Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
multimodal-embedding
retrieval
evaluation
custom_code
Instructions to use woooooz/WeMM-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use woooooz/WeMM-Embedding-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="woooooz/WeMM-Embedding-9B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from woooooz/WeMM-Embedding-9B: direct link, hf CLI and curl.
- Browser
- Download file 106 Bytes
-
https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/modules.json
- Command line
-
hf download hf://woooooz/WeMM-Embedding-9B/modules.json
-
curl -L -o modules.json https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/modules.json
106 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "modeling_st_wemm.WeMMTransformer" | |
| } | |
| ] | |