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/home/user/poc/node_modules/fastembed/node_modules/onnxruntime-node/dist/backend.js:24
            __classPrivateFieldGet(this, _OnnxruntimeSessionHandler_inferenceSession, "f").loadModel(pathOrBuffer, options);
                                                                                           
Error: Load model from local_cache/fast-bge-small-en-v1.5/model_optimized.onnx failed:Protobuf parsing failed.
    at new OnnxruntimeSessionHandler (/home/user/poc/node_modules/fastembed/node_modules/onnxruntime-node/dist/backend.js:24:92)
    at /home/user/poc/node_modules/fastembed/node_modules/onnxruntime-node/dist/backend.js:64:29
    at process.processTicksAndRejections (node:internal/process/task_queues:77:11)

let embeddingModel;
(async () => {
  //default model i am trying to load here 
    embeddingModel = await FlagEmbedding.init();
  }
})();

from fastembed.embedding import FlagEmbedding as Embedding
from typing import List
import numpy as np

documents: List[str] = [
    "passage: Hello, World!",
    "query: Hello, World!", # these are two different embedding
    "passage: This is an example passage.",
    "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
]
embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) 
embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator