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Lightricks releases open-source LTX-Video13B, a 13B-parameter video generation model with multi-scale rendering, achieving 30x faster speeds. It runs on consumer GPUs, supports 1216×704 real-time generation, and offers text/image/video-to-video modes. The model enhances coherence and detail, enabling keyframe control and style transfer. Free for SMEs, it includes training tools and optimized versions to democratize AI video creation.....

Moonshot AI's open-source model Kimi K2 excels in programming, matching Qwen3-235B-A22B and nearing o3-mini-high & Claude-3.7-Sonnet. With 1T MoE, 128k context, 65.8% accuracy on SWE-bench, and $0.14/M tokens, it's ideal for coding. Supports web generation, workflows, vLLM/Hugging Face deployment, MIT-licensed.....

NVIDIA's Canary-Qwen-2.5B sets a 5.63% WER record on Hugging Face OpenASR. This CC-BY licensed model combines FastConformer encoder with Qwen3-1.7B LLM decoder for efficient speech-to-text and NLP. Supports multi-GPU deployment for cloud/edge applications.....

Recently, the ByteDance Seed team collaborated with the University of Hong Kong and Fudan University to introduce an innovative reinforcement learning training method called POLARIS. This method successfully enhances the mathematical reasoning capabilities of small models to levels comparable to those of large models through a carefully designed Scaling RL strategy, offering a new approach for optimizing small models in the field of artificial intelligence. Experimental results show that the 4 billion parameter open-source model Qwen3-4B trained using POLARIS achieved remarkable performance on AIME25 and AIME24 mathematical tests.

Recently, Tencent Cloud officially launched the API service for the Tencent Hunyuan A13B model on its official website. The input price is set at 0.5 yuan per million Tokens, and the output price is 2 yuan per million Tokens, which has quickly sparked enthusiastic discussions in the developer community. As the first 13B-level MoE (Mixture of Experts) open-source hybrid inference model in the industry, Hunyuan-A13B features a total of 80B parameters and only 13B activated parameters, achieving performance comparable to leading open-source models of the same architecture, while also demonstrating efficient reasoning capabilities.

Tencent open-sourced 'Hunyuan-A13B', an 80B-parameter MoE model with dynamic inference (130B active params). Supports 256K context, trained on 20T tokens. Achieves 87.3% on AIME2024 math, but comparisons show version disparities.....

Bilibili focuses on video podcasting, launching 'Project H' AI tool for audio-to-video conversion. Support includes traffic boost, free recording spaces, and AI tools. The tool converts 1k words to video in 6 mins (target: 3 mins), with positive test feedback.....

B站开源AniSora V3动漫视频生成模型，提升质量与流畅度，支持多种风格。基于CogVideoX-5B/Wan2.1-14B，优化时空掩码，新增华为NPU加速20%。扩展千万片段数据集，增强多任务处理，达到顶尖角色一致性。....

On June 30, Huawei officially announced the open sourcing of the Pangu dense model with 7 billion parameters, the PanguPro MoE model with 72 billion parameters, and the model inference technology based on Ascend. This open-source initiative is a key step in Huawei's strategy to build an Ascend ecosystem, aiming to promote research and innovation in large model technology, accelerate the application of artificial intelligence across industries, and create value.

Welcome to AIbase's [AI Daily Report]! Spend three minutes every day to learn about the latest AI news, helping you understand AI industry trends and innovative AI product applications. For more AI updates, visit: https://www.aibase.com/zh1. Tencent open-sources the lightweight Huyuan-A13B model, which can be deployed with just one mid-range GPU card. Tencent has released a new member of the Huyuan large model family, the Huyuan-A13B model, which uses a mixture of experts (MoE) architecture, with a total parameter scale of 80 billion and an activated parameter count of 13 billion, large

Tencent officially launched and open-sourced a new member of the Hypermix large model family - the Hypermix-A13B model. The model adopts an expert mixture (MoE) architecture, with a total parameter scale of 80 billion and an activated parameter count of 13 billion. It maintains the performance of top-tier open-source models while significantly reducing inference latency and computational costs, providing a more cost-effective AI solution for individual developers and small and medium-sized enterprises.