Update README.md

README.md

@@ -22,18 +22,6 @@ datasets:
 - **Task**: Text generation with a focus on "brainrot" content (humorous, absurd, or nonsensical text).
 - **Fine-Tuning Dataset Size**: 32 rows (small dataset for experimental purposes).
 
-### Intended Use
-This model is intended for experimental and entertainment purposes. It is fine-tuned on a small dataset of "brainrot" content. Use cases include:
-- Generating funny or absurd text for entertainment.
-- Exploring the effects of fine-tuning on small, niche datasets.
-- Testing the limits of language models with minimal data.
-
-### Limitations
-- **Overfitting**: Due to the extremely small dataset (32 rows), the model may have overfitted to the training data, leading to poor generalization on unseen data.
-- **Validation Loss**: The validation loss increased during training, indicating potential overfitting or lack of generalization.
-- **Niche Use Case**: The model is specialized for "brainrot" content and may not perform well on general text generation tasks.
-- **Ethical Considerations**: The model may generate nonsensical or inappropriate content. Use with caution and ensure outputs are reviewed before sharing.
-
 ## Quick start
 
 ```python
@@ -76,6 +64,18 @@ response = full_response.split("assistant\n")[-1].strip()
 print(response)
 ```
 
+### Intended Use
+This model is intended for experimental and entertainment purposes. It is fine-tuned on a small dataset of "brainrot" content. Use cases include:
+- Generating funny or absurd text for entertainment.
+- Exploring the effects of fine-tuning on small, niche datasets.
+- Testing the limits of language models with minimal data.
+
+### Limitations
+- **Overfitting**: Due to the extremely small dataset (32 rows), the model may have overfitted to the training data, leading to poor generalization on unseen data.
+- **Validation Loss**: The validation loss increased during training, indicating potential overfitting or lack of generalization.
+- **Niche Use Case**: The model is specialized for "brainrot" content and may not perform well on general text generation tasks.
+- **Ethical Considerations**: The model may generate nonsensical or inappropriate content. Use with caution and ensure outputs are reviewed before sharing.
+
 ## Training procedure
 
 This model was trained with SFT.