Sailor-7B-Chat is a 7.7 billion parameter instruction-tuned causal language model developed by sail, built upon the Qwen 1.5 architecture. It is specifically tailored for South-East Asian languages, including Indonesian, Thai, Vietnamese, Malay, and Lao, while maintaining proficiency in English and Chinese. The model excels at tasks such as question answering and commonsense reasoning across these diverse linguistic landscapes, with a context length of 32768 tokens.
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Overview
Sailor-7B-Chat is a 7.7 billion parameter instruction-tuned language model from the Sailor suite, developed by sail. It is built upon the robust Qwen 1.5 architecture and specifically optimized for South-East Asian (SEA) languages, including Indonesian, Thai, Vietnamese, Malay, and Lao. The model also maintains strong performance in English and Chinese.
Key Capabilities & Training
- Multilingual Proficiency: Designed to understand and generate text across diverse SEA linguistic landscapes, alongside English and Chinese.
- Instruction-Tuned: Fine-tuned with publicly available instruction datasets such as aya_collection, aya_dataset, and OpenOrca.
- Extensive Pre-training: Continuously pre-trained on 200-400 billion tokens from corpora like SlimPajama, SkyPile, CC100, and MADLAD-400, with aggressive data deduplication and cleaning.
- Performance: Benchmarking indicates proficiency in tasks like question answering and commonsense reasoning in SEA languages.
- Context Length: Supports a context length of 32768 tokens.
Use Cases
Sailor-7B-Chat is particularly well-suited for applications requiring strong language understanding and generation in South-East Asian languages. Its instruction-tuned nature makes it effective for conversational AI, question answering systems, and other tasks where precise instruction following is crucial, especially within the SEA region.