tencent/WeMM-Embedding-2B开源了新模型

tencent/WeMM-Embedding-2B · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/tencent) tencent (https://huggingface.co/tencent) / WeMM-Embedding-2B (https://huggingface.co/tencent/WeMM-Embedding-2B) like 71 Follow Tencent 12k Feature Extraction (https://huggingface.co/models?pipeline_tag=feature-extraction) Transformers (https://huggingface.co/models?library=transformers) Safetensors (https://huggingface.co/models?library=safetensors) sentence-transformers (https://huggingface.co/models?library=sentence-transformers) Chinese (https://huggingface.co/models?language=zh) English (https://huggingface.co/models?language=en) qwen3_5 (https://huggingface.co/models?other=qwen3_5) image-text-to-text (https://huggingface.co/models?other=image-text-to-text) multimodal-embedding (https://huggingface.co/models?other=multimodal-embedding) text-embedding (https://huggingface.co/models?other=text-embedding) image-embedding (https://huggingface.co/models?other=image-embedding) video-embedding (https://huggingface.co/models?other=video-embedding) mrl (https://huggingface.co/models?other=mrl) custom_code (https://huggingface.co/models?other=custom_code) arxiv: 2608.24053 License: apache-2.0 Model card (https://huggingface.co/tencent/WeMM-Embedding-2B) Files Files and versions xet (https://huggingface.co/tencent/WeMM-Embedding-2B/tree/main) Community 3 (https://huggingface.co/tencent/WeMM-Embedding-2B/discussions) Deploy Copy to bucket new Use this model Instructions to use tencent/WeMM-Embedding-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/tencent/WeMM-Embedding-2B?library=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) # 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 (https://huggingface.co/tencent/WeMM-Embedding-2B?library=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." ] embe

Tencent tencent/WeMM-Embedding-2B tencent/WeMM-Embedding-2B https://huggingface.co/tencent/WeMM-Embedding-2B