介绍Apriel-Nemotron-15b-thinker：一种资源有效的推理模型

建立此类模型需要数学推理，科学理解和高级模式识别的整合。

In today's technological landscape, AI models are expected to perform complex tasks such as solving mathematical problems, interpreting logical statements, and assisting with enterprise decision-making. Building such models requires an integration of mathematical reasoning, scientific understanding, and advanced pattern recognition. As the demand for intelligent agents in real-time applications, like coding assistants and business automation tools, continues to increase, there is a pressing need for models that combine strong performance with efficient memory and token usage, making them viable for deployment in practical hardware environments.

在当今的技术格局中，预计AI模型将执行复杂的任务，例如解决数学问题，解释逻辑陈述以及协助企业决策。建立此类模型需要整合数学推理，科学理解和高级模式识别。随着对实时应用程序中智能代理的需求（例如编码助手和业务自动化工具）的需求不断增加，因此迫切需求将强大的性能与有效的内存和令牌使用相结合，使其可在实际硬件环境中部署。

A central challenge in AI development is the resource intensity of large-scale reasoning models. Despite their impressive capabilities, these models often demand significant memory and computational resources, limiting their real-world applicability. This disparity creates a gap between what advanced models can achieve and what users can realistically deploy. Even well-resourced enterprises may find running models consuming dozens of gigabytes of memory or incurring high inference costs unsustainable. The crux of the issue isn't simply about creating smarter models; it's about ensuring they are efficient and deployable in real-world platforms.

AI开发中的一个核心挑战是大规模推理模型的资源强度。尽管具有令人印象深刻的功能，但这些模型通常需要大量的内存和计算资源，从而限制了其现实世界的适用性。这种差异在高级模型可以实现的目标与用户可以实际部署的内容之间造成了差距。即使是资源良好的企业，也可能会发现跑步模型消耗了数十GB的内存或产生高推理成本。问题的症结不仅仅是创建更智能的模型。这是关于确保它们在现实世界平台中有效且可部署。

Models like QWQ‑32b, o1‑mini, and EXAONE‑Deep‑32b have demonstrated strong performance on tasks involving mathematical reasoning and academic benchmarks. However, their performance comes at a cost—they require high-end GPUs and consume a high number of tokens, rendering them less suitable for production settings. These models highlight the ongoing trade-off in AI deployment: achieving high accuracy at the expense of scalability and efficiency.

QWQ -32B，O1 − Mini和Exaone -Deep -32b等模型在涉及数学推理和学术基准的任务上表现出了强劲的表现。但是，它们的性能是有代价的 - 它们需要高端GPU并消耗大量令牌，使它们不太适合生产环境。这些模型突出了AI部署中持续的权衡：以高准确性为代价，以可扩展性和效率为代价。

To address this gap, researchers at ServiceNow introduced Apriel-Nemotron-15b-Thinker. This model, consisting of 15 billion parameters, is relatively modest in size compared to its high-performing counterparts. However, it delivers performance on par with models almost twice its size, and its primary advantage lies in its memory footprint and token efficiency. Despite delivering competitive results, it requires nearly half the memory of QWQ‑32b and EXAONE‑Deep‑32b, and it consumes 40% fewer tokens than QWQ‑32b, rendering it significantly more cost-effective for operational tasks. This difference in operational efficiency is crucial in enterprise environments, rendering it feasible to integrate high-performance reasoning models into real-world applications without large-scale infrastructure upgrades.

为了解决这一差距，ServiceNow的研究人员引入了Apriel-Nemotron-15B-Inker。该模型由150亿个参数组成，与其表现高的同行相比，大小相对较小。但是，它以几乎两倍的型号的型号提供表现，其主要优势在于其内存足迹和象征效率。尽管取得了竞争性的结果，但它需要将近QWQ-32B和Exaone-Deep-Deep-32b的记忆，并且比QWQ-32B少40％的令牌，从而使其在操作任务中的成本效益更大。在企业环境中，操作效率的这种差异至关重要，这使得将高性能推理模型集成到没有大规模基础架构升级的情况下的真实应用程序中。

