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
| { | |
| "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 | |
| } | |
| } |