70 lines
1.9 KiB
Markdown
70 lines
1.9 KiB
Markdown
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---
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language:
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- en
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- bn
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license: mit
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library_name: transformers
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tags:
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- medical
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- bangladesh
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- qwen
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- gguf
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- healthcare
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- offline
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- mobile
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pipeline_tag: text-generation
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model-index:
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- name: ShasthoGPT-BD-3B
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results: []
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datasets:
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- Mahadih534/all-Bangladeshi-medicines
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- GBaker/MedQA-USMLE-4-options
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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---
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# 🏥 Qwen-MediCare-BD
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**Bangladesh's First Offline Medical AI Assistant**
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## Model Description
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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.
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### Key Features
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- 🇧🇩 **Bangladesh-specific**: Includes local diseases, drugs, and medical context
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- 📱 **Mobile-ready**: Quantized to Q4_K_M (1.8GB)
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- 🔒 **100% Offline**: No internet required for inference
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- 🩺 **Medically validated**: Trained on 30,523 medical Q&A pairs
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- 💚 **Multilingual**: Supports English and Bangla queries
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## Model Variants
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| Variant | Size | Format | Use Case |
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|---------|------|--------|----------|
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| Full Model | 6.2 GB | Safetensors | Training/Research |
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| Q4_K_M | 1.8 GB | GGUF | Mobile/Edge devices |
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| LoRA Adapters | 114 MB | Safetensors | Fine-tuning |
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## Quick Start
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### Using Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"CBrootA/Qwen-MediCare-BD",
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device_map="auto",
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load_in_4bit=True
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)
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tokenizer = AutoTokenizer.from_pretrained("CBrootA/Qwen-MediCare-BD")
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messages = [
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{"role": "system", "content": "You are a medical assistant for Bangladesh."},
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{"role": "user", "content": "What are dengue symptoms?"}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
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outputs = model.generate(inputs, max_new_tokens=256)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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