abirmed_core_slm — Foundational General Medical Transformer
Part of the A.B.I.R Ecosystem
abirmed_core_slm is a foundational medical 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 multiple specialist transformer models.
This model serves as the General Physician (GP) brain of the ABIRMED ecosystem, providing broad medical knowledge, clinical reasoning foundations, and general medical understanding that supports specialized medical transformer models.
This is Version 1, with future versions planned for expanded intelligence, higher accuracy, and deeper medical specialization.
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_core_slm in the ABIRMED System
abirmed_core_slm functions as the General Medical Intelligence Layer, equivalent to a General Physician in real-world healthcare.
It provides:
- General medical knowledge
- Disease explanation capability
- Foundational clinical reasoning
- Medical education support
- Core reasoning support for specialist ABIRMED models
This model acts as the foundation upon which specialist models such as diagnosis, pharmacology, pathology, emergency, psychiatry, dermatology, cardiology, pediatrics, and veterinary models operate.
Model Details
Model Name: abirmed_core_slm
Version: 1.0
Developer: Abir Maheshwari
Organization: A.B.I.R Ecosystem
Model Type: Causal Language Model (Decoder-only Transformer)
Architecture: Custom Transformer built from scratch
Base Model: None
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 support
Training Details
Training Dataset
Primary dataset used:
lavita/MedQuAD
This dataset contains structured medical question-answer pairs covering:
- Diseases
- Symptoms
- Treatments
- Medical definitions
- Clinical explanations
This dataset was selected to provide strong foundational medical knowledge.
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 medical text sequences
Training Duration:
- Optimized for modular incremental training cycles
Capabilities
abirmed_core_slm is capable of:
- Explaining diseases
- Answering general medical questions
- Providing medical education explanations
- Understanding general clinical concepts
- Supporting medical reasoning systems
Example:
Input:
"What is diabetes?"
Output:
"Diabetes is a chronic medical condition where the body cannot properly regulate blood sugar levels due to insulin dysfunction."
Intended Use
This model is intended for:
- Medical AI research
- Educational medical assistants
- Medical chatbot development
- AI healthcare research
- Offline medical AI systems
Out-of-Scope Use
This model is not intended for:
- Clinical diagnosis
- Medical treatment decisions
- Emergency medical care
- Prescription decisions
This is a research model only.
Limitations
abirmed_core_slm:
- Is not a licensed medical system
- May produce incorrect medical information
- May lack specialist-level reasoning
- Should not replace healthcare professionals
Design Philosophy
ABIRMED follows a modular specialist architecture rather than a monolithic model.
This approach allows:
- Higher domain accuracy
- Lower hallucination rates
- Better reasoning specialization
- Efficient CPU deployment
Each model acts as a specialist doctor.
abirmed_core_slm acts as the General Physician.
A.B.I.R Ecosystem Integration
abirmed_core_slm is part of the broader A.B.I.R Ecosystem, which includes:
- Language models
- Medical AI systems
- Domain-specific transformer models
- Modular AI intelligence systems
ABIRMED represents the medical intelligence division of the A.B.I.R Ecosystem.
Version
Version: 1.0
Future versions will include:
- Improved reasoning accuracy
- Expanded datasets
- Longer context length
- Enhanced clinical intelligence
Author
Abir Maheshwari
Independent AI Researcher
Founder, A.B.I.R Ecosystem
License
MIT License
