| --- |
| 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. |
|
|
| ## 馃摝 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. |
| |
| ## 馃捇 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: |
| 馃憠 **[View Project VAJRAM on GitHub](https://github.com/shrishSVaidya/Project-VAJRAM)** |
| |
| ## 馃寪 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. |
| |
| 馃憠 **[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).* |
| |
| ## 馃殌 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 |
| ``` |
| |
