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
WeMM-Embedding-9B with MMEB evaluation
Mirror of tencent/WeMM-Embedding-9B with the local Nexus MMEB evaluation source, configuration and runtime snapshot, prepared on 2026-10-15. The weights retain their upstream provenance and license. This is not a new fine-tuned model.
- Original model documentation and inference examples: UPSTREAM_README.md.
- Evaluation setup and runnable commands: evaluation/README.md.
- Integrity and source information: package_manifest.json.
The evaluation package includes image, video, moment and visual-document retrieval configurations. Evaluation data must be obtained separately. No new benchmark scores are asserted by this mirror.
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