LowNSAE

Sparse Autoencoders Improve Low - N Protein Function Prediction and Design

This repository contains the models used in the paper "Sparse Autoencoders Improve Low - N Protein Function Prediction and Design", by Darin Tsui, Kunal Talreja, and Amirali Aghazadeh. A link to the paper can be found here.

models/
├── esm2_t33_650M_UR50D/           # Base ESM2 protein language model
├── SPG1_STRSG_Wu_2016/            # Protein-specific models for SPG1
├── SPG1_STRSG_Olson_2014/         # Protein-specific models for SPG1 (Olson)
├── GRB2_HUMAN_Faure_2021/         # Protein-specific models for GRB2
├── GFP_AEQVI_Sarkisyan_2016/      # Protein-specific models for GFP
├── F7YBW8_MESOW_Ding_2023/        # Protein-specific models for F7YBW8
└── DLG4_HUMAN_Faure_2021/         # Protein-specific models for DLG4

Each protein directory contains two types of models:

Fine-tuned ESM2 Models (esm_ft/)

• adapter_model.safetensors: Fine-tuning adapter weights
• adapter_config.json: Adapter configuration
• README.md: Model card with detailed information
• tokenizer_config.json
• special_tokens_map.json

Sparse Autoencoders (sae/)

• checkpoints/: Training checkpoints with different loss values
• Model files with naming pattern: esm2_plm{PLM_DIM}_l{SAE_LAYER}_sae{SAE_DIM}_k{SAE_K}_auxk{SAE_AUXK}_-step={step}-avg_mse_loss={loss}.ckpt
• Last checkpoint simply named last.ckpt

Usage

Loading Fine-tuned Models

from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForMaskedLM

# Load fine-tuned model
base_model = AutoModel.from_pretrained("models/esm2_t33_650M_UR50D")
ft_model = PeftModel.from_pretrained(base_model, f"models/{DMS_ASSAY}/esm_ft")