Nvidia發佈小型語言模型Nemotron-Nano-9B-v2，擁有9億參數，較原版12億大幅縮減，旨在單臺A10GPU高效運行。該模型採用混合架構，適應實際部署需求，推動小型模型發展。
英偉達發佈小型語言模型Nemotron-Nano-9B-V2，參數量90億，專爲高效推理設計。該模型在多項基準測試中表現優異，部分測試達到同類產品最高水平，旨在與麻省理工和谷歌的小型AI模型競爭，適用於智能手錶和手機等設備。
Nvidia發佈小型語言模型Nemotron-Nano-9B-v2，參數量90億，較前代減少30億，專爲單個A10GPU優化。該模型在基準測試中表現優異，支持靈活控制推理功能，處理速度提升高達6倍，適用於多種應用場景。
bartowski
這是NVIDIA Nemotron-Nano-9B-v2模型的量化版本，使用llama.cpp b6317版本進行量化處理。該模型提供了多種量化選項，包括bf16、Q8_0、Q6_K_L等，適用於不同的硬件和使用場景，方便用戶部署和使用。
Gguf%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
