--- 格式版本: 2 标题: "Extending Context and Exploring Multilingual Reasoning with Poro 2 Long" 原文链接: "https://www.amd.com/en/blogs/2026/extending-context-and-exploring-multilingual-reasoning-with-poro.html" 发布日期: "2026-08-06" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:scrape:provider_published_at" 发布时间证据: "provider publishedAt: 2026-08-06" 发布时间校准原因: "规则确认唯一严格发布时间，来源 scrape:provider_published_at" 发布时间校准置信度: "high" 发布时间候选数量: 5 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-10T16:03:56+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-08-10T15:57:19+08:00" 入库时间: "2026-08-10T08:03:57.282Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://www.amd.com/en/blogs.html" 匹配关键词: - "GPU" - "performance" 相关厂家: - "AMD" 相关专家: [] 内容类型: "网页" 抓取工具: "Free Fetch + Defuddle" 清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 15 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文为AMD开源多语言模型发布，仅涉及LLM软件层面，未讨论超节点、AI Rack、机柜级系统、供电、散热、互连或量产落地等硬件基础设施主题，与项目关注范围无关。" AI质检模型: "deepseek-v4-flash" AI质检时间: "2026-08-10T16:04:04+08:00" AI主题相关性: 0 AI来源权威性: 15 AI新颖性: 0 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 0 采集批次: "2026年8月10日15点37分56秒" 采集批次ID: "20260810-153756-703" 去重键: "https://www.amd.com/en/blogs/2026/extending-context-and-exploring-multilingual-reasoning-with-poro.html" --- **AMD Silo AI is releasing two new additions to the Poro 2 family: Poro 2 8B Long, which provides a significantly expanded context window, and Poro 2 8B Math Reasoning, a preview of a math-focused reasoning variant trained to solve mathematical problems in Finnish and English. Together, these releases and their training approach lay the foundation for future development of agentic workflows, enabling more dynamic and autonomous AI processes.** Poro is a family of open-source, multilingual large language models (LLMs) optimized for Finnish language performance, developed by AMD Silo AI in partnership with  [TurkuNLP](https://turkunlp.org/)  and trained on the  [LUMI supercomputer](https://lumi-supercomputer.eu/). The original Poro 2 models (8B and 70B parameters), released last year, were built on the Llama 3.1 8B and 70B base models using Continued Pretraining (CPT) to improve Finnish language competence. While Finnish serves as the demonstration language, the underlying methodology applies to any language: Poro exemplifies how base models can be adapted for multilingual use.   ## Extending Context Window with 16x Increase in Tokens Poro 2 8B Long extends the model’s maximum context window from 8,192 tokens to 131,072 tokens, a 16x improvement over the original Poro 2 8B. This allows the model to process substantially more text in a single interaction. The expanded context window is especially valuable in Retrieval Augmented Generation (RAG) pipelines, where relevant documents are automatically retrieved and provided to the model as additional context when answering user questions. With a larger context window, the model can incorporate more retrieved information, supporting more comprehensive and well-informed responses. ## Poro 2 8B Math Reasoning: Exploring Multilingual Mathematical Reasoning  Poro 2 8B Math Reasoning is an alternative version of Poro 2 8B Long that has been trained to reason about mathematical problems. As a preview release, it is not yet production-ready, but it represents a meaningful step forward. Many reasoning models today produce reasoning traces only in English or Chinese, while Poro 2 8B Math Reasoning can reason in both Finnish and English. This allows Finnish-speaking users to review reasoning traces in their native language, identify errors, and build confidence in results.    Poro 3, expected later in the year, will build on this foundation as a more capable reasoning model with additional features. In the meantime, the Math Reasoning model, along with its associated code and training datasets, is released under an open-source license, making the resources available for researchers and developers to inspect, reproduce, and extend. Additionally, the release includes playbooks describing how the models were trained on AMD Instinct  TM GPU hardware, further expanding the community's access to practical model training techniques for multilingual AI development.  Beyond these immediate capabilities, both the extended context window and the reasoning features introduced in this release lay out the groundwork for future agentic capabilities, enabling more dynamic and autonomous AI workflows in upcoming releases.  ## Advancing Digital Sovereignty and European AI Capabilities AMD Silo AI develops open-source, multilingual language models and training resources for research and industry, work that includes the Poro family and initiatives such as Minerva. AMD Silo AI is a member of the OpenEuroLLM consortium, a pan‑European initiative of 20 universities, companies and EuroHPC centres building open, next‑generation language models for European languages. The Poro 2 8B Long release contributes to OpenEuroLLM’s mission to strengthen Europe’s digital sovereignty and competitiveness and to democratize access to high‑quality, multilingual AI. To learn more, visit the initiatives  [website](https://openeurollm.eu/).   ## The released materials The Poro 2 8B Long and Poro 2 8B Math Reasoning models are available on HuggingFace. The updated playbooks and some of the training datasets are available on the AMD ROCm™ blog.  - Poro 2 8B Long [on Hugging Face](https://huggingface.co/collections/LumiOpen/poro-2-long) - Poro 2 8B Math Reasoning [on Hugging Face](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Long-Math-Reasoning-RL-Preview) - The playbook for both Poro 2 8B Long and Poro 2 8B Math Reasoning on the [AMD ROCm™ Blog](https://rocm.blogs.amd.com/artificial-intelligence/rl-training-playbook/README.html). For more information on previous Poro releases, visit the  [AMD ROCm™ blog.](https://rocm.blogs.amd.com/artificial-intelligence/multilingual-continued-pretraining/README.html) --- ![](https://www.amd.com/content/dam/amd/en/images/backgrounds/dividers/divider-white-pearl-gradient-medium.jpg "white pearl gradient medium color divider")