Feature Extraction
Transformers
Safetensors
Chinese
tianmu_mere
multimodal
embedding
retrieval
e-commerce
product-understanding
image-text-retrieval
fashion
Instructions to use TianmuLab/Tianmu-MERE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TianmuLab/Tianmu-MERE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TianmuLab/Tianmu-MERE")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TianmuLab/Tianmu-MERE", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "initializer_factor": 1.0, | |
| "model_type": "siglip", | |
| "text_config": { | |
| "hidden_size": 1152, | |
| "intermediate_size": 4304, | |
| "model_type": "siglip_text_model", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 27, | |
| "projection_size": 1152, | |
| "vocab_size": 256000 | |
| }, | |
| "transformers_version": "4.49.0.dev0", | |
| "vision_config": { | |
| "hidden_size": 1152, | |
| "image_size": 384, | |
| "intermediate_size": 4304, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 27, | |
| "patch_size": 14 | |
| } | |
| } | |