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Qwen-Image-2.1 supports several ways to specify local edits. The example below uses circles to identify three regions and asks the model to “remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas,” performing removals and modifications in all three regions at once.

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inted annotations, and separate masks to specify where an edit should take place. The first example uses circles of different colors to identify three regions at once: “Remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas.” The annotated input is on the left, and the result is on the right. Painted annotations provide another way to identify a region. In this example, the model adds a

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across 47,885 pairs, v2.5 was the preferred take the majority of the time. The gap is widest with songs that are vocal-led and acoustic-heavy genres like R&B, soul, hip hop, rock, metal, orchestral and cinematic music. 2.5 is the new default in ElevenMusic, but if you're attached to Music v2, don't worry. It isn't going anywhere. Your music Every track you make in ElevenMusic is yours. On the Free plan you get five lossless downloads a day, and you can use what you make commercially as long as y

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le product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models! Related Stories MAI-Transcribe-2 is the fastest, most accurate and cheapest speech recognition model in the world MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost MAI-Image-2.6 launches at No. 2 on Arena ahead of Google, Meta and xAI

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flow Visit The Rundown University . How do you use AI? Tell us for a chance to be featured. 🚀 Claude Fable 5.1 - Anthropic’s new top-ranked model upgrade 👂 Muse Voice Transcribe - Meta’s live speech-to-text AI, tracks 20+ speakers 👁️ Gemini - Google’s AI, now with efficient agentic video understanding 🎨 Google Pics - Google's Nano Banana image editor built into Docs and Slides OpenAI restarted the training run for future Astra versions that it froze after the Hugging Face breach, rating Astra as

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not a transcription failure; Chart scores extraction of numerical data series from chart images into structured tables. Our model treats charts as visual elements with descriptive metadata, not as data-extraction targets. This is a product scoping decision — chart data extraction is planned for the next Parser version On the three dimensions that align with our product goals — Tables, Text Content, and Text Formatting — we report full results. These cover the structured transcription and semanti

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cted original K/V, and dispatches to TRTLLM-Gen on Blackwell or packed FlashAttention otherwise. The indexer can overlap the main Q/K/V projection on a second CUDA stream, and the metadata paths are CUDA-graph compatible. A QSA layer runs an indexer that picks which tokens to attend, then a sparse attention over exactly those tokens. The second stage has a fixed token budget; the first scores its query against all ⌈L/4⌉ compressed blocks, so beyond a few thousand tokens the indexer, not the atte

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u decide the song structure; Fine-grained control via Structured Captions — Beyond a plain-language description, the model accepts a Structured Caption with three sections: Global Metadata (genre, BPM, key, scale, emotional progression, production profile), Vocal Details (gender, timbre, performance style, harmony, backing vocals, effects), and Arrangement (instruments, section-level instrument evolution, groove, bass, percussion, textures, spatial effects). This lets you steer not just the over

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sovereignty. Three days after Laguna shipped, Jensen Huang made a version of the same argument in his first post on X, sharing an open letter signed by NVIDIA alongside Microsoft, Meta and Hugging Face on why open models strengthen security, accelerate innovation and enable sovereignty, and why the world needs frontier models of both kinds. The same logic applies inside an enterprise. Most frontier AI is consumed through an API or a desktop app, which means the customer’s code crosses its securi

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-H-Dreams also provides the human-machine interfaces that turn generation into interaction. A browser client can send keyboard commands and receive generated frames over WebRTC. A Meta Quest client can map tracked controller motion into robot actions and display the synthesized scene through WebXR. The same model can also be connected to a learned surgical policy, with generated observations and predicted actions exchanged inside a closed loop. 4. Adapting to Your Own Data While Cosmos-H-Dreams

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esigns, the kind of work Google’s models demonstrated earlier. I don’t think K3 dethrones OpenAI or Anthropic. But it looks probably ahead of other leading proprietary models like Meta Spark and Grok 4.5 on coding and agentic tasks. That’s the market it will actually contest and can take share in. It also raises the obvious question: why keep a model this good open at all? My read is that the race is now cutthroat enough that no one knows how long K3 stays out front among open models. MiniMax is

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tal simulation as an internal planning step — whereas traditional agent training focuses solely on state-to-action decision-making. Next-state prediction is thus internalized as a meta-reasoning pattern similar to “reflection” but oriented toward the future: predict before you act . Furthermore, accurate next-state prediction itself requires reasoning, knowledge, instruction following, and long-context handling — capabilities that are foundational to general agents. What makes general-purpose la

