Update README.md

README.md

@@ -1,26 +1,40 @@
 ---
 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 library_name: peft
+license: apache-2.0
+language:
+- en
 tags:
 - code
 - python
 - lora
-- qwen
+- peft
+- qwen2
+- code-generation
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
 ---
 
 # my-python-coder
 
-A LoRA fine-tune of Qwen2.5-Coder-1.5B-Instruct specialized for Python code generation.
+A LoRA fine-tune of [Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) specialized for Python code generation.
+
+This model was fine-tuned as a learning project to demonstrate the full workflow of taking a base model, training it on a custom dataset, and publishing it to the Hugging Face Hub.
 
 ## Training Details
-- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
-- **Dataset:** iamtarun/python_code_instructions_18k_alpaca (first 1,500 examples)
-- **Method:** LoRA (r=16, alpha=32, target_modules=all-linear)
-- **Steps:** 200
-- **Hardware:** Google Colab T4
 
-## Usage
-[insert example code snippet]
+| Parameter | Value |
+|---|---|
+| **Base model** | `Qwen/Qwen2.5-Coder-1.5B-Instruct` |
+| **Dataset** | `iamtarun/python_code_instructions_18k_alpaca` (first 1,500 examples) |
+| **Method** | LoRA (r=16, alpha=32, target_modules=`all-linear`) |
+| **Training steps** | 200 |
+| **Learning rate** | 2e-4 |
+| **Effective batch size** | 8 (batch=2 × grad_accum=4) |
+| **Max sequence length** | 1024 |
+| **Hardware** | Google Colab (NVIDIA T4, 16 GB VRAM) |
+| **Training time** | ~33 minutes |
+
+## Prompt Format
 
-## Limitations
-Trained on a small subset; may not generalize to all Python tasks.
+This model was trained with the following instruction format. Using the same format at inference time will give the best results: