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arXiv · 2605.27379

Soro: A Lightweight Foundation Model and Chatbot for Tajik

Stanislav Liashkov·Haitz Sáez de Ocáriz Borde·Azizjon Azimi·Khushbakht Shoymardonov·Shuhratjon Khalilbekov·Bonu Boboeva
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Abstract

We present Soro, a family of Tajik-specialized conversational large language models (LLMs) designed for real-world deployment under tight compute and connectivity constraints in Tajikistan. Starting from open-weight Gemma 3 checkpoints, we perform Tajik-only continual pretraining on a curated 1.9-billion-token corpus spanning filtered web text, PDF documents, and curriculum-aligned educational materials, followed by supervised instruction tuning on 40K Tajik teacher-style examples. To enable rigorous evaluation despite the limited coverage of Tajik in standard benchmarks, we introduce a suite of Tajik benchmarks covering general knowledge, linguistic competence, and school- and university entrance-exam domains, and we open-source them on Hugging Face. Across these Tajik benchmarks, Soro substantially outperforms same-size Gemma 3 baselines while retaining strong English performance on standard datasets. We further show that FP8 and INT4 quantization of Soro preserves most Tajik-language gains while reducing memory requirements for edge deployment, supporting an ongoing education-sector pilot and planned scale-out across schools in Tajikistan.

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Stanislav Liashkov, Haitz Sáez de Ocáriz Borde, Azizjon Azimi, Khushbakht Shoymardonov, Shuhratjon Khalilbekov, Bonu Boboeva. 2026-04-09. Soro: A Lightweight Foundation Model and Chatbot for Tajik. https://arxiv.org/abs/2605.27379

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