Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
qwen3_5
image-text-to-text
multimodal-embedding
text-embedding
image-embedding
video-embedding
mrl
custom_code
Instructions to use tencent/WeMM-Embedding-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/WeMM-Embedding-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tencent/WeMM-Embedding-2B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tencent/WeMM-Embedding-2B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("tencent/WeMM-Embedding-2B", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use tencent/WeMM-Embedding-2B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/WeMM-Embedding-2B", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -113,12 +113,11 @@ print(query_embeddings.shape, document_embeddings.shape)
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similarities = model.similarity(query_embeddings, document_embeddings)
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print(similarities)
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# tensor([[0.0685, 0.5379, 0.0423],
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# [0.7829, 0.1852, 0.4729])
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```
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Each input is a string, a URL or path, a `PIL.Image`, or a dict combining `image`,
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`video`, and `text` keys.
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the ordering used above. Chat messages such as
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`{"role": "user", "content": [{"type": "image", "image": ...}, {"type": "text", "text": ...}]}`
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are also accepted, which is the way to interleave several images or videos in one input.
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similarities = model.similarity(query_embeddings, document_embeddings)
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print(similarities)
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# tensor([[0.0685, 0.5379, 0.0423],
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# [0.7829, 0.1852, 0.4729]])
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```
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Each input is a string, a URL or path, a `PIL.Image`, or a dict combining `image`,
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`video`, and `text` keys. Chat messages such as
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`{"role": "user", "content": [{"type": "image", "image": ...}, {"type": "text", "text": ...}]}`
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are also accepted, which is the way to interleave several images or videos in one input.
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