L M S Y S_ Gemma2_ Q Lo R A_ft

Introduction
The LMSYS_Gemma2_QLoRA_ft is a trained model designed for the LMSYS competition hosted on Kaggle. This model is based on Google's Gemma-2-9b model and has been fine-tuned using the QLoRA technique to enhance performance for specific tasks related to chatbot functionality.

Architecture
The model utilizes the Google Gemma-2 architecture, which is a 9 billion parameter transformer model. This provides a robust foundation for fine-tuning and adapting to specific use cases, such as those required by the LMSYS competition.

Training
The model was specifically trained for the LMSYS competition on Kaggle. This involved applying the QLoRA technique to the base Gemma-2-9b model, optimizing it for improved task-specific performance, particularly in the domain of chatbots.

Guide: Running Locally

1. Clone the Repository: Start by cloning the LMSYS_Gemma2_QLoRA_ft repository from Hugging Face.
2. Install Dependencies: Ensure all necessary libraries and dependencies are installed. This typically includes Hugging Face's Transformers library and PyTorch.
3. Load the Model: Use the Transformers library to load the Gemma-2 model with the fine-tuned weights from LMSYS_Gemma2_QLoRA_ft.
4. Run Inference: Implement a script to perform inference using the model on your data.
5. Suggest Cloud GPUs: For enhanced performance and efficiency, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License
The LMSYS_Gemma2_QLoRA_ft model is released under the MIT License. This allows for flexibility in use, distribution, and modification, provided that appropriate credit is given to the original creator.