Update README.md

README.md

@@ -1,3 +1,4 @@
+
 ---
 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 library_name: peft
@@ -39,6 +40,13 @@ This model was fine-tuned as a learning project to demonstrate the full workflow
 
 This model was trained with the following instruction format. Using the same format at inference time will give the best results:
 
+```
+### Instruction:
+<your task description>
+
+### Response:
+<model's answer>
+```
 
 ## Usage
 
@@ -66,11 +74,20 @@ Write a Python function that checks if a number is prime.
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
+## Example Output
+
+**Prompt:**
+```
 ### Instruction:
 Write a Python function that checks if a number is prime.
 
 ### Response:
+```
+
+**Model output:**
+```python
 def is_prime(num):
     # Check for 0 and 1
     if num <= 1:
@@ -88,3 +105,24 @@ def is_prime(num):
             if num % i == 0:
                 return False
         return True
+```
+
+## Limitations
+
+- Trained on a **small subset** (1,500 of 18,612 examples) for only 200 steps — this is a proof-of-concept, not a production model.
+- May not generalize well to complex Python tasks (large refactors, multi-file projects, advanced libraries).
+- Inherits any biases or limitations present in the base model and training dataset.
+- Not evaluated against standard benchmarks.
+
+## Future Improvements
+
+- Train on the full dataset for multiple epochs
+- Increase LoRA rank for greater capacity
+- Evaluate on HumanEval or MBPP benchmarks
+
+## Acknowledgements
+
+- Base model: [Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) by the Qwen team
+- Dataset: [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
+- Training framework: Hugging Face `transformers`, `peft`, `trl`
+```