Update README.md

README.md

@@ -1,4 +1,4 @@
-
+---
 license: apache-2.0
 tags:
   - peft
@@ -48,48 +48,53 @@ prompt = "Q: Julie read 12 pages yesterday and twice as many today. If she wants
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=100)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## 📊 Evaluation Results
 
+| Task        | Metric                      | Score  | Samples |
+|-------------|-----------------------------|--------|---------|
+| GSM8K       | Exact Match (strict)        | 54.6%  | 500     |
+| ARC-Easy    | Accuracy                    | 79.0%  | 500     |
+| HellaSwag   | Accuracy (Normalized)       | 61.0%  | 500     |
 
-⸻
+> Benchmarks were run using [EleutherAI’s lm-eval-harness](https://github.com/EleutherAI/lm-eval-harness)
 
-📊 Evaluation Results
+---
 
-Task	Metric	Score	Samples
-GSM8K	Exact Match (strict)	54.6%	500
-ARC-Easy	Accuracy	79.0%	500
-HellaSwag	Accuracy (Normalized)	61.0%	500
+## ⚙️ Training Details
 
-Benchmarks were run using EleutherAI’s lm-eval-harness
+- **Method**: LoRA (rank=8, alpha=16, dropout=0.1)
+- **Epochs**: 1 (proof of concept)
+- **Batch size**: 4 per device
+- **Precision**: FP16
+- **Platform**: Google Colab (T4 GPU)
+- **Framework**: [🤗 Transformers](https://github.com/huggingface/transformers) + [PEFT](https://github.com/huggingface/peft)
 
-⸻
+---
 
-⚙️ Training Details
-	•	Method: LoRA (rank=8, alpha=16, dropout=0.1)
-	•	Epochs: 1 (proof of concept)
-	•	Batch size: 4 per device
-	•	Precision: FP16
-	•	Platform: Google Colab (T4 GPU)
-	•	Framework: 🤗 Transformers + PEFT
+## 🔍 Limitations
 
-⸻
+- Fine-tuned for math problems only (not general-purpose reasoning)
+- Trained for 1 epoch — additional training may improve performance
+- Adapter-only: base model (`microsoft/phi-2`) must be loaded alongside
 
-🔍 Limitations
-	•	Fine-tuned for math problems only (not general-purpose reasoning)
-	•	Trained for 1 epoch — additional training may improve performance
-	•	Adapter-only: base model (microsoft/phi-2) must be loaded alongside
+---
 
-⸻
+## 📘 Citation & References
 
-📘 Citation & References
-	•	LoRA: Low-Rank Adaptation
-	•	Phi-2 Model Card
-	•	GSM8K Dataset
-	•	PEFT Library
-	•	Transformers
+- [LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)
+- [Phi-2 Model Card](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/)
+- [GSM8K Dataset](https://huggingface.co/datasets/gsm8k)
+- [PEFT Library](https://github.com/huggingface/peft)
+- [Transformers](https://huggingface.co/docs/transformers)
+
+---
 
-⸻
+## 💬 Author
 
-💬 Author
+This model was fine-tuned and open-sourced by **[Darsh Joshi](https://huggingface.co/darshjoshi16)**.  
+Feel free to [reach out](mailto:contact@darshjoshi.com) or contribute.
 
-This model was fine-tuned and open-sourced by Darsh Joshi (contact@darshjoshi.com).
-Feel free to reach out or contribute.