Image-Text-to-Text
Transformers
Safetensors
multilingual
internvl_chat
feature-extraction
internvl
custom_code
conversational
Instructions to use OpenGVLab/InternVL2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL2-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-4B", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternVL2-4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenGVLab/InternVL2-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenGVLab/InternVL2-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/OpenGVLab/InternVL2-4B
- SGLang
How to use OpenGVLab/InternVL2-4B 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 "OpenGVLab/InternVL2-4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "OpenGVLab/InternVL2-4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use OpenGVLab/InternVL2-4B with Docker Model Runner:
docker model run hf.co/OpenGVLab/InternVL2-4B
fix compatibility issue for transformers 4.46+
Browse files- config.json +1 -0
- configuration_internvl_chat.py +1 -1
config.json
CHANGED
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@@ -21,6 +21,7 @@
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi3.Phi3Config",
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"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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},
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"bad_words_ids": null,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi3.Phi3Config",
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+
"AutoModel": "modeling_phi3.Phi3ForCausalLM",
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"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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},
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"bad_words_ids": null,
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configuration_internvl_chat.py
CHANGED
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@@ -49,7 +49,7 @@ class InternVLChatConfig(PretrainedConfig):
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self.vision_config = InternVisionConfig(**vision_config)
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if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
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self.llm_config = LlamaConfig(**llm_config)
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-
elif llm_config.get
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self.llm_config = Phi3Config(**llm_config)
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else:
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raise ValueError('Unsupported architecture: {}'.format(llm_config.get(['architectures'])[0]))
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self.vision_config = InternVisionConfig(**vision_config)
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if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
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self.llm_config = LlamaConfig(**llm_config)
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+
elif llm_config.get('architectures')[0] == 'Phi3ForCausalLM':
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self.llm_config = Phi3Config(**llm_config)
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else:
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raise ValueError('Unsupported architecture: {}'.format(llm_config.get(['architectures'])[0]))
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