How to use tencent/WeMM-Embedding-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tencent/WeMM-Embedding-9B", trust_remote_code=True)
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tencent/WeMM-Embedding-9B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("tencent/WeMM-Embedding-9B", trust_remote_code=True, device_map="auto")
How to use tencent/WeMM-Embedding-9B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/WeMM-Embedding-9B", 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]
https://huggingface.co/tencent/WeMM-Embedding-9B/resolve/main/.gitattributes
hf download hf://tencent/WeMM-Embedding-9B/.gitattributes
curl -L -o .gitattributes https://huggingface.co/tencent/WeMM-Embedding-9B/resolve/main/.gitattributes
