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-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/WeMM-Embedding-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tencent/WeMM-Embedding-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tencent/WeMM-Embedding-4B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("tencent/WeMM-Embedding-4B", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use tencent/WeMM-Embedding-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/WeMM-Embedding-4B", 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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Integrate with Sentence Transformers, restore training-time tokenization on newer transformers (#1)
- Integrate with Sentence Transformers, restore training-time tokenization on newer transformers (a78aeb413f7ff4bc6fe6628af72b4a0953c9302b) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>52cc528 -
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