This project showcases the implementation and usage of a custom Large Language Model (LLM) built to explore advanced NLP tasks. The notebook includes workflows for training, fine-tuning, and integrating LLMs with state-of-the-art tools like Hugging Face, FAISS, and LangChain.
- Implementation of NLP tasks like text generation, summarization, and semantic search.
- Fine-tuning LLMs using pre-trained transformer models.
- Vector search integration for efficient similarity matching using FAISS.
- Interactive coding and visualization in Jupyter Notebook.
- Flexible, modular design to adapt to different use cases.
- Python: Core programming language for all implementations.
- Jupyter Notebook: Interactive development and visualization environment.
- Hugging Face Transformers: Pre-trained model utilization and fine-tuning.
- FAISS: High-performance vector similarity search.
- LangChain: To structure workflows and applications using LLMs.
- PyTorch / TensorFlow: For building and customizing models.
- Docker: Optional containerization for deployment and reproducibility.
Before running the project, ensure you have:
- Python 3.8 or later.
- Jupyter Notebook installed.
- Required dependencies listed in the
requirements.txtfile.
- Clone this repository to your local machine:
git clone https://github.com/yourusername/custom-llm.git cd custom-llm - Install dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook sheesikramCustomLLM.ipynb
- Follow the provided instructions to train, fine-tune, or deploy the LLM.
- Example use cases:
- Text Generation: Experiment with creative text generation tasks.
- Summarization: Generate concise summaries from large datasets.
- Semantic Search: Build a similarity search tool using vectorized embeddings.
sheesikramCustomLLM.ipynb: Main Jupyter Notebook containing all project code and workflows.README.md: This documentation file.requirements.txt: List of dependencies required to run the project.- Additional files or directories, if applicable:
data/: Folder for training datasets.models/: Pre-trained or fine-tuned model storage.scripts/: Any helper scripts used.
Contributions are welcome! Feel free to:
- Fork this repository.
- Open an issue for bug reports or feature requests.
- Submit pull requests to improve the project.
This project is licensed under the MIT License. See the LICENSE file for more details.
Developed by Shees Ikram