Instructions to use chandanreddy/rx-interactome-lora-medgemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chandanreddy/rx-interactome-lora-medgemma with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="chandanreddy/rx-interactome-lora-medgemma")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chandanreddy/rx-interactome-lora-medgemma", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chandanreddy/rx-interactome-lora-medgemma with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chandanreddy/rx-interactome-lora-medgemma"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chandanreddy/rx-interactome-lora-medgemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chandanreddy/rx-interactome-lora-medgemma

SGLang

How to use chandanreddy/rx-interactome-lora-medgemma with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "chandanreddy/rx-interactome-lora-medgemma" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chandanreddy/rx-interactome-lora-medgemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "chandanreddy/rx-interactome-lora-medgemma" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chandanreddy/rx-interactome-lora-medgemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chandanreddy/rx-interactome-lora-medgemma with Docker Model Runner:
```
docker model run hf.co/chandanreddy/rx-interactome-lora-medgemma
```

🧬 Rx-Interactome MedGemma (LoRA Fine-Tuned)

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

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))

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Model tree for chandanreddy/rx-interactome-lora-medgemma

Base model

google/gemma-3-4b-pt

Finetuned

google/medgemma-4b-pt

Finetuned

google/medgemma-4b-it

Adapter

(110)

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