Image-Text-to-Text
Transformers
Safetensors
English
Chinese
ovis
text-generation
MLLM
conversational
custom_code
Instructions to use AIDC-AI/Ovis2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIDC-AI/Ovis2-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="AIDC-AI/Ovis2-4B", 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AIDC-AI/Ovis2-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AIDC-AI/Ovis2-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AIDC-AI/Ovis2-4B", "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/AIDC-AI/Ovis2-4B
- SGLang
How to use AIDC-AI/Ovis2-4B 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 "AIDC-AI/Ovis2-4B" \ --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": "AIDC-AI/Ovis2-4B", "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 "AIDC-AI/Ovis2-4B" \ --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": "AIDC-AI/Ovis2-4B", "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 AIDC-AI/Ovis2-4B with Docker Model Runner:
docker model run hf.co/AIDC-AI/Ovis2-4B
| # copied from https://huggingface.co/apple/aimv2-huge-patch14-448 | |
| from typing import Any | |
| from transformers.configuration_utils import PretrainedConfig | |
| __all__ = ["AIMv2Config"] | |
| class AIMv2Config(PretrainedConfig): | |
| """This is the configuration class to store the configuration of an [`AIMv2Model`]. | |
| Instantiating a configuration with the defaults will yield a similar configuration | |
| to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224). | |
| Args: | |
| hidden_size: Dimension of the hidden representations. | |
| intermediate_size: Dimension of the SwiGLU representations. | |
| num_hidden_layers: Number of hidden layers in the Transformer. | |
| num_attention_heads: Number of attention heads for each attention layer | |
| in the Transformer. | |
| num_channels: Number of input channels. | |
| image_size: Image size. | |
| patch_size: Patch size. | |
| rms_norm_eps: Epsilon value used for the RMS normalization layer. | |
| attention_dropout: Dropout ratio for attention probabilities. | |
| projection_dropout: Dropout ratio for the projection layer after the attention. | |
| qkv_bias: Whether to add a bias to the queries, keys and values. | |
| use_bias: Whether to add a bias in the feed-forward and projection layers. | |
| kwargs: Keyword arguments for the [`PretrainedConfig`]. | |
| """ | |
| model_type: str = "aimv2" | |
| def __init__( | |
| self, | |
| hidden_size: int = 1024, | |
| intermediate_size: int = 2816, | |
| num_hidden_layers: int = 24, | |
| num_attention_heads: int = 8, | |
| num_channels: int = 3, | |
| image_size: int = 224, | |
| patch_size: int = 14, | |
| rms_norm_eps: float = 1e-5, | |
| attention_dropout: float = 0.0, | |
| projection_dropout: float = 0.0, | |
| qkv_bias: bool = False, | |
| use_bias: bool = False, | |
| **kwargs: Any, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.attention_dropout = attention_dropout | |
| self.rms_norm_eps = rms_norm_eps | |
| self.projection_dropout = projection_dropout | |
| self.qkv_bias = qkv_bias | |
| self.use_bias = use_bias | |