Update readme with ordering of sections

README.md

@@ -25,32 +25,6 @@ This model is a fine-tuned version of Google's Gemma-2B Instruction model, optim
 - **Repository:** Hugging Face Hub
 - **Base Model:** google/gemma-2b-it
 
-## Uses
-
-### Direct Use
-This model is designed for generating Telugu news headlines from article content. It can be used by:
-- News organizations for automated headline generation
-- Content creators working with Telugu news content
-- Researchers studying Telugu natural language generation
-
-### Out-of-Scope Use
-- The model should not be used for generating fake news or misleading headlines
-- Not suitable for non-Telugu content
-- Not designed for general text generation tasks
-- Should not be used for classification or other non-headline generation tasks
-
-## Bias, Risks, and Limitations
-- May reflect biases present in Telugu news media
-- Performance may vary based on news domain and writing style
-- Limited to the vocabulary and patterns present in the training data
-- May occasionally generate grammatically incorrect Telugu text
-- Could potentially generate sensationalized headlines
-
-### Recommendations
-- Use with human oversight for published content
-- Verify generated headlines for accuracy
-- Monitor output for potential biases
-- Implement content filtering for inappropriate generations
 
 ## How to Get Started with the Model
 
@@ -227,4 +201,31 @@ model = AutoModelForCausalLM.from_pretrained(
 - PyTorch
 - Transformers library
 - TRL for supervised fine-tuning
-- CUDA for GPU acceleration
+- CUDA for GPU acceleration
+
+## Uses
+
+### Direct Use
+This model is designed for generating Telugu news headlines from article content. It can be used by:
+- News organizations for automated headline generation
+- Content creators working with Telugu news content
+- Researchers studying Telugu natural language generation
+
+### Out-of-Scope Use
+- The model should not be used for generating fake news or misleading headlines
+- Not suitable for non-Telugu content
+- Not designed for general text generation tasks
+- Should not be used for classification or other non-headline generation tasks
+
+## Bias, Risks, and Limitations
+- May reflect biases present in Telugu news media
+- Performance may vary based on news domain and writing style
+- Limited to the vocabulary and patterns present in the training data
+- May occasionally generate grammatically incorrect Telugu text
+- Could potentially generate sensationalized headlines
+
+### Recommendations
+- Use with human oversight for published content
+- Verify generated headlines for accuracy
+- Monitor output for potential biases
+- Implement content filtering for inappropriate generations