---
license: apache-2.0
language:
- en
base_model:
- google/medgemma-1.5-4b-it
pipeline_tag: question-answering
tags:
- medical
- agent
---


# VAJRAM Edge & Cloud Models: Clinical Decision Support Toolkit

This repository contains the highly optimized `.gguf` weights, vision projectors, and LoRA adapters powering **Project VAJRAM**, an agentic Clinical Decision Support System (CDSS) for Multiple Myeloma. 

These models are heavily quantized and formatted to run entirely offline on cpu, hf spaces and even on android phones (via `llama.rn`) utilizing a LangGraph-driven Mixture of Adapters (MoA) architecture.

## 📦 Repository Contents (The MoA Arsenal)

This is a modular toolkit. Rather than running one massive monolithic model, VAJRAM utilizes a fast base model and hot-swaps lightweight LoRA adapters into memory depending on the clinical task.

### 1. Base Models & Vision
* **`medgemma_q4km.gguf` (2.49 GB):** The core foundational medical reasoning engine. Quantized to `Q4_K_M` to perfectly balance speed, RAM footprint, and preservation of the 256k Gemma medical vocabulary. Fits comfortably in <4GB RAM.
* **`medgemma_vision_base.gguf`:** The vision-aligned base model for multi-modal tasks.
* **`medgemma_Bone_marrow_vision.gguf` :** The Llava-style Multimodal Projector (`mmproj`). This converts clinical WSI (Whole Slide Image) patch pixels into embeddings the LLM can understand.

### 2. The Clinical LoRA Adapters
These are lightweight (<120MB), hot-swappable domain experts trained for specific LangGraph agent nodes:
* **`lora_module2.gguf`:** Agent Tool2 - For Multiple Myeloma risk stratification.
* **`lora_module3.gguf`:** Agent Tool3 - For Bone Marrow biopsy analysis for figure out percentage of malignant myeloma cells.
* **`lora_module4.gguf`:** Agent Tool4 - For Myeloma Progression analysis.

### 3. Build Artifacts
* **`llama_cpp_python-0.3.16-cp310-cp310-linux_x86_64.whl` (4.75 MB):** A custom, pre-compiled Python wheel with OpenBLAS hardware acceleration baked in. Used to bypass strict compilation timeouts when deploying the VAJRAM orchestrator to serverless environments like Hugging Face Spaces.

## 💻 Open Source Architecture & Code
The complete orchestrator codebase, including the LangGraph MoA setup, Python backend, and the native cpu and gpu based application, is fully open-source. 

For all the codes and brief documentation: 
👉 **[View Project VAJRAM on GitHub](https://github.com/shrishSVaidya/Project-VAJRAM)**

## 🌐 Live Cloud Demo
Want to test the cloud-based MoA orchestrator without installing anything? The LangGraph Python backend is currently deployed as a live interactive web app.

👉 **[Test VAJRAM on Hugging Face Spaces](https://huggingface.co/spaces/shrishSVaidya/Project_VAJRAM)**
*(Note: This demo is hosted on a free-tier CPU environment. Inference generation will be significantly slower than the native, hardware-accelerated Edge deployment).*

## 🚀 Quick Start: Inference via CLI

To test the base model locally on your terminal using `llama.cpp`:

```bash
# Standard Text Inference
./llama-cli -m medgemma_q4km.gguf -p "User: What are the distinct morphological features of a myeloblast? \n\nAssistant:" -n 256 --temp 0.2

# Testing a LoRA Hot-Swap
./llama-cli -m medgemma_q4km.gguf --lora lora_module2.gguf -p "User: Analyze this patient protocol... \n\nAssistant:" -n 256
```