Soro: A Lightweight Foundation Model and Chatbot for Tajik
Soro is a family of Tajik-specialized conversational LLMs built on Gemma 3, using 1.9B token Tajik continual pretraining and 40K instruction tuning examples. It substantially outperforms same-size Gemma 3 on Tajik benchmarks while retaining English performance. FP8/INT4 quantization preserves gains for edge deployment. An education pilot is underway in Tajikistan.
Article intelligence
Key points
- Based on Gemma 3, with 1.9B token Tajik continual pretraining and 40K instruction tuning examples.
- Substantially outperforms same-size Gemma 3 on Tajik benchmarks, retains English performance.
- FP8/INT4 quantization preserves most Tajik gains, reduces memory for edge deployment.
- Education sector pilot in Tajikistan, planned scale-out to schools.
Why it matters
This matters because based on Gemma 3, with 1.9B token Tajik continual pretraining and 40K instruction tuning examples.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27379] Soro: A Lightweight Foundation Model and Chatbot for Tajik
[Submitted on 9 Apr 2026]
Title:Soro: A Lightweight Foundation Model and Chatbot for Tajik
View a PDF of the paper titled Soro: A Lightweight Foundation Model and Chatbot for Tajik, by Stanislav Liashkov and 5 other authors
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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.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2605.27379 [cs.AI]
(or arXiv:2605.27379v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.27379
arXiv-issued DOI via DataCite
Submission history
From: Bonu Boboeva [view email] [v1] Thu, 9 Apr 2026 15:04:32 UTC (3,650 KB)
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