Create README.md

README.md

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+---
+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`:** Clinical Agent Node 2.
+* **`lora_module3.gguf`:** Clinical Agent Node 3.
+* **`lora_module4.gguf`:** Clinical Agent Node 4.
+
+### 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/Projet_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
+```