清华大学&面壁智能 投稿 量子位 | 公众号 QbitAI 端侧性价比之王，清华大学和面壁智能团队开源新模型—— MiniCP M 4 ，提供 8B、0.5B 两种参数规模， 仅使用同级别开源模型22%的训练开销 ，就达到了同级别最优性能。 MiniCPM4-8B是 开源首个开源的原生稀疏模型，5%的极高稀疏度加持，让长文本、深思考在端侧真正跑起来。 在MMLU、CEval、MATH500、HumanEval等基准测试中，以仅22%的训练开销，性能比肩 Qwen-3-8B，超越Gemma-3-12B。 MiniCPM4-0.5B 在性能上，也展现出以小博大——在MMLU、CEval、BBH、HumanEval等基准测试中，MiniCPM4.0 -0.5B性能超越同级 的Qwen-3-0.6B、Llama 3.2、Gemma3， 并通过 原生QAT技术 实现几乎不掉点的int4量化以及600Token/s的极速推理速度。 在常见端侧芯片，比如Jetson AGX Orin与RTX 4090上，MiniCPM 4可实现长文本处理的5倍常规加速与极限场景下的百倍加速。 请看VCR： 目前团队已公开发布技术报告，该模 ...

Core Viewpoint - Meta has launched the Llama 4 series, which includes the most advanced models to date, Llama 4 Scout and Llama 4 Maverick, marking a significant advancement in open-source AI models and a response to emerging competitors like DeepSeek [1][3][10]. Group 1: Model Features - Llama 4 series includes two efficient models: Llama 4 Scout and Llama 4 Maverick, with a preview of the powerful Llama 4 Behemoth [5][8]. - The Llama 4 models utilize a mixture of experts (MoE) architecture, enhancing computational efficiency by activating only a small portion of parameters for each token [7][8]. - Llama 4 Behemoth boasts a total parameter count of 2 trillion, while Llama 4 Scout has 109 billion parameters and Llama 4 Maverick has 400 billion parameters [8]. Group 2: Multi-Modal Capabilities - Llama 4 is designed as a native multi-modal model, employing early fusion technology to integrate text, images, and video data seamlessly [8][9]. - The model supports extensive visual understanding, capable of processing up to 48 images during pre-training and 8 images during post-training, achieving strong results [9]. Group 3: Contextual Understanding - Llama 4 Scout supports a context window of up to 10 million tokens, setting a new record for open-source models and outperforming competitors like GPT-4o [9]. Group 4: Competitive Landscape - The release of Llama 4 comes amid increasing competition in the open-source model space, particularly from DeepSeek and Alibaba's Tongyi Qianwen series [11][12]. - Meta's previous open-source initiatives, such as Llama 2, have spurred innovation within the developer community, leading to a vibrant ecosystem [11]. - The competitive environment is intensifying, with ongoing advancements in model capabilities and frequent releases from various companies [13].