Update README.md

README.md

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----
-license: apache-2.0
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+---
+language: en
+license: apache-2.0
+tags:
+- fashion
+- ecommerce
+- product-description
+- roman-urdu
+- watches
+- opalhours
+datasets:
+- nvidia/Llama-Nemotron-Post-Training-Dataset
+metrics:
+- accuracy
+base_model:
+- MuzammilKhosa/opalhours-ai
+new_version: deepseek-ai/DeepSeek-R1
+pipeline_tag: text-classification
+library_name: adapter-transformers
+---
+
+# OpalHours AI
+
+**OpalHours AI** is a lightweight language model designed to assist with watch-related e-commerce content. It helps generate product descriptions, respond to customer queries, and maintain a consistent brand tone—especially for businesses communicating in both English and Roman Urdu.
+
+## Model Capabilities
+
+- Create elegant product descriptions for wristwatches  
+- Generate quick replies for customer messages (WhatsApp-style)  
+- Suggest taglines, captions, and headlines  
+- Supports basic Roman Urdu generation (e.g., "yeh watch classy aur modern lagti hai")
+
+## Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("opalhours/opalhours-ai")
+model = AutoModelForCausalLM.from_pretrained("opalhours/opalhours-ai")
+
+prompt = "Describe a minimal silver dial men's watch with a black leather strap."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))