Update README.md

README.md

@@ -10,10 +10,8 @@ pipeline_tag: reinforcement-learning
 tags:
 - text-to-sql
 ---
-
-        ---
-        library: gemma3-text-to-sql
-        ---
+library: gemma3-text-to-sql
+---
 # Gemma 3 Text-to-SQL
 
 A powerful LoRA-fine-tuned adapter for Gemma 3 that converts natural language questions into SQL queries with high accuracy and contextual understanding.
@@ -58,7 +56,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
 # Load adapter
-adapter_path = "your-username/gemma-3-text-to-sql"  # Replace with your HF model path
+adapter_path = "parole-study-viper/gemma-3-text-to-sql"  # Replace with your HF model path
 model = PeftModel.from_pretrained(model, adapter_path)
 
 # Format prompt
@@ -87,7 +85,7 @@ from mlx_lm.utils import get_model_path
 
 # Setup paths
 model_path = "lmstudio-community/gemma-3-27b-it-GGUF"
-adapter_path = "your-username/gemma-3-text-to-sql/adapter_model.safetensors"
+adapter_path = "parole-study-viper/gemma-3-text-to-sql/adapter_model.safetensors"
 
 # Run generation
 prompt = "Convert the following natural language query to SQL: Find all customers in New York"
@@ -108,7 +106,7 @@ You can also use the Hugging Face Inference API:
 ```python
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/your-username/gemma-3-text-to-sql"
+API_URL = "https://api-inference.huggingface.co/models/parole-study-viper/gemma-3-text-to-sql"
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
 def query(payload):
@@ -214,11 +212,11 @@ If you use this model in your research or applications, please cite:
 
 ```bibtex
 @misc{gemma3-text-to-sql,
-  author = {Your Name},
+  author = {parole-study-viper},
   title = {Gemma 3 Text-to-SQL: A LoRA-fine-tuned adapter for natural language to SQL conversion},
   year = {2025},
   publisher = {HuggingFace},
-  howpublished = {\url{https://huggingface.co/your-username/gemma-3-text-to-sql}}
+  howpublished = {\url{https://huggingface.co/parole-study-viper/gemma-3-text-to-sql}}
 }
 ```
 
@@ -235,4 +233,4 @@ We thank Google for releasing the Gemma 3 models and the Hugging Face team for t
 If you find any issues or have suggestions for improvement, please open an issue on the GitHub repository or reach out on the Hugging Face community forums.
 
 
-        This model created by [@parole-study-viper]
+This model created by [@parole-study-viper]