Koala-13B

Koala is a dialogue model developed for academic research, designed to generate human-like responses to a wide range of user queries. This AI-powered tool aims to provide a competitive alternative to larger, closed-source models by leveraging high-quality, curated datasets fine-tuned from Meta's LLaMA model.

Key features:
- Dialogue Generation: Responds to various user queries with outputs often preferred over those from similar models.
- High-Quality Data Utilization: Trained on carefully curated dialogue data, including interactions with other large language models and human feedback datasets.
- Competitive Performance: Demonstrates capabilities comparable to larger, closed-source models in user studies.
- Quality-Focused Dataset: Emphasizes a smaller, high-quality dataset over maximizing the quantity of web-scraped data.
- LLaMA-based Architecture: Built on Meta's LLaMA model and fine-tuned to enhance dialogue capabilities.

How it works:
1. Users interact with Koala through an online interactive demo.
2. The model processes user input and generates responses based on its training.
3. Researchers can evaluate the model's performance and identify areas for improvement.
4. Feedback on concerning actions can be reported to help refine the model.

Use of AI:
Koala leverages generative AI techniques to produce coherent and contextually appropriate responses. It is fine-tuned on dialogue data scraped from the web and public datasets, focusing on high-quality responses from other large language models and human feedback.

AI foundation model:
Koala is built on Meta's LLaMA, a large language model architecture. By fine-tuning LLaMA on high-quality dialogue data, Koala enhances its generative capabilities for dialogue tasks.

Target users:
- Academic researchers interested in dialogue models and generative AI
- AI enthusiasts exploring the capabilities of smaller, open-source models

How to access:
Koala is available as a web app for research purposes. It is not fully open-source but provides an interactive demo for researchers to explore its capabilities.

Training data composition:
- Dialogues with existing large language models
- Question answering datasets
- Human feedback datasets, both positive and negative