Update README.md

README.md

@@ -1,5 +1,56 @@
----
-license: mit
-tags:
-- unsloth
----
+---
+license: mit
+tags:
+- medical
+- dental
+- lora
+- qwen
+- instruction-tuning
+- unsloth
+- adapter
+- peft
+- transformers
+---
+
+# 🦷 doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct
+
+This is a **LoRA adapter** fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) on a domain-specific dataset that combines:
+
+- **Realistic doctor–patient conversations**  
+- **Dental implant Q&A** extracted from Straumann® technical manuals
+
+> 🔬 Designed to make Qwen2.5-7B-Instruct capable of answering both general health questions and dental-specific scenarios.
+
+---
+
+## 🧠 Base Model
+
+- **Base**: [`Qwen/Qwen2.5-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
+- **Adapter**: LoRA (PEFT-based)
+
+This repo contains only the **LoRA adapter weights**, not the full model.
+
+---
+
+## 🗂 Files
+
+| File                    | Purpose                            |
+|-------------------------|-------------------------------------|
+| `adapter_model.safetensors` | LoRA weight file (for PEFT loading) |
+| `adapter_config.json`   | LoRA hyperparameter configuration |
+| `tokenizer.json`, `vocab.json`, `merges.txt` | Tokenizer (shared with base model) |
+
+---
+
+## 📦 How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model
+base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
+
+# Load adapter
+model = PeftModel.from_pretrained(base, "BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct")