="InternRobotics/G2VLM-Qwen2-VL-2B")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InternRobotics/G2VLM-Qwen2-VL-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InternRobotics/G2VLM-Qwen2-VL-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InternRobotics/G2VLM-Qwen2-VL-2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InternRobotics/G2VLM-Qwen2-VL-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/InternRobotics/G2VLM-Qwen2-VL-2B
- SGLang
How to use InternRobotics/G2VLM-Qwen2-VL-2B 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 "InternRobotics/G2VLM-Qwen2-VL-2B" \ --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": "InternRobotics/G2VLM-Qwen2-VL-2B", "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 "InternRobotics/G2VLM-Qwen2-VL-2B" \ --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": "InternRobotics/G2VLM-Qwen2-VL-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use InternRobotics/G2VLM-Qwen2-VL-2B with Docker Model Runner:
docker model run hf.co/InternRobotics/G2VLM-Qwen2-VL-2B
Download preprocessor_config.json from InternRobotics/G2VLM-Qwen2-VL-2B: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/InternRobotics/G2VLM-Qwen2-VL-2B/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://InternRobotics/G2VLM-Qwen2-VL-2B/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/InternRobotics/G2VLM-Qwen2-VL-2B/resolve/main/preprocessor_config.json
347 Bytes
- Xet hash:
- 347c82a8ab29046cd46da44246c32bb4fbecac09dfdcb278b08e9780a2fe5448
- Size of remote file:
- 347 Bytes
- SHA256:
- b5eaad0c2815f07631535dcc58f3c462b0d73693638ad21d19f3c50820eae1cc
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