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DeeperML - #deepseek
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Google is enhancing its AI Hypercomputer with optimized recipes designed to streamline the deployment of large AI models like Meta's Llama4 and DeepSeek. This move aims to alleviate the resource-intensive challenges faced by developers and ML engineers when working with these advanced models. The new recipes will facilitate the use of Llama4 Scout and Maverick models, as well as DeepSeek models, on Google Cloud Trillium TPUs and A3 Mega/Ultra GPUs, making these powerful AI tools more accessible and efficient to deploy.
JetStream, Google’s high-throughput inference engine for LLMs on XLA devices, now supports Llama-4-Scout-17B-16E and Llama-4-Maverick-17B-128E inference on Trillium TPUs. New recipes provide steps to deploy these models using JetStream and MaxText on a Trillium TPU GKE cluster. Pathways on Google Cloud simplifies large-scale machine learning computations by enabling a single JAX client to orchestrate workloads across multiple large TPU slices. MaxText now features reference implementations for Llama4 and DeepSeek, offering detailed guidance on checkpoint conversion, training, and decoding processes. Developers can find these new recipes and resources on the AI Hypercomputer GitHub repository. These optimized recipes promise to simplify the deployment and resource management of Llama4 and DeepSeek models, enabling users to harness the full potential of these advanced AI technologies on Google Cloud's AI Hypercomputer platform. This initiative underscores Google's commitment to providing a robust AI infrastructure and fostering innovation in the open-source AI community. 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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@pub.towardsai.net
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pub.towardsai.net
DeepSeek's R1 model is garnering attention as a potential game-changer for entrepreneurs, offering advancements in "reasoning per dollar." This refers to the amount of reasoning power one can obtain for each dollar spent, potentially unlocking opportunities previously deemed too expensive or technologically challenging. The model's high-reasoning capabilities at a reasonable cost are seen as a way to make advanced AI more accessible, particularly for tasks that require deep understanding and synthesis of information. One example is the creation of sophisticated AI-powered tools, like a "lawyer agent" that can review contracts, which were once cost-prohibitive.
The DeepSeek R1 model has been updated and released on Hugging Face, reportedly featuring significant changes and improvements. The update comes amidst both excitement and apprehension regarding the model's capabilities. While the model demonstrates promise in areas like content generation and customer support, concerns exist regarding potential political bias and censorship. This stems from observations of alleged Chinese government influence in the model's system instructions, which may impact the neutrality of generated content. The adoption of DeepSeek R1 requires careful self-assessment by businesses and individuals, weighing its strengths and potential drawbacks against specific needs and values. Users must consider the model's alignment with their data governance, privacy requirements, and ethical principles. For instance, while the model's content generation capabilities are strong, some categories might be censored or skewed by built-in constraints. Similarly, its chatbot integration may lead to heavily filtered replies, raising concerns about alignment with corporate values. Therefore, it is essential to be comfortable with the possible official or heavily filtered replies, and to consider monitoring the AI's responses to ensure they align with the business' values. Recommended read:
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