https://github.com/kaifcoder/stan
Develop and deploy a Large Language Model (LLM) based tool for generating human like responses to natural language inputs for network not connected over internet
https://github.com/kaifcoder/stan
chroma huggingface-transformers langchain-python llm python streamlit
Last synced: 6 months ago
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Develop and deploy a Large Language Model (LLM) based tool for generating human like responses to natural language inputs for network not connected over internet
- Host: GitHub
- URL: https://github.com/kaifcoder/stan
- Owner: kaifcoder
- Created: 2023-09-27T04:09:35.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-23T10:34:46.000Z (12 months ago)
- Last Synced: 2025-01-06T00:36:09.338Z (9 months ago)
- Topics: chroma, huggingface-transformers, langchain-python, llm, python, streamlit
- Language: Python
- Homepage:
- Size: 293 KB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# STAN - Text Analysis Tool
Stan is a powerful text analysis tool designed to summarize content and answer questions using large language models.
# Demo Video
https://github.com/kaifcoder/Stan/assets/57701861/40d051ed-a659-42c0-8f15-bef37f1b158e
## Key Features
- Summarize long texts into concise paragraphs.
- Answer questions about your text, such as 'What,' 'When,' and 'Who' questions.
- Works offline, ensuring data privacy and security.
- User-friendly interface for easy text analysis.
## How Stan Works
Stan is built upon the latest advancements in natural language processing. It utilizes a locally deployed language model to perform tasks such as summarization and question answering.
## Contact Information
For questions and support, please contact us at [contact@kaifcoder](mailto:kaifmohd2014@gmail.com).
## Team
Stan is developed and maintained by a team of dedicated software engineers and AI enthusiasts.
## Privacy
We value your privacy. Stan does not require an internet connection and does not store your data.
## Acknowledgments
We would like to acknowledge the open-source community and the developers of the underlying libraries and tools that made Stan possible.