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 tokenizer.json from woooooz/WeMM-Embedding-9B: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/tokenizer.json
- Command line
-
hf download hf://woooooz/WeMM-Embedding-9B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/tokenizer.json
20 MB
- Xet hash:
- de0ebff7c9772a2d3d2dec2e74f192614b356de68fd99836bc00189f082667f3
- Size of remote file:
- 20 MB
- SHA256:
- 40e444c744512f423da4c8443c47c21e22ff76056ba4e9796a81c04c13a9daf0
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