Feature Extraction
sentence-transformers
Safetensors
GGUF
English
Chinese
multilingual
qwen3_5
multimodal
embeddings
retrieval
quantization
mixed-precision
w4a8
fp8
int4
svd
mrl
text-embeddings
image-embedding
video-embedding
cross-modal
custom_code
Eval Results (legacy)
Instructions to use ewin-reg/WeMM-Embedding-2B-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ewin-reg/WeMM-Embedding-2B-Quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewin-reg/WeMM-Embedding-2B-Quantized", 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
feat: Final Hybrid FP8 + INT4-g16 (99.2204% fidelity, 0.7796% degradation, 1.75GB)
Browse files- model.safetensors +2 -2
model.safetensors
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