初始化项目，由ModelHub XC社区提供模型

Model: CBrootA/Qwen-MediCare-BD
Source: Original Platform

README.md

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+---
+language:
+- en
+- bn
+license: mit
+library_name: transformers
+tags:
+- medical
+- bangladesh
+- qwen
+- gguf
+- healthcare
+- offline
+- mobile
+pipeline_tag: text-generation
+model-index:
+- name: ShasthoGPT-BD-3B
+  results: []
+datasets:
+- Mahadih534/all-Bangladeshi-medicines
+- GBaker/MedQA-USMLE-4-options
+base_model:
+- 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
+```python
+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)