https://github.com/engrzulqarnain/instructions-dataset-generator-for-llm-fine-tuning
https://github.com/engrzulqarnain/instructions-dataset-generator-for-llm-fine-tuning
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/engrzulqarnain/instructions-dataset-generator-for-llm-fine-tuning
- Owner: ENGRZULQARNAIN
- Created: 2024-04-20T20:34:48.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-20T20:43:15.000Z (about 1 year ago)
- Last Synced: 2024-04-20T21:32:55.263Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Instructions Dataset Generator for LLM Fine-Tuning
### Overview
This repository contains the tools and scripts necessary to create a custom instructions dataset for fine-tuning large language models (LLMs). Leveraging the capabilities of Google Gemini and LangChain, our solution is designed to cater specifically to your unique use cases. The primary goal of this project is to streamline the development of tailored instruction sets that enhance LLM performance in specific tasks or domains.
### Features
- Data Collection: Scripts to automate the gathering of relevant data from specified sources.
- Data Processing: Utilities for cleaning and preprocessing the collected data to ensure high-quality datasets.
- Instruction Generation: Implementation of a robust framework using Google Gemini for generating precise and contextually appropriate instructions.
- Integration with LangChain: Utilize LangChain to seamlessly incorporate generated instructions into LLM workflows, enabling efficient fine-tuning processes.
### Getting Started
just open .ipynb notebook and run each cell
```
### Contributing
We welcome contributions to this project! Whether it's refining the scripts, expanding the documentation, or adding new features, your help would be greatly appreciated. Please read through our contribution guidelines for more information on how to submit pull requests.
### License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
### Contact
For any questions or issues, please open an issue on the GitHub repository or contact us directly through our provided channels.