This model is a LoRA fine-tuned version of MedGemma-4B-it for protein–protein interaction reasoning and drug effect analysis on protein interaction networks (interactomes).

It is part of the Rx-Interactome project.

📌 Model Overview

Base Model: google/medgemma-4b-it
Fine-Tuning Method: Supervised Fine-Tuning (SFT)
Parameter-Efficient Training: LoRA adapters
Quantization: 4-bit (NF4)
Precision: bfloat16
Frameworks: Transformers, PEFT, TRL

This model is designed to reason over:

Protein–protein interaction sub-networks
Context-aware interaction patterns
Drug-induced network perturbations
Network-level therapeutic effects

🧠 Intended Use

This model is intended for:

Protein interaction reasoning
Systems biology research
Drug mechanism analysis
Network-based disease modeling
Hypothesis generation in biomedical research

Multiple proteins can be provided in the same prompt to enable interaction-level reasoning.

🗂 Training Data

Training data was constructed from:

Subcellular localization information
Pathway membership (Reactome)
Protein–protein interactions (STRING database)

Large interaction networks were decomposed into biologically meaningful 3–4 protein sub-networks to improve contextual learning.

Each training sample followed a chat-style format:

User: Protein sequences and context
Assistant: Interaction reasoning or network explanation

⚙️ Training Configuration

Epochs: 3
Learning Rate: 5e-4
Batch Size: 1
Gradient Accumulation: 2
Evaluation Steps: 15
Optimizer: AdamW (fused)
Scheduler: Linear
Max Gradient Norm: 0.3
Warmup Ratio: 0.03
Gradient Checkpointing: Enabled

LoRA Configuration:

r: 16
alpha: 16
dropout: 0.05
Target modules: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj

Quantization:

4-bit NF4
Double quantization enabled
bfloat16 compute dtype

🚀 How to Use

python

from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel

base_model = AutoModelForImageTextToText.from_pretrained(
    "google/medgemma-4b-it",
    device_map="auto",
    torch_dtype="bfloat16"
)

model = PeftModel.from_pretrained(
    base_model,
    "your-username/rx-interactome-medgemma"
)

processor = AutoProcessor.from_pretrained("google/medgemma-4b-it")

prompt = """
Analyze the following protein sequences:
Protein: ENSP000001
Sequence: MSEQUENCE...

Protein: ENSP000002
Sequence: ASEQUENCE...


Provide structured interpretation.
"""

inputs = processor(text=prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(processor.decode(outputs[0], skip_special_tokens=True))

chandanreddy/rx-interactome-lora-medgemma

🧬 Rx-Interactome MedGemma (LoRA Fine-Tuned)

📌 Model Overview

🧠 Intended Use

🗂 Training Data

⚙️ Training Configuration

🚀 How to Use

chandanreddy/
rx-interactome-lora-medgemma