Instructions to use ananddey/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ananddey/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ananddey/bge-m3") 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
BGE-M3 INT8 ONNX
INT8 quantized ONNX version of BGE-M3, optimized for faster inference and lower memory usage while preserving strong multilingual embedding performance.
Model Details
- Base Model: BAAI/bge-m3
- Format: ONNX
- Quantization: INT8
- Embedding Size: 1024
- Max Sequence Length: 8192
This model was converted from the original BGE-M3 model and quantized to INT8 for improved deployment efficiency on CPU and edge environments.
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