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```markdown |
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This folder will contain the pre-trained weights for the **Facilitator** model. The Facilitator model is part of the BioM3 pipeline and serves as a key component for further alignment or generation tasks. |
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--- |
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## **Downloading Pre-trained Weights** |
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The Google Drive link for downloading the Facilitator pre-trained weights will be added here soon. |
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```bash |
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pip install gdown # assuming gdown package is not already installed |
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gdown --id 1_YWwILXDkx9MSoSA1kfS-y0jk3Vy4HJE -O BioM3_Facilitator_epoch20.bin |
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``` |
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--- |
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## **Usage** |
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Once available, the pre-trained weights can be loaded as follows: |
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```python |
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import json |
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import torch |
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from argparse import Namespace |
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import Stage1_source.model as mod |
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# Step 1: Load JSON Configuration |
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def load_json_config(json_path): |
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""" |
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Load a JSON configuration file and return it as a dictionary. |
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""" |
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with open(json_path, "r") as f: |
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config = json.load(f) |
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return config |
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# Step 2: Convert JSON Dictionary to Namespace |
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def convert_to_namespace(config_dict): |
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""" |
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Recursively convert a dictionary to an argparse Namespace. |
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""" |
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for key, value in config_dict.items(): |
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if isinstance(value, dict): |
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config_dict[key] = convert_to_namespace(value) |
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return Namespace(**config_dict) |
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if __name__ == '__main__': |
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# Path to configuration and weights |
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config_path = "stage2_config.json" |
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model_weights_path = "weights/Facilitator/BioM3_Facilitator_epoch20.bin" |
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# Load Configuration |
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print("Loading configuration...") |
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config_dict = load_json_config(config_path) |
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config_args = convert_to_namespace(config_dict) |
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# Load Model |
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print("Loading pre-trained model weights...") |
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model = mod.Facilitator( |
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in_dim=config_args.emb_dim, |
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hid_dim=config_args.hid_dim, |
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out_dim=config_args.emb_dim, |
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dropout=config_args.dropout |
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) # Initialize the model with arguments |
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model.load_state_dict(torch.load(model_weights_path, map_location="cpu")) |
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model.eval() |
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print("Model loaded successfully with weights!") |
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``` |
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