feat: Added embedding_dtype and vocabulary_quantization to config #280
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This PR adds the used vocabulary_quantization and embedding_dtype for quantization to the config, which makes it easier to see the precision of the model and (optionally) number of clusters for the vocabulary directly from the config.
E.g.:
{ "model_type": "model2vec", "architectures": [ "StaticModel" ], "tokenizer_name": "baai/bge-base-en-v1.5", "apply_pca": 256, "apply_zipf": true, "hidden_dim": 256, "seq_length": 1000000, "normalize": true, "vocabulary_quantization": 128, "embedding_dtype": "float16" }