Instructions to use Musyysysy/bge-m3-dynamic-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Musyysysy/bge-m3-dynamic-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Musyysysy/bge-m3-dynamic-int8") 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
File size: 789 Bytes
d9674cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"base_model": "BAAI/bge-m3",
"quantization": {
"method": "PyTorch dynamic quantization",
"dtype": "torch.qint8",
"quantized_modules": [
"torch.nn.Linear"
],
"device": "cpu"
},
"sentence_transformers": {
"max_seq_length": 128,
"normalize_embeddings": true
},
"checkpoint": {
"filename": "bge_m3_quantized_state_dict.pt",
"format": "PyTorch state_dict"
},
"evaluation": {
"fast_mode": true,
"base_inference_time_seconds": 5.61921859998256,
"quantized_inference_time_seconds": 0.5463337999826763,
"speedup": 10.285321172076008,
"base_state_dict_size_mib": 2165.9678840637207,
"quantized_state_dict_size_mib": 1299.0863275527954,
"size_reduction_percent": 40.0228259564269
}
} |