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--- |
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license: apache-2.0 |
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tags: |
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- chemistry |
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- drug-discovery |
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- molecular-modeling |
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- mumo |
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--- |
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# mumo-pin1 |
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This model was trained using MuMo (Multi-Modal Molecular) framework. |
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## Model Description |
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- **Model Type**: MuMo Pretrained Model |
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- **Training Data**: Molecular structures and properties |
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- **Framework**: PyTorch + Transformers |
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## Usage |
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Loading the Model |
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MuMo uses a custom loading function. Here's how to load the pretrained model: |
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git clone https://github.com/selmiss/MuMo.git |
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from transformers import AutoConfig, AutoTokenizer |
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from model.load_model import load_model |
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from dataclasses import dataclass |
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# Load configuration and tokenizer |
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repo = "zihaojing/MuMo-pin1" |
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config = AutoConfig.from_pretrained(repo, trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(repo) |
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# Set up model arguments |
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class ModelArgs: |
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model_name_or_path: str = repo |
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model_class: str = "MuMoFinetunePairwise" # or "MuMoPretrain" for pretraining |
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cache_dir: str = None |
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model_revision: str = "main" |
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use_auth_token: bool = False |
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task_type: str = None # e.g., "classification" or "regression" for finetuning |
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model_args = ModelArgs() |
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# Load the model |
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model = load_model(config, tokenizer=tokenizer, model_args=model_args) |
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Notes: |
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Use model_class="MuMoPretrain" for pretraining or inference |
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Use model_class="MuMoFinetune" or "MuMoFinetunePairwise" for finetuning tasks |
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Set task_type to "classification" or "regression" when using MuMoFinetune |
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The model supports loading from both Hugging Face Hub (e.g., "zihaojing/MuMo-pin1") and local paths (e.g., "/path/to/model") |
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## Training Details |
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- Training script: See repository for details |
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- Framework: Transformers + DeepSpeed |
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## Citation |
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If you use this model, please cite the original MuMo paper. |
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