News from the AI & ML world
DeeperML - #opensourceai
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@www.marktechpost.com
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Moonshot AI has unveiled Kimi K2, a groundbreaking open-source AI model designed to challenge proprietary systems from industry leaders like OpenAI and Anthropic. This trillion-parameter Mixture-of-Experts (MoE) model boasts a remarkable focus on long context, sophisticated code generation, advanced reasoning capabilities, and agentic behavior, meaning it can autonomously perform complex, multi-step tasks. Kimi K2 is designed to move beyond simply responding to prompts and instead to actively execute actions, utilizing tools and writing code with minimal human intervention.
Kimi K2 has demonstrated superior performance in key benchmarks, particularly in coding and software engineering tasks. On SWE-bench Verified, a challenging benchmark for software development, Kimi K2 achieved an impressive 65.8% accuracy, surpassing many existing open-source models and rivaling some proprietary ones. Furthermore, in LiveCodeBench, a benchmark designed to simulate realistic coding scenarios, Kimi K2 attained 53.7% accuracy, outperforming GPT-4.1 and DeepSeek-V3. The model's strengths extend to mathematical reasoning, where it scored 97.4% on MATH-500, exceeding GPT-4.1's score of 92.4%. These achievements position Kimi K2 as a powerful, accessible alternative for developers and researchers. The release of Kimi K2 signifies a significant step towards making advanced AI more open and accessible. Moonshot AI is offering two versions of the model: Kimi-K2-Base for researchers and developers seeking customization, and Kimi-K2-Instruct, optimized for chat and agentic applications. The company highlights that Kimi K2's development involved training on over 15.5 trillion tokens and utilizes a custom MuonClip optimizer to ensure stable training at an unprecedented scale. This open-source approach allows the AI community to leverage and build upon this powerful technology, fostering innovation in the development of AI-powered solutions. Recommended read:
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@ComputerWeekly.com
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Meta and the UK Government have joined forces to launch a £1 million ‘Open Source AI Fellowship’ program. The goal is to embed some of the UK’s most promising AI experts within Whitehall, the UK government's administrative center, to develop advanced AI tools. These tools will aim to improve government agility and contribute to the delivery of the Plan for Change. The Alan Turing Institute is also backing the fellowship.
The program intends to harness the power of open source AI models, including Meta's Llama models. These models have shown great potential for scientific and medical breakthroughs and could transform public service delivery. Fellows will work within government departments, potentially contributing to high-security use cases like AI-powered language translation for national security, or speeding up the approval process for house building by leveraging construction planning data. The fellowship is a practical response to the growing demand for generative AI talent. It will provide engineers a chance to address high-impact public sector challenges, which aims to create transparent, sovereign AI infrastructure that can scale across departments while reducing costs and enhancing productivity. Technology Secretary Peter Kyle emphasizes the aim is to create open, practical AI tools "built for public good," focusing on delivery rather than just ideas and developing sovereign capabilities in areas like national security and critical infrastructure. Recommended read:
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@colab.research.google.com
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Google's Magenta project has unveiled Magenta RealTime (Magenta RT), an open-weights live music model designed for interactive music creation, control, and performance. This innovative model builds upon Google DeepMind's research in real-time generative music, providing opportunities for unprecedented live music exploration. Magenta RT is a significant advancement in AI-driven music technology, offering capabilities for both skill-gap accessibility and enhancement of existing musical practices. As an open-weights model, Magenta RT is targeted towards eventually running locally on consumer hardware, showcasing Google's commitment to democratizing AI music creation tools.
Magenta RT, an 800 million parameter autoregressive transformer model, was trained on approximately 190,000 hours of instrumental stock music. It leverages SpectroStream for high-fidelity audio (48kHz stereo) and a newly developed MusicCoCa embedding model, inspired by MuLan and CoCa. This combination allows users to dynamically shape and morph music styles in real-time by manipulating style embeddings, effectively blending various musical styles, instruments, and attributes. The model code is available on Github and the weights are available on Google Cloud Storage and Hugging Face under permissive licenses with some additional bespoke terms. Magenta RT operates by generating music in sequential chunks, conditioned on both previous audio output and style embeddings. This approach enables the creation of interactive soundscapes for performances and virtual spaces. Impressively, the model achieves a real-time factor of 1.6 on a Colab free-tier TPU (v2-8 TPU), generating two seconds of audio in just 1.25 seconds. This technology unlocks the potential to explore entirely new musical landscapes, experiment with never-before-heard instrument combinations, and craft unique sonic textures, ultimately fostering innovative forms of musical expression and performance. Recommended read:
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@www.analyticsvidhya.com
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MiniMaxAI, a Chinese AI company, has launched MiniMax-M1, a large-scale open-source reasoning model, marking a significant step in the open-source AI landscape. Released on the first day of the "MiniMaxWeek" event, MiniMax-M1 is designed to compete with leading models like OpenAI's o3, Claude 4, and DeepSeek-R1. Alongside the model, MiniMax has released a beta version of an agent capable of running code, building applications, and creating presentations. MiniMax-M1 presents a flexible option for organizations looking to experiment with or scale up advanced AI capabilities while managing costs.
