Tajik-specialized Gemma 3 derivatives (12B/27B) beat same-size baselines by ~6–8 points on new Tajik exams after 1.9B-token continual pretraining, with FP8/INT4 still usable on edge GPUs.
VinaLLaMA: LLaMA-based Vietnamese Foundation Model
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this technical report, we present VinaLLaMA, an open-weight, state-of-the-art (SOTA) Large Language Model for the Vietnamese language, built upon LLaMA-2 with an additional 800 billion trained tokens. VinaLLaMA not only demonstrates fluency in Vietnamese but also exhibits a profound understanding of Vietnamese culture, making it a truly indigenous model. VinaLLaMA-7B-chat, trained on 1 million high-quality synthetic samples, achieves SOTA results on key benchmarks, including VLSP, VMLU, and Vicuna Benchmark Vietnamese, marking a significant advancement in the Vietnamese AI landscape and offering a versatile resource for various applications.
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cs.AI 1years
2026 1verdicts
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Soro: A Lightweight Foundation Model and Chatbot for Tajik
Tajik-specialized Gemma 3 derivatives (12B/27B) beat same-size baselines by ~6–8 points on new Tajik exams after 1.9B-token continual pretraining, with FP8/INT4 still usable on edge GPUs.