---
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.