MiniMax-M1 boasts a 1 million token context window and utilizes a new, highly efficient reinforcement learning technique. The model comes in two variants, MiniMax-M1-40k and MiniMax-M1-80k. Built on a Mixture-of-Experts (MoE) architecture, the model is trained on 456 billion parameters. MiniMax has introduced Lightning Attention for its M1 model, dramatically reducing inference costs and only consumes 25% of the floating point operations (FLOPs) required by DeepSeek R1 at a generation length of 100,000 tokens. Available on AI code sharing communities like Hugging Face and GitHub, MiniMax-M1 is released under the Apache 2.0 license, enabling businesses to freely use, modify, and implement it for commercial applications without restrictions or payment. MiniMax-M1 features a web search functionality and can handle multimodal input like text, images, and presentations. The expansive context window allows the model to exchange information equivalent to a small collection or book series, far exceeding OpenAI's GPT-4o, which has a context window of 128,000 tokens. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Mistral AI has launched its first reasoning model, Magistral, signaling a commitment to open-source AI development. The Magistral family features two models: Magistral Small, a 24-billion parameter model available with open weights under the Apache 2.0 license, and Magistral Medium, a proprietary model accessible through an API. This dual release strategy aims to cater to both enterprise clients seeking advanced reasoning capabilities and the broader AI community interested in open-source innovation.
Mistral's decision to release Magistral Small under the permissive Apache 2.0 license marks a significant return to its open-source roots. The license allows for the free use, modification, and distribution of the model's source code, even for commercial purposes. This empowers startups and established companies to build and deploy their own applications on top of Mistral’s latest reasoning architecture, without the burdens of licensing fees or vendor lock-in. The release serves as a powerful counter-narrative, reaffirming Mistral’s dedication to arming the open community with cutting-edge tools. Magistral Medium demonstrates competitive performance in the reasoning arena, according to internal benchmarks released by Mistral. The model was tested against its predecessor, Mistral-Medium 3, and models from Deepseek. Furthermore, Mistral's Agents API's Handoffs feature facilitates smart, multi-agent workflows, allowing different agents to collaborate on complex tasks. This enables modular and efficient problem-solving, as demonstrated in systems where agents collaborate to answer inflation-related questions. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Mistral AI has launched Magistral, its inaugural reasoning large language model (LLM), available in two distinct versions. Magistral Small, a 24 billion parameter model, is offered with open weights under the Apache 2.0 license, enabling developers to freely use, modify, and distribute the code for commercial or non-commercial purposes. This model can be run locally using tools like Ollama. The other version, Magistral Medium, is accessible exclusively via Mistral’s API and is tailored for enterprise clients, providing traceable reasoning capabilities crucial for compliance in highly regulated sectors such as legal, financial, healthcare, and government.
Mistral is positioning Magistral as a powerful tool for both professional and creative applications. The company highlights Magistral's ability to perform "transparent, multilingual reasoning," making it suitable for tasks involving complex calculations, programming logic, decision trees, and rule-based systems. Additionally, Mistral is promoting Magistral for creative writing, touting its capacity to generate coherent or, if desired, uniquely eccentric content. Users can experiment with Magistral Medium through the "Thinking" mode within Mistral's Le Chat platform, with options for "Pure Thinking" and a high-speed "10x speed" mode powered by Cerebras. Benchmark tests reveal that Magistral Medium is competitive in the reasoning arena. On the AIME-24 mathematics benchmark, the model achieved an impressive 73.6% accuracy, comparable to its predecessor, Mistral Medium 3, and outperforming Deepseek's models. Mistral's strategic release of Magistral Small under the Apache 2.0 license is seen as a reaffirmation of its commitment to open source principles. This move contrasts with the company's previous release of Medium 3 as a proprietary offering, which had raised concerns about a shift towards a more closed ecosystem. Recommended read:
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anket.sah@lambda.ai (Anket@lambdalabs.com
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lambda.ai
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DeepSeek's latest model, R1-0528, is now available on Lambda’s Inference API, marking an upgrade to the original R1 model released in January 2025. The new model, built upon the deepseek_v3 architecture, boasts a blend of mathematical capabilities, code generation finesse, and reasoning depth, aiming to challenge the dominance of OpenAI’s o3 and Google’s Gemini 2.5 Pro. DeepSeek-R1-0528 employs FP8 quantization, enhancing its ability to handle complex computations efficiently and features a mixture-of-experts (MoE) model with multi-headed latent attention (MLA) and multi-token prediction (MTP), enabling efficient handling of complex reasoning tasks.
