A structured workspace for hands-on learning and experimentation with Generative AI models, techniques, and frameworks.
This repository serves as a comprehensive learning platform for exploring various aspects of Generative AI. It provides a structured approach to understanding, implementing, and experimenting with different generative models - from large language models to diffusion-based image generators. Each project is organized in dedicated branches, allowing focused learning while maintaining a clean workspace.
- Main Branch: Acts as an index and navigation hub, providing an overview of all projects
- Model-Specific Branches: Each branch corresponds to a particular generative AI technique or model implementation
- Isolated Environments: Every project has its own virtual environment and dependency management to prevent conflicts
# Clone the repository
git clone https://github.com/yourusername/Generative-AI.git
cd Generative-AI
# List all available branches
git branch -a
# Switch to a specific project branch
git checkout <branch-name>
cd <branch-name>This repository follows a structured learning path across different branches:
- Foundation Models: Understanding base architectures and capabilities
- Fine-Tuning & Adaptation: Customizing models for specific tasks and domains
- Optimization & Inference: Running models efficiently with various techniques
- Multimodal Integration: Combining text, image, and audio generation
- Production Deployment: Moving from experimentation to usable applications
| Branch | Description | Key Technologies |
|---|---|---|
| Initial_Test | Implementation of DeepSeek-Prover-V2-671B using Hugging Face for efficient chatbot experiences | LangChain |
| simple_story_generator | An simple story generator | LangChain |
For beginners, we recommend following this sequence:
- Start with the
Initial_Testbranch to understand basic LLM inference
Each branch includes sample notebooks and scripts with detailed comments to guide your learning.
This project is licensed under the MIT License