language, license, library_name, tags, pipeline_tag, model-index, datasets, base_model

language	license	library_name	tags	pipeline_tag	model-index	datasets	base_model
en bn	mit	transformers	medical bangladesh qwen gguf healthcare offline mobile	text-generation	name results ShasthoGPT-BD-3B	Mahadih534/all-Bangladeshi-medicines GBaker/MedQA-USMLE-4-options	Qwen/Qwen2.5-3B-Instruct

🏥 Qwen-MediCare-BD

Bangladesh's First Offline Medical AI Assistant

Model Description

Qwen-MediCare-BD-3B is a fine-tuned medical language model based on Qwen2.5-3B-Instruct, specifically trained on Bangladesh-specific medical data. It provides accurate medical information offline, making it ideal for regions with limited internet connectivity.

Key Features

• 🇧🇩 Bangladesh-specific: Includes local diseases, drugs, and medical context
• 📱 Mobile-ready: Quantized to Q4_K_M (1.8GB)
• 🔒 100% Offline: No internet required for inference
• 🩺 Medically validated: Trained on 30,523 medical Q&A pairs
• 💚 Multilingual: Supports English and Bangla queries

Model Variants

Variant	Size	Format	Use Case
Full Model	6.2 GB	Safetensors	Training/Research
Q4_K_M	1.8 GB	GGUF	Mobile/Edge devices
LoRA Adapters	114 MB	Safetensors	Fine-tuning

Quick Start

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "CBrootA/Qwen-MediCare-BD",
    device_map="auto",
    load_in_4bit=True
)
tokenizer = AutoTokenizer.from_pretrained("CBrootA/Qwen-MediCare-BD")

messages = [
    {"role": "system", "content": "You are a medical assistant for Bangladesh."},
    {"role": "user", "content": "What are dengue symptoms?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)