DeepSeek-R1-0528, while a solid upgrade, didn't generate the same excitement as the initial R1 release. When R1 was released in January 2025, it was seen as a watershed moment for the company. This time around, it's considered a solid model for its price and status as an open model, and is best suited for tasks that align with its specific strengths. The initial DeepSeek release created a "DeepSeek moment", leading to market reactions and comparisons to other models. The first R1 model was released with a free app featuring a clear design and visible chain-of-thought, which forced other labs to follow suit. While DeepSeek R1-0528 offers advantages, experts warn of potential risks associated with open-source AI models. Cisco issued a report shortly after R1 began dominating headlines which claimed DeepSeek failed to block a single harmful prompt when tested against 50 random prompts taken from the HarmBench dataset. These risks include potential misuse for cyber threats, spread of misinformation, and reinforcement of biases. There are concerns regarding data poisoning, where compromised training data could lead to biased or disinformation. Furthermore, adversaries could modify the models to bypass controls, generate harmful content, or embed backdoors for exploitation. Recommended read:
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@medium.com
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DeepSeek's latest AI model, R1-0528, is making waves in the AI community due to its impressive performance in math and reasoning tasks. This new model, despite having a similar name to its predecessor, boasts a completely different architecture and performance profile, marking a significant leap forward. DeepSeek R1-0528 has demonstrated "unprecedented levels of demand" shooting to the top of the App Store past closed model rivals and overloading their API with unprecedented levels of demand to the point that they actually had to stop accepting payments.
The most notable improvement in DeepSeek R1-0528 is its mathematical reasoning capabilities. On the AIME 2025 test, the model's accuracy increased from 70% to 87.5%, surpassing Gemini 2.5 Pro and putting it in close competition with OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model using significantly more tokens per question, engaging in more thorough chains of reasoning. This means the model can check its own work, recognize errors, and course-correct during problem-solving. DeepSeek's success is challenging established closed models and driving competition in the AI landscape. DeepSeek-R1-0528 continues to utilize a Mixture-of-Experts (MoE) architecture, now scaled up to an enormous size. This sparse activation allows for powerful specialized expertise in different coding domains while maintaining efficiency. The context also continues to remain at 128k (with RoPE scaling or other improvements capable of extending it further.) The rise of DeepSeek is underscored by its performance benchmarks, which show it outperforming some of the industry’s leading models, including OpenAI’s ChatGPT. Furthermore, the release of a distilled variant, R1-0528-Qwen3-8B, ensures broad accessibility of this powerful technology. Recommended read:
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@www.marktechpost.com
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DeepSeek, a Chinese AI startup, has launched an updated version of its R1 reasoning AI model, named DeepSeek-R1-0528. This new iteration brings the open-source model near parity with proprietary paid models like OpenAI’s o3 and Google’s Gemini 2.5 Pro in terms of reasoning capabilities. The model is released under the permissive MIT License, enabling commercial use and customization, marking a commitment to open-source AI development. The model's weights and documentation are available on Hugging Face, facilitating local deployment and API integration.
The DeepSeek-R1-0528 update introduces substantial enhancements in the model's ability to handle complex reasoning tasks across various domains, including mathematics, science, business, and programming. DeepSeek attributes these improvements to leveraging increased computational resources and applying algorithmic optimizations in post-training. Notably, the accuracy on the AIME 2025 test has surged from 70% to 87.5%, demonstrating deeper reasoning processes with an average of 23,000 tokens per question, compared to the previous version's 12,000 tokens. Alongside enhanced reasoning, the updated R1 model boasts a reduced hallucination rate, which contributes to more reliable and consistent output. Code generation performance has also seen a boost, positioning it as a strong contender in the open-source AI landscape. DeepSeek provides instructions on its GitHub repository for those interested in running the model locally and encourages community feedback and questions. The company aims to provide accessible AI solutions, underscored by the availability of a distilled version of R1-0528, DeepSeek-R1-0528-Qwen3-8B, designed for efficient single-GPU operation. Recommended read:
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@www.marktechpost.com
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DeepSeek has released a major update to its R1 reasoning model, dubbed DeepSeek-R1-0528, marking a significant step forward in open-source AI. The update boasts enhanced performance in complex reasoning, mathematics, and coding, positioning it as a strong competitor to leading commercial models like OpenAI's o3 and Google's Gemini 2.5 Pro. The model's weights, training recipes, and comprehensive documentation are openly available under the MIT license, fostering transparency and community-driven innovation. This release allows researchers, developers, and businesses to access cutting-edge AI capabilities without the constraints of closed ecosystems or expensive subscriptions.
The DeepSeek-R1-0528 update brings several core improvements. The model's parameter count has increased from 671 billion to 685 billion, enabling it to process and store more intricate patterns. Enhanced chain-of-thought layers deepen the model's reasoning capabilities, making it more reliable in handling multi-step logic problems. Post-training optimizations have also been applied to reduce hallucinations and improve output stability. In practical terms, the update introduces JSON outputs, native function calling, and simplified system prompts, all designed to streamline real-world deployment and enhance the developer experience. Specifically, DeepSeek R1-0528 demonstrates a remarkable leap in mathematical reasoning. On the AIME 2025 test, its accuracy improved from 70% to an impressive 87.5%, rivaling OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model now utilizing significantly more tokens per question, indicating more thorough and systematic logical analysis. The open-source nature of DeepSeek-R1-0528 empowers users to fine-tune and adapt the model to their specific needs, fostering further innovation and advancements within the AI community. Recommended read:
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