Update README.md

README.md

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----
-license: apache-2.0
-tags:
-- unsloth
----
+# Fine-Tuning Qwen2.5-3B-Instruct with GRPO for GSM8K Dataset
+
+This notebook demonstrates the process of fine-tuning the **Qwen2.5-3B-Instruct** model using **GRPO (Generalized Reward Policy Optimization)** on the **GSM8K** dataset. The goal is to improve the model's ability to solve mathematical reasoning problems by leveraging reinforcement learning with custom reward functions.
+
+## Overview
+
+The notebook is structured as follows:
+
+1. **Installation**: Installs necessary libraries such as `unsloth`, `vllm`, and `trl` for efficient fine-tuning and inference.
+2. **Unsloth Setup**: Configures the environment for faster fine-tuning using Unsloth's `PatchFastRL` and loads the Qwen2.5-3B-Instruct model with LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning.
+3. **Data Preparation**: Loads and preprocesses the GSM8K dataset, formatting it for training with a system prompt and XML-style reasoning and answer format.
+4. **Reward Functions**: Defines custom reward functions to evaluate the model's responses, including:
+   - **Correctness Reward**: Checks if the extracted answer matches the ground truth.
+   - **Format Reward**: Ensures the response follows the specified XML format.
+   - **Integer Reward**: Verifies if the extracted answer is an integer.
+   - **XML Count Reward**: Evaluates the completeness of the XML structure in the response.
+5. **GRPO Training**: Configures and runs the GRPO trainer with vLLM for fast inference, using the defined reward functions to optimize the model's performance.
+6. **Training Progress**: Monitors the training progress, including rewards, completion length, and KL divergence, to ensure the model is improving over time.
+
+## Key Features
+
+- **Efficient Fine-Tuning**: Utilizes Unsloth and LoRA to fine-tune the model with reduced memory usage and faster training times.
+- **Custom Reward Functions**: Implements multiple reward functions to guide the model towards generating correct and well-formatted responses.
+- **vLLM Integration**: Leverages vLLM for fast inference during training, enabling efficient generation of multiple responses for reward calculation.
+- **GSM8K Dataset**: Focuses on improving the model's performance on mathematical reasoning tasks, specifically the GSM8K dataset.
+
+## Requirements
+
+- Python 3.11
+- Libraries: `unsloth`, `vllm`, `trl`, `torch`, `transformers`
+
+## Installation
+
+To set up the environment, run:
+
+```bash
+pip install unsloth vllm trl
+```
+
+## Usage
+- **Load the Model**: The notebook loads the Qwen2.5-3B-Instruct model with LoRA for fine-tuning.
+
+- **Prepare the Dataset**: The GSM8K dataset is loaded and formatted with a system prompt and XML-style reasoning and answer format.
+
+- **Define Reward Functions**: Custom reward functions are defined to evaluate the model's responses.
+
+- **Train the Model**: The GRPO trainer is configured and run to fine-tune the model using the defined reward functions.
+
+- **Monitor Progress**: The training progress is monitored, including rewards, completion length, and KL divergence.
+
+## Results
+- The training process is designed to improve the model's ability to generate correct and well-formatted responses to mathematical reasoning problems. The reward functions guide the model towards better performance, and the training progress is logged for analysis.
+
+## Future Work
+- **Hyperparameter Tuning**: Experiment with different learning rates, batch sizes, and reward weights to optimize performance.
+
+- **Additional Datasets**: Extend the fine-tuning process to other datasets to improve the model's generalization capabilities.
+
+- **Advanced Reward Functions**: Implement more sophisticated reward functions to further refine the model's responses.
+
+## Acknowledgments
+- **Unsloth**: For providing tools to speed up fine-tuning.
+
+- **vLLM**: For enabling fast inference during training.
+
+- **Hugging Face**: For the trl library and the GSM8K dataset.
+
+- Special thanks to @sudhir2016 sir for mentoring me for developing such a prominent fine-tuning model.
+
+## License
+This project is licensed under the MIT License. See the LICENSE file for details.