Image-Text-to-Text
Transformers
Safetensors
multilingual
internvl_chat
feature-extraction
internvl
custom_code
conversational
Instructions to use OpenGVLab/InternVL2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL2-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-1B", 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/InternVL2-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenGVLab/InternVL2-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenGVLab/InternVL2-1B" # 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/InternVL2-1B", "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/InternVL2-1B
- SGLang
How to use OpenGVLab/InternVL2-1B 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 "OpenGVLab/InternVL2-1B" \ --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/InternVL2-1B", "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/InternVL2-1B" \ --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/InternVL2-1B", "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/InternVL2-1B with Docker Model Runner:
docker model run hf.co/OpenGVLab/InternVL2-1B
Upload folder using huggingface_hub
Browse files- README.md +5 -0
- modeling_internvl_chat.py +2 -1
README.md
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@@ -144,6 +144,7 @@ model = AutoModel.from_pretrained(
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval().cuda()
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load_in_8bit=True,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval()
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```
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load_in_4bit=True,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval()
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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device_map=device_map).eval()
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```
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval().cuda()
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
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path,
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torch_dtype=torch.bfloat16,
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use_flash_attn=True,
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trust_remote_code=True).eval().cuda()
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```
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torch_dtype=torch.bfloat16,
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load_in_8bit=True,
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trust_remote_code=True).eval()
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torch_dtype=torch.bfloat16,
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load_in_4bit=True,
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use_flash_attn=True,
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trust_remote_code=True).eval()
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path,
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use_flash_attn=True,
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trust_remote_code=True,
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device_map=device_map).eval()
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```
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path,
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torch_dtype=torch.bfloat16,
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use_flash_attn=True,
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trust_remote_code=True).eval().cuda()
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
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modeling_internvl_chat.py
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from .configuration_internvl_chat import InternVLChatConfig
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from .conversation import get_conv_template
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from .modeling_intern_vit import InternVisionModel
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logger = logging.get_logger(__name__)
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self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
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self.downsample_ratio = config.downsample_ratio
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self.ps_version = config.ps_version
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config.vision_config.use_flash_attn = True if use_flash_attn else False
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config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
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from .configuration_internvl_chat import InternVLChatConfig
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from .conversation import get_conv_template
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from .modeling_intern_vit import InternVisionModel, has_flash_attn
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logger = logging.get_logger(__name__)
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self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
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self.downsample_ratio = config.downsample_ratio
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self.ps_version = config.ps_version
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use_flash_attn = use_flash_attn if has_flash_attn else False
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config.vision_config.use_flash_attn = True if use_flash_attn else False
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config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
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