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English(EN) Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away

新的2.6B塔吉克语模型在工具调用方面表现出色,但英语能力有所下降

研究人员开发了Saidzoda-Tajik-LFM-2.6B,一个拥有26亿参数的混合模型,已适配塔吉克语,并专注于工具调用能力。适配过程包括在塔吉克语网络文档上进行持续预训练,然后对工具调用数据进行监督微调。虽然该模型显著提高了塔吉克语工具调用的准确性,但它也明显削弱了模型以其原生英语格式执行工具调用的能力,并导致英语MMLU分数下降。研究人员警告说,该模型并非通用的塔吉克语助手,其调用在执行前需要验证。 AI

影响 这项研究展示了一种将现有模型适配到新语言和任务的方法,突出了不同能力之间性能的权衡。

排序理由 该集群描述了一篇新的研究论文,详细介绍了将一个语言模型适配到特定语言和任务的过程,包括方法论和评估结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的2.6B塔吉克语模型在工具调用方面表现出色,但英语能力有所下降

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该集群描述了一篇新的研究论文,详细介绍了将一个语言模型适配到特定语言和任务的过程,包括方法论和评估结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov, Abubakr Abdullozoda, Amin Jalilov, Jonibek Kishvarov, Rahmonali Sharipov, Mariyamkhon Shodyeva ·

    Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away

    arXiv:2610.41576v1 Announce Type: new Abstract: We release Saidzoda-Tajik-LFM-2.6B, a Tajik adaptation of the 2.6B-parameter hybrid model LFM2.5-2.6B-Base, built for tool calling in Tajik on modest hardware. The recipe has two stages: LoRA continued pretraining (CPT) on 1,485,319…