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