Typhoon T1: An Open Thai Reasoning Model

TL;DR

This paper presents Typhoon T1, an open Thai reasoning model built with supervised fine-tuning on open datasets, capable of generating reasoning traces in a low-resource language, aiming to advance research in this area.

Contribution

It introduces a cost-effective approach to developing reasoning models in low-resource languages using supervised fine-tuning and shares detailed datasets, model weights, and insights.

Findings

Successfully generated reasoning traces in Thai.

Demonstrated generalization across multiple domains.

Provided open datasets and model weights for research use.

Abstract

This paper introduces Typhoon T1, an open effort to develop an open Thai reasoning model. A reasoning model is a relatively new type of generative model built on top of large language models (LLMs). A reasoning model generates a long chain of thought before arriving at a final answer, an approach found to improve performance on complex tasks. However, details on developing such a model are limited, especially for reasoning models that can generate traces in a low-resource language. Typhoon T1 presents an open effort that dives into the details of developing a reasoning model in a more cost-effective way by leveraging supervised fine-tuning using open datasets, instead of reinforcement learning. This paper shares the details about synthetic data generation and training, as well as our dataset and model weights. Additionally, we provide insights gained from developing a reasoning model that generalizes across domains and is capable of generating reasoning traces in a low-resource language, using Thai as an example. We hope this open effort provides a foundation for further research in this field.