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README.md

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+---
+language:
+- en
+- code
+tags:
+- python
+- text-generation
+- qwen
+- qlora
+- custom-finetune
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+---
+
+# Qwen2.5-Coder-1.5B-python-MyTune (Fine-tuned by Karim)
+
+## 📌 Model Description
+This model is a highly optimized, fine-tuned version of `Qwen/Qwen2.5-Coder-1.5B-Instruct`. It has been specifically trained to understand complex algorithmic instructions and generate clean, efficient, and highly accurate **Python** code. 
+
+The training architecture utilized the **QLoRA** (Quantized Low-Rank Adaptation) method. This approach ensures high parameter efficiency, allowing the model to acquire new coding skills while preserving the robust logical reasoning capabilities of the original base weights.
+
+## 📊 Training Data
+The model was fine-tuned on a carefully curated subset of the [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset. This dataset provides high-quality Python coding instructions, algorithmic challenges, and their corresponding structured solutions.
+
+## 🎯 Intended Use
+This model is designed to assist software engineers, data scientists, and quantitative analysts with:
+- Generating Python scripts from natural language prompts.
+- Solving complex algorithmic problems.
+- Writing data engineering and mathematical logic code.
+
+## ⚙️ Training Hardware
+- **Compute:** Google Colab T4 GPU (16GB VRAM)
+- **Precision:** Mixed Precision (4-bit Base + float16 Adapters)
+- **Method:** PEFT / QLoRA Integration