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.

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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

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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.

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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

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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

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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.

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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.

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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."

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Intended Use

This model is intended for:

• Cardiology education
• Medical AI research
• Cardiovascular education tools
• Healthcare chatbot development
• Cardiovascular research support

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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.

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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

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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

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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.

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Version

Version: 1.0

Future versions will include:

• Expanded cardiovascular datasets
• Improved cardiological reasoning accuracy
• Larger training datasets
• Enhanced cardiovascular intelligence

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Author

Abir Maheshwari
Independent AI Researcher
Founder, A.B.I.R Ecosystem

Hugging Face:
https://huggingface.co/abirmaheshwari

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License

MIT License