abirmed_derm_slm — Dermatology and Skin Disease Specialist Transformer
Part of the A.B.I.R Ecosystem
abirmed_derm_slm is a specialized dermatology 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 Dermatology Specialist, designed to understand skin-related symptoms, dermatological diseases, inflammatory conditions, infections, and skin disorder reasoning patterns.
This is Version 1.0, with future versions planned for expanded dermatological datasets, improved skin disease recognition, and enhanced dermatological reasoning 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_derm_slm in the ABIRMED System
abirmed_derm_slm functions as the Dermatology Specialist, equivalent to a clinical dermatologist in real-world healthcare systems.
Its primary role is to provide dermatological reasoning capabilities including:
- Skin disease interpretation
- Dermatological symptom analysis
- Skin condition explanation
- Dermatological education support
- Skin disorder reasoning
This model works alongside other ABIRMED specialists such as diagnosis, pharmacology, pathology, emergency, psychiatry, cardiology, pediatrics, and veterinary models.
Model Details
Model Name: abirmed_derm_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 dermatological and skin disease educational datasets containing:
- Skin disease descriptions
- Dermatological condition explanations
- Inflammatory skin condition information
- Skin infection and disorder narratives
These datasets enable the model to learn relationships between skin symptoms and dermatological conditions.
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 dermatological reasoning sequences
Training Format:
Instruction → Output
Converted to:
Question → Answer format
Identity training lines were included to ensure ecosystem integration.
Capabilities
abirmed_derm_slm is capable of:
- Understanding skin-related symptoms
- Explaining dermatological diseases
- Supporting dermatology education
- Providing skin condition explanations
- Supporting dermatological research
Example:
Input: "Red itchy rash on arm"
Output: "This may indicate dermatitis, an inflammatory skin condition caused by irritation or allergic reactions."
Intended Use
This model is intended for:
- Dermatology education
- Medical AI research
- Dermatological education tools
- Healthcare chatbot development
- Skin disease research support
Out-of-Scope Use
This model is not intended for:
- Dermatological diagnosis
- Medical treatment decisions
- Clinical dermatology use
- Replacement of licensed dermatologists
This is a research model only.
Limitations
abirmed_derm_slm:
- Is not a licensed dermatology system
- May produce incomplete dermatological assessments
- Should not replace medical professionals
- May lack full dermatological 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_derm_slm serves as the dermatological intelligence specialist.
This architecture improves:
- Domain accuracy
- Reasoning reliability
- Computational efficiency
- Modular scalability
A.B.I.R Ecosystem Integration
abirmed_derm_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 dermatology datasets
- Improved dermatological reasoning accuracy
- Larger training datasets
- Enhanced dermatological intelligence
Author
Abir Maheshwari
Independent AI Researcher
Founder, A.B.I.R Ecosystem
Hugging Face:
https://huggingface.co/abirmaheshwari
License
MIT License
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