Welcome to the "Introduction-to-LLMs" repository! This comprehensive guide walks you through the basics of Large Language Models (LLMs), explores various LLMs, demonstrates how to finetune them on downstream tasks, leverage Vector DBs such as Qurant, and most importantly, deploy the finetuned model. Dive into the world of LLMs with practical examples provided in Jupyter notebooks.
- Getting Started
- Basics of LLMs
- Various LLMs
- Finetuning on Downstream Tasks
- Leveraging Vector DBs
- Model Deployment
- Notebooks
- Contributing
- License
- Acknowledgments
- Contact
Clone the repository to get started:
git clone https://github.com/Praveen76/Introduction-to-LLMs.git
cd Introduction-to-LLMsExplore the various sections and follow the step-by-step guides to enhance your understanding of LLMs.
Understand the fundamental concepts of Large Language Models, including architecture, training, and their applications in natural language processing.
Explore a variety of Large Language Models, comparing their strengths, weaknesses, and use cases. Gain insights into the latest advancements in the field.
Learn how to finetune LLMs on specific downstream tasks to tailor them to your specific requirements. Follow practical examples and best practices.
Discover the power of Vector Databases, with a focus on Qurant. Understand how to integrate and utilize these databases to enhance the capabilities of your models.
Master the art of deploying finetuned models into production environments. Explore deployment strategies, considerations, and practical tips.
Find detailed implementations and solutions in the notebooks folder:
- part-1-openai.ipynb
- part-2-prompt-enginering.ipynb
- part-3-langchain.ipynb
- part-4-rag.ipynb
- part-5-finetune.ipynb
Explore these Jupyter notebooks for hands-on examples and practical demonstrations.
If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.
Please adhere to our Code of Conduct in all your interactions with the project.
This project is licensed under the MIT License.
For questions or inquiries, feel free to contact me on Linkedin.
I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.