Typhoon T1: An Open Thai Reasoning Model
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.
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Pittawat Taveekitworachai
claimed_verified
2025-02-14T08:01:24.073Z
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Pittawat Taveekitworachai
user
pittawat
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Potsawee Manakul
admin_assigned
2025-02-14T12:48:21.806Z
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Potsawee Manakul
user
potsawee
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Kasima Tharnpipitchai
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Kunat Pipatanakul
claimed_verified
2025-02-14T12:41:14.906Z
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Kunat Pipatanakul
user
kunato
Typhoon T1: An Open Thai Reasoning Model
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.
Typhoon T1 is an open effort to develop a reasoning model in Thai using supervised fine-tuning on open datasets, providing insights for low-resource language reasoning.
reasoning model large language models supervised fine-tuning synthetic data generation reasoning traces