This repository contains Python scripts and Jupyter Notebooks related to General Artificial Intelligence (GenAI) and Large Language Models (LLM).
- Prompt: Tutorials and crash course related to Prompt Engineering.
- GenAI: General Artificial Intelligence refers to AI systems that can perform tasks across a wide range of domains, often integrating multiple modalities such as text, image, and speech.
- LLM: Large Language Models are AI models trained on vast amounts of text data to understand and generate human-like language.
- Agentic AI: Agentic AI is a type of artificial intelligence (AI) that can make decisions and take actions independently. It can also adapt to new information and collaborate with humans to solve problems.
- Mistral AI: Frontier AI in your hands.
- Langchain: LangChain is a composable framework to build with LLMs. https://www.langchain.com
- RAG: Retrieval-augmented generation (RAG) combines LLMs with external knowledge bases to improve their outputs.
- Gemini: Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI. https://gemini.google.com/
The repository includes:
- Python scripts (
*.py) for various tasks related to GenAI and LLM. - Jupyter Notebooks (
*.ipynb) demonstrating applications, experiments, and tutorials. - Additional resources and datasets used in the scripts and notebooks.
- https://ai.google.dev/gemma#models | https://ai.google.dev/gemini-api | https://openai.com/api/ | https://huggingface.co/models
Clone the repository and explore the files using your preferred Python environment or Jupyter Notebook application. Run the scripts and notebooks to experiment with GenAI and LLM functionalities.
Ensure you have Python installed along with the necessary libraries specified in the individual scripts and notebooks. Consider using a virtual environment to manage dependencies.
Include any license information or terms of use for the repository content.
Feel free to contribute by submitting pull requests or opening issues for bug fixes, feature requests, or improvements.
For questions, feedback, or collaborations, contact Chapagain Anish.
Happy coding!