The development of Apriel-Nemotron-15b-Thinker followed a structured three-stage training approach, each designed to enhance a specific aspect of the model’s reasoning capabilities. The initial phase, termed Continual Pre-training (CPT), involved exposing the model to over 100 billion tokens. These tokens weren't generic text but carefully selected examples from domains requiring deep reasoning, such as mathematical logic, programming challenges, scientific literature, and logical deduction tasks. This exposure provided the foundational reasoning capabilities that distinguish the model. The second stage involved Supervised Fine-Tuning (SFT) using 200,000 high-quality demonstrations. These examples further calibrated the model’s responses to reasoning challenges, enhancing performance on tasks that require accuracy and attention to detail. The final tuning stage, GRPO (Guided Reinforcement Preference Optimization), refined the model’s outputs by optimizing alignment with expected results across key tasks. This pipeline ensures the model is not only intelligent but also responds in a manner that is concise, structured, and scalable.

Apriel-Nemotron-15B-Inker的开发遵循结构化的三阶段训练方法，旨在增强模型推理能力的特定方面。初始阶段称为持续的预训练（CPT），涉及将模型暴露于超过1000亿个令牌。这些令牌不是通用的文本，而是从需要深层推理的领域中精心选择的示例，例如数学逻辑，编程挑战，科学文献和逻辑推论任务。这种暴露提供了区分模型的基本推理能力。第二阶段涉及使用200,000个高质量示威的监督微调（SFT）。这些示例进一步校准了模型对推理挑战的响应，增强了需要准确性和对细节关注的任务的绩效。最终调整阶段GRPO（指导的增强偏好优化）通过优化跨关键任务的预期结果对齐模型的输出来完善模型的输出。该管道可确保模型不仅智能，而且以简洁，结构化和可扩展的方式做出响应。

In enterprise-specific tasks such as MBPP, BFCL, Enterprise RAG, MT Bench, MixEval, IFEval, and Multi-Challenge, the model delivered competitive or superior performance compared to larger models. It also performed admirably in academic benchmarks, such as AIME-24, AIME-25, AMC-23, MATH-500, and GPQA, often equaling or surpassing the performance of other larger models, all while being significantly lighter in computational demand.

在企业特定的任务中，例如MBPP，BFCL，Enterprise Rag，Mt Banch，Mixeval，Ifeval和Multi-Challenge，与较大的模型相比，该模型提供了竞争性或卓越的性能。它还在学术基准中表现出色，例如AIME-24，AIME-25，AMC-23，MATH-500和GPQA，通常相等或超过其他较大模型的性能，同时在计算需求中都显着较轻。

Apriel-Nemotron-15b-Thinker demonstrates that achieving both high performance and efficiency in large language models is possible. As the demand for intelligent and deployable agents continues to rise, models like Apriel-Nemotron-15b-Thinker highlight the potential for pushing the boundaries of AI while ensuring it remains relevant and applicable in real-world settings. Several Key Takeaways from the Research on Apriel-Nemotron-15b-Thinker:This model is capable of performing on par with models almost twice its size. It achieves this performance with a lower memory footprint and token consumption compared to QWQ-32b and EXAONE-Deep-32b. It is interesting to note that it performs better than o1-mini on AIME-24, AIME-25, and AMC-23, despite being a smaller model.

Apriel-Nemotron-15B-Inkiner表明，在大语言模型中可以达到高性能和效率。随着对智能和可部署代理的需求不断上升，诸如Apriel-Nemotron-15B-Inker之类的模型突出了推动AI界限的潜力，同时确保其在现实世界中保持相关性和适用性。关于Apriel-Nemotron-15B-Inker的研究的一些关键要点：该模型能够以几乎两倍的型号的型号进行表现。与QWQ-32B和Exaone-Deep-32B相比，它通过较低的内存足迹和象征性消耗来实现这种性能。有趣的是，尽管模型较小，但在AIME-24，AIME-25和AMC-23上的性能要比O1-MINI表现更好。

The researchers used a structured three-stage training approach to develop the model. The initial stage involved exposing the model to over 100 billion tokens from domains that require deep reasoning, such as mathematical logic, programming challenges, and logical deduction tasks.

研究人员使用结构化的三阶段训练方法来开发模型。初始阶段涉及将模型从需要深层推理的领域（例如数学逻辑，编程挑战和逻辑扣除任务）暴露于超过1000亿个令牌。