--- license: mit language: - en tags: - medical - Cardiovascular - A.B.I.R - abirmaheshwari - slm - indiaai - heart-disease library_name: transformers --- # abirmed_cardio_slm — Cardiovascular and Heart Disease Specialist Transformer ### Part of the A.B.I.R Ecosystem abirmed_cardio_slm is a specialized cardiology language model developed as part of the **A.B.I.R Ecosystem** and the **ABIRMED Modular Medical Specialist Transformer System**, a distributed artificial intelligence architecture designed to replicate real-world medical specialization using modular transformer models. This model functions as the **Cardiology Specialist**, designed to understand cardiovascular symptoms, heart diseases, circulatory system disorders, and cardiological reasoning patterns. This is **Version 1.0**, with future versions planned for expanded cardiovascular datasets, improved heart disease reasoning accuracy, and enhanced cardiological intelligence capabilities. --- # ABIRMED — Modular Medical Specialist Transformer System ABIRMED is a modular medical AI ecosystem consisting of multiple specialist Small Language Models (SLMs), each trained for a specific medical domain. Instead of using a single large monolithic model, ABIRMED uses a distributed specialist architecture inspired by real-world clinical specialization. Each model acts as an independent medical specialist while collectively forming a unified medical reasoning system. This modular approach provides: - Higher accuracy within specialized domains - Lower computational requirements - CPU-efficient inference capability - Scalable and extensible medical intelligence architecture Developed by: Abir Maheshwari Architecture: Modular Decoder-only Transformer System Framework: PyTorch + HuggingFace Transformers Training Platform: Google Colab T4 GPU License: MIT --- # Role of abirmed_cardio_slm in the ABIRMED System abirmed_cardio_slm functions as the **Cardiology Specialist**, equivalent to a clinical cardiologist in real-world healthcare systems. Its primary role is to provide cardiovascular reasoning capabilities including: - Heart disease interpretation - Cardiovascular symptom analysis - Circulatory system disorder explanation - Cardiac condition reasoning - Cardiovascular education support This model complements other ABIRMED specialist models such as diagnosis, pharmacology, pathology, emergency, psychiatry, dermatology, pediatrics, and veterinary models. --- # Model Details Model Name: abirmed_cardio_slm Version: 1.0 Developer: Abir Maheshwari Organization: A.B.I.R Ecosystem Model Type: Causal Language Model (Decoder-only Transformer) Base Model: None (trained from scratch) License: MIT --- # Technical Specifications Architecture: Decoder-only Transformer Parameters: ~38 Million Transformer Layers: 8 Attention Heads: 8 Hidden Size: 512 Intermediate Size: 2048 Context Length: 256 tokens Tokenizer: GPT-2 tokenizer with custom PAD token Weight Sharing: Embedding and LM Head tied Training Objective: Causal Language Modeling Precision: FP16 mixed precision Framework: PyTorch Export Formats: - safetensors - PyTorch (.pt) Checkpoint Support: - Full training state resume capability --- # Training Details ## Training Dataset Primary datasets include curated cardiovascular and cardiology educational datasets containing: - Heart disease descriptions - Cardiovascular condition explanations - Circulatory system disorder information - Cardiac clinical reasoning narratives These datasets enable the model to learn relationships between cardiovascular symptoms and heart diseases. --- ## Training Procedure Optimizer: AdamW Learning Rate: 5e-4 Batch Size: 8 Gradient Accumulation Steps: 2 Training Platform: - Google Colab - NVIDIA T4 GPU Training Objective: - Predict next token in cardiological reasoning sequences Training Format: Instruction → Output Converted to: Question → Answer format Identity training lines were included to ensure proper integration into the ABIRMED ecosystem. --- # Capabilities abirmed_cardio_slm is capable of: - Understanding cardiovascular symptoms - Explaining heart diseases - Supporting cardiology education - Providing cardiovascular reasoning explanations - Supporting cardiology research Example: Input: "Chest pain radiating to the arm" Output: "This symptom may indicate myocardial infarction, a serious cardiovascular condition affecting the heart." --- # Intended Use This model is intended for: - Cardiology education - Medical AI research - Cardiovascular education tools - Healthcare chatbot development - Cardiovascular research support --- # Out-of-Scope Use This model is not intended for: - Clinical cardiology diagnosis - Medical treatment decisions - Emergency cardiac decision making - Replacement of licensed cardiologists This is a research model only. --- # Limitations abirmed_cardio_slm: - Is not a licensed cardiology system - May produce incomplete cardiovascular assessments - Should not replace medical professionals - May lack full cardiological accuracy --- # Design Philosophy The ABIRMED ecosystem follows a modular specialist architecture inspired by real-world healthcare systems. Each model specializes in a specific domain. abirmed_cardio_slm serves as the cardiovascular intelligence specialist. This architecture improves: - Domain accuracy - Reasoning reliability - Computational efficiency - Modular scalability --- # A.B.I.R Ecosystem Integration abirmed_cardio_slm is part of the A.B.I.R Ecosystem, which includes: - Modular transformer intelligence systems - Language models - Domain-specialized AI systems - Medical AI infrastructure ABIRMED represents the medical intelligence division of the A.B.I.R Ecosystem. --- # Version Version: 1.0 Future versions will include: - Expanded cardiovascular datasets - Improved cardiological reasoning accuracy - Larger training datasets - Enhanced cardiovascular intelligence --- # Author Abir Maheshwari Independent AI Researcher Founder, A.B.I.R Ecosystem Hugging Face: https://huggingface.co/abirmaheshwari --- # License MIT License