AGENTIC-SYSTEMSMay 28, 20261 min read5 views

Soro: A Lightweight Foundation Model and Chatbot for Tajik

Researchers have introduced Soro, a family of lightweight large language models specialized for the Tajik language, designed to operate under tight computational and connectivity constraints. Developed from Gemma 3, Soro is poised to drive digital transformation in Tajikistan's education sector through edge deployment.

Computer Science > Artificial Intelligence

Title:Soro: A Lightweight Foundation Model and Chatbot for Tajik

View PDFAbstract: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.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

HG-RAG (Hierarchy-Guided Retrieval-Augmented Generation) is a novel framework designed to overcome the limitations of traditional flat RAG systems by traversing hierarchical knowledge graphs, significantly improving multi-hop reasoning and reducing hallucinations in LLMs.

agentic-systems

Enhancing Small Language Models Reasoning through Knowledge Graph Grounding

Researchers have proposed a neuro-symbolic framework that enhances the reasoning capabilities of Small Language Models (SLMs) like Gemma 3 and Llama 3.2 using Knowledge Graphs, achieving up to a 2x performance boost.

agentic-systems

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

This paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies by integrating LLMs with numerical simulation tools under human supervision.

agentic-systems

Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach

Researchers propose a new methodology using Large Language Models and AI agents to construct Bayesian Belief Networks, bridging the gap between expert opinion and data-driven learning. The framework is demonstrated through a healthcare decision-making model, revealing key insights into customer behavior.

agentic-systems

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

Researchers have developed LLM-T1D, a novel approach combining reinforcement learning and large language models to automate insulin delivery for Type 1 Diabetes. The system not only outperforms traditional methods but also explains its decisions in plain language, addressing the 'black-box' challenge in medical AI.

agentic-systems

RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

Researchers have introduced RegNetAgents, an AI-oriented multi-agent framework designed to identify regulatory driver candidates across heterogeneous gene networks in cancer genomics. By integrating bulk tumor and single-cell data, the system offers a powerful tool for discovering therapeutic targets in breast and colorectal cancers.