https://github.com/uditakhourii/rtrafactor
Rtrafactor - LLMs on Steroids
https://github.com/uditakhourii/rtrafactor
ai llm llm-training machine-learning machine-learning-library
Last synced: about 2 months ago
JSON representation
Rtrafactor - LLMs on Steroids
- Host: GitHub
- URL: https://github.com/uditakhourii/rtrafactor
- Owner: UditAkhourii
- License: apache-2.0
- Created: 2024-04-13T05:41:05.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T17:36:26.000Z (about 2 years ago)
- Last Synced: 2025-10-30T23:53:20.831Z (8 months ago)
- Topics: ai, llm, llm-training, machine-learning, machine-learning-library
- Language: Python
- Homepage: https://hyperllm.org
- Size: 21.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Rtrafactor - LLMs on Steroids
Welcome to Eternity AI's Rtrafactor workspace!
[](https://colab.research.google.com/drive/1nzJou9tONTF6AOKqJTZc8--rIwI38JuR?usp=sharing)
## Overview
Rtrafactor is a Python library developed at the Indian Institute of Technology, Patna, as part of the Real Time Retrieval Argumentation (RTRA) architecture. It integrates real-time access tools like search, retrieval, summarization, and argumentation into AI models via a Python library.
To read the research paper detailing RTRA, click [here](https://eternityai.tech).
## Installation
Install Rtrafactor into your project using pip:
```bash
pip install rtrafactor
```
## Package Validation
Ensure the authenticity of the installed package:
```bash
pip show rtrafactor
```
## Usage
Rtrafactor is available as a L2LM (Language-to-Language Model) architecture, currently integrated with HuggingFace's Inference API. Follow these steps to use Rtrafactor:
1. **Import RTRAConnector**:
```python
from rtrafactor import RTRAConnector
```
2. **Instantiate RTRAConnector**:
```python
connector = RTRAConnector(huggingface_model, huggingface_api_token)
```
3. **Query for Answers**:
```python
query = "Your question here?"
one_shot_answer = connector.compare_answers(query)
print(one_shot_answer)
```
## Examples
Here are some example queries you can try with Rtrafactor:
1. **Why is Delhi's CM in jail?**
2. **Who is Dr. Kuldip Singh Patel?**
3. **Who is Udit Akhouri?**
## Limitations & Future Scope
While RTRA enhances model capabilities, there are still limitations to address:
- **Hallucination & Citation**: Addressing false information and improving citation accuracy.
- **Latency**: Reducing response time and optimizing underlying architecture.
## Breakthrough
RTRA architecture challenges the conventional approach of relying on vast computational resources by democratizing access to the internet's information. It enables developers, including solo developers and small teams, to build efficient models without requiring extensive computational resources.
## Acknowledgements
This project was developed by Udit Raj, Sanya Gupta, and other fellow researchers at the Indian Institute of Technology, Patna, as part of ongoing research in AI and natural language processing.
---
For detailed usage examples and code, visit [Rtrafactor on Google Colab](https://colab.research.google.com/drive/1nzJou9tONTF6AOKqJTZc8--rIwI38JuR?usp=sharing).