Update README.md

README.md

@@ -8,25 +8,6 @@ tags:
 - peft
 - lora
 ---
-
-# BERT-QA-SQuAD2 LoRA Adapter
-
-A **Parameter-Efficient (LoRA)** adapter for `bert-base-uncased`, fine-tuned on **SQuAD 2.0** for extractive question answering. Optimized for low-rank adaptation (LoRA) to reduce memory usage while preserving performance.
-
-## Model Details
-
-### Model Description
-- **Developed by:** [Your Name/Organization]
-- **Model type:** PEFT (LoRA) adapter for BERT
-- **Language(s):** English
-- **License:** Apache 2.0
-- **Finetuned from:** `bert-base-uncased`
-- **Adapter Size:** ~3MB (vs. ~440MB for full BERT)
-
-### Model Sources
-- **Repository:** [GitHub link if applicable]
-- **Demo:** [Hugging Face Spaces link if available]
-
 ## Uses
 
 ### Direct Use
@@ -48,4 +29,24 @@ qa_pipeline = pipeline("question-answering", model=merged_model, tokenizer=token
 
 context = "Hugging Face is based in New York City."
 question = "Where is Hugging Face located?"
-result = qa_pipeline(question=question, context=context)  # Output: {'answer': 'New York City', ...}
+result = qa_pipeline(question=question, context=context)  # Output: {'answer': 'New York City', ...}
+
+```
+# BERT-QA-SQuAD2 LoRA Adapter
+
+A **Parameter-Efficient (LoRA)** adapter for `bert-base-uncased`, fine-tuned on **SQuAD 2.0** for extractive question answering. Optimized for low-rank adaptation (LoRA) to reduce memory usage while preserving performance.
+
+## Model Details
+
+### Model Description
+- **Developed by:** [Your Name/Organization]
+- **Model type:** PEFT (LoRA) adapter for BERT
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from:** `bert-base-uncased`
+- **Adapter Size:** ~3MB (vs. ~440MB for full BERT)
+
+### Model Sources
+- **Repository:** [GitHub link if applicable]
+- **Demo:** [Hugging Face Spaces link if available]
+