SEA-LION Foundation Family

The SEA-LION models have served as a foundation for developing localized AI solutions tailored to specific linguistic and cultural needs. Over multiple iterations, other regions have built upon SEA-LION’s architecture to create specialized versions that enhance language understanding for their respective regions. These models leverage SEA-LION’s robust multilingual capabilities while being fine-tuned with localized datasets, ensuring better performance in regional contexts.

SEA-LION Model Tree

Base	Localized Model
SEA-LION v1	→ WangchanLion 7B (Thai): WangchanLion 7B is a multilingual instruction-following model developed by PyThaiNLP and the VISTEC-depa AI Research Institute of Thailand. Fine-tuned on SEA-LION-v1-7B, it incorporates approximately 500,000 samples from open-source, commercially permissible datasets, with a focus on Thai and English languages.
SEA-LION v2	→ Llama3 8B CPT Sahabat-AI v1 Instruct (Indonesian): Llama3 8B CPT Sahabat-AI v1 Instruct model, co-developed by GoTo Group and AI Singapore, is an Indonesian-focused adaptation fine-tuned with 448,000 Indonesian instruction-completion pairs, along with 96,000 Javanese and 98,000 Sundanese pairs. It supports Indonesian, Javanese, Sundanese, and English, making it a significant advancement in AI for the Indonesian linguistic landscape.
SEA-LION v3	→ Gemma2 9B WangchanLIONv2 (Thai): The Gemma2 9B WangchanLIONv2 Instruct model is a collaborative effort between VISTEC and AI Singapore. It has been fine-tuned with approximately 3,760,000 Thai instruction-completion pairs derived from human-annotated instructions, FLAN-style automatic data construction, and synthetic samples. This multilingual model supports both Thai and English languages. → Gemma2 9B CPT Sahabat-AI (Indonesian): The Gemma2 9B CPT Sahabat-AI v1 Instruct model, co-developed by GoTo Group and AI Singapore, has been fine-tuned with approximately 448,000 Indonesian instruction-completion pairs, along with 96,000 in Javanese, 98,000 in Sundanese, and an additional 129,000 in English. This multilingual model supports Indonesian, Javanese, Sundanese, and English.

Impact and Future Directions

By leveraging SEA-LION’s architecture, these localized models provide AI solutions that align more closely with native language requirements. As SEA-LION continues to evolve, more localized versions are expected to emerge, further expanding the reach and effectiveness of AI in Southeast Asian languages.