Feature Extraction
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3_5
multimodal
embeddings
retrieval
quantization
mixed-precision
int8
w8a8
mrl
cross-modal
text-embeddings
image-embedding
video-embedding
custom_code
Eval Results (legacy)
8-bit precision
Instructions to use ewin-reg/WeMM-Embedding-2B-INT8 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-INT8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewin-reg/WeMM-Embedding-2B-INT8", 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
card: the wide variant now ships, point at it and state what it does not buy
Browse files
README.md
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## What this build does not change
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It does not touch the int4 tensors, so the dominant quantization error in the model is unchanged. Measured on this model's own shapes, the int4 group-16 grid carries about 9 percent weight error against 0.6 percent for the new int8 grid, which is where the remaining 0.007 gap to the base lives. That gap is documented, not hidden
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## What this build does not change
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It does not touch the int4 tensors, so the dominant quantization error in the model is unchanged. Measured on this model's own shapes, the int4 group-16 grid carries about 9 percent weight error against 0.6 percent for the new int8 grid, which is where the remaining 0.007 gap to the base lives. That gap is documented, not hidden. A wider variant that converts those same int4 tensors to int8 now exists as [WeMM-Embedding-2B-INT8-Wide](https://huggingface.co/ewin-reg/WeMM-Embedding-2B-INT8-Wide), and it measured 0.993015 against the base on the text protocol, which is this build's own 0.992999 to within the noise. Buy it for one fewer dtype family, not for better embeddings. This repo stays behavior compatible with the FP8 card.
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