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tion overhead: replacing per-call cudaMalloc / cudaFree with pre-allocated torch::empty tensors, eliminating synchronous cudaMemcpy calls for prefix length queries by using tensor metadata instead, and unrolling the inner loop 2x to amortize loop control overhead and increase instruction-level parallelism. Workload-adaptive split tuning (5.37x → 6.85x, 3h): The model evolved from a fixed split divisor to workload-size-dependent heuristics — applying more aggressive splitting for smaller inputs a

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the drums, while the host mirrors some of the steps but struggles to keep pace, occasionally stumbling and laughing at himself. Dust swirls around their feet; sunlight flashes off metallic ornaments on the costume. The crowd’s energy rises—shouts, whistles, and rhythmic clapping blend with the relentless percussion. 00:26.000 – 00:31.000 In a climactic flourish, the masked dancer spins rapidly, cape flaring like a multicolored wheel. He closes the distance and enfolds the host in a brief celebra

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けに対し、Web検索を使わず指定された文字数を守って回答。Namazuは、端的かつ抑制的なトーンで回答を返してくれる傾向が見られます。 検索機能を使用せず、モデル内部の知識のみで英語論述を行った例。「有能さ（Competent）」の定義について、個人のスキルだけでなく社会的な役割や関係性に重点を置いた内容で回答を生成しています。 おわりに DeepSeek、Meta、OpenAIをはじめとする、優れた基盤モデルを開発・公開しているAIコミュニティに深く敬意を表します。当社の成果は、こうした先行する技術基盤とオープンなエコシステムの上に成り立っています。また、Namazuの訓練にあたり、2025年10月から11月の2ヶ月間にわたり、GMOインターネット株式会社より「GMO GPUクラウド」の計算リソースをご支援いただきました（プレスリリース、インタビュー記事）。Sakana Chatの改善にご協力いただいた約1,000名のβテスターの皆様にも改めて感謝申し上げます。 今回のプロジェクトは、適切な事後学習を施すことで、大規模なモデルであっても各国で安全に使うための要件へ適応可能であることを

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ing and proving. Use /leanstall to active. Then to use Leanstral press Shift+Tab until the model displays as Leanstral, Alternately, use vibe --agent lean; Labs API: Access the model via our free/near-free API endpoint labs-leanstral-2603. We are keeping this endpoint highly accessible for a limited period to gather realistic feedback and observability data to fuel the next generation of verified code models; Own the Weights: Download the Apache 2.0 licensed model and run it on your own metal 0%

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captures a wide, paved asphalt road stretching into the distance under a bright, clear sky. On the left side of the road, there is a sidewalk lined with a series of blue and white metal bike racks. Behind these racks, a low fence with a green base and yellow horizontal bars separates the sidewalk from a dense, lush green tree line. The trees are thick with foliage, creating a natural canopy over the road. In the middle distance, a blue public trash bin is visible near the fence. Further down the

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urm + Cgroup v2 ensures clean job teardown and prevents leftover processes from blocking GPU memory; Storage: Lustre for high-throughput training I/O, and NVMe-backed NFS for fast metadata and seamless SSH access; Observability: DCGM + Prometheus monitoring lets us catch errors early and drain unstable nodes before they impact training INTELLECT-3 Training Recipe INTELLECT-3 was trained in two main stages: a supervised fine-tuning stage on top of the GLM-4.5-Air base model, and a large-scale RL

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h-end phones, tablets, and laptops for a wide range of applications. Inference The following video shows LFM2-8B-A1B running locally on a MacBook Pro with an Apple M2 Pro chip and Metal enabled (Q4 0 quantization). LFM2-8B-A1B is compatible with multiple inference frameworks, such as llama.cpp, ExecuTorch, and vLLM (public version coming soon). The following plots show the decode throughput with llama.cpp backend and Q4 0 quantization on two hardware targets: the Samsung Galaxy S24 Ultra (Qualco

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ices directly—ensuring our model matches the discrete nature of attention without extra overhead A Practical On-Device Implementation We’ve optimized Mamba-2 kernels using Apple’s Metal framework, allowing our models to take full advantage of Apple Silicon’s GPU parallelism and unified memory architecture. Our integration with the MLX machine learning framework enables dynamic graph construction and efficient tensor operations, making it possible to run these models with consistent high throughp

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