Image Feature Extraction
Transformers
Safetensors
English
videollama3_vision_encoder
feature-extraction
visual-encoder
multi-modal-large-language-model
custom_code
Instructions to use DAMO-NLP-SG/VL3-SigLIP-NaViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VL3-SigLIP-NaViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="DAMO-NLP-SG/VL3-SigLIP-NaViT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DAMO-NLP-SG/VL3-SigLIP-NaViT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Videollama3VisionEncoderModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_videollama3_encoder.Videollama3VisionEncoderConfig", | |
| "AutoModel": "modeling_videollama3_encoder.Videollama3VisionEncoderModel" | |
| }, | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1152, | |
| "intermediate_size": 4304, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "videollama3_vision_encoder", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 27, | |
| "patch_size": 14, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.46.3" | |
| } | |