language, license, tags, pipeline_tag, base_model, widget

language	license	tags	pipeline_tag	base_model	widget
en ur	apache-2.0	medical clinical icd-10 billing mlx	text-generation	Qwen/Qwen2.5-0.5B-Instruct	text example_title <system>You are the Cognitapp Global ICD-10 Assistant. Extract the primary ICD-10 code.</system> <user>Patient with severe high fever, joint pain, and suspected Dengue from Lahore.</user> <assistant> Dengue Case (Regional) text example_title <system>You are the Cognitapp Global ICD-10 Assistant. Extract the primary ICD-10 code.</system> <user>65yo male smoker with chronic cough and SOB. Spirometry shows FEV1/FVC < 0.70.</user> <assistant> COPD Case (Global)

Cognitapp-Med-Nano-v1

Cognitapp-Med-Nano-v1 is a specialized, lightweight medical large language model (LLM) developed by Cognitapp Labs. It is fine-tuned from the Qwen2.5-0.5B architecture to excel at ICD-10-CM Medical Billing and Clinical Extraction.

Key Features

• Global & Regional Awareness: Optimized for both international clinical standards.
• Efficiency: 0.5B parameters, designed for 100% offline use on mobile and desktop devices via MLX or llama.cpp.
• Precision: Trained using prompt-masking to prioritize alphanumeric code accuracy over conversational filler.

How to use with MLX

from mlx_lm import load, generate

model, tokenizer = load("Cognitapp/Cognitapp-Med-Nano-v1")
prompt = "<system>You are the Cognitapp Global ICD-10 Assistant. Extract the primary ICD-10 code.</system> <user>Patient has 103F fever, body aches, and positive NS1 for Dengue.</user> <assistant>"
response = generate(model, tokenizer, prompt=prompt, max_tokens=10)
print(response)

Intended Use

This model is a supportive tool for medical professionals and billers. It is NOT a diagnostic tool.

Training Data

Fine-tuned on a balanced dataset of 1,200+ global and regional clinical scenarios including pediatrics, geriatrics, and infectious diseases.

Disclaimer

All outputs must be verified by a licensed healthcare professional. Cognitapp Labs is not responsible for any clinical or billing errors.