News from the AI & ML world
DeeperML - #alibaba
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@bdtechtalks.com
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Alibaba has recently launched Qwen-32B, a new reasoning model, which demonstrates performance levels on par with DeepMind's R1 model. This development signifies a notable achievement in the field of AI, particularly for smaller models. The Qwen team showcased that reinforcement learning on a strong base model can unlock reasoning capabilities for smaller models that enhances their performance to be on par with giant models.
Qwen-32B not only matches but also surpasses models like DeepSeek-R1 and OpenAI's o1-mini across key industry benchmarks, including AIME24, LiveBench, and BFCL. This is significant because Qwen-32B achieves this level of performance with only approximately 5% of the parameters used by DeepSeek-R1, resulting in lower inference costs without compromising on quality or capability. Groq is offering developers the ability to build FAST with Qwen QwQ 32B on GroqCloud™, running the 32B parameter model at ~400 T/s. This model is proving to be very competitive in reasoning benchmarks and is one of the top open source models being used. The Qwen-32B model was explicitly designed for tool use and adapting its reasoning based on environmental feedback, which is a huge win for AI agents that need to reason, plan, and adapt based on context (outperforms R1 and o1-mini on the Berkeley Function Calling Leaderboard). With these capabilities, Qwen-32B shows that RL on a strong base model can unlock reasoning capabilities for smaller models that enhances their performance to be on par with giant models. Recommended read:
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@bdtechtalks.com
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Alibaba's Qwen team has unveiled QwQ-32B, a 32-billion-parameter reasoning model that rivals much larger AI models in problem-solving capabilities. This development highlights the potential of reinforcement learning (RL) in enhancing AI performance. QwQ-32B excels in mathematics, coding, and scientific reasoning tasks, outperforming models like DeepSeek-R1 (671B parameters) and OpenAI's o1-mini, despite its significantly smaller size. Its effectiveness lies in a multi-stage RL training approach, demonstrating the ability of smaller models with scaled reinforcement learning to match or surpass the performance of giant models.
The QwQ-32B is not only competitive in performance but also offers practical advantages. It is available as open-weight under an Apache 2.0 license, allowing businesses to customize and deploy it without restrictions. Additionally, QwQ-32B requires significantly less computational power, running on a single high-end GPU compared to the multi-GPU setups needed for larger models like DeepSeek-R1. This combination of performance, accessibility, and efficiency positions QwQ-32B as a valuable resource for the AI community and enterprises seeking to leverage advanced reasoning capabilities. Recommended read:
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Ryan Daws@AI News
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Alibaba's Qwen team has launched QwQ-32B, a 32-billion parameter AI model, designed to rival the performance of much larger models like DeepSeek-R1, which has 671 billion parameters. This new model highlights the effectiveness of scaling Reinforcement Learning (RL) on robust foundation models. QwQ-32B leverages continuous RL scaling to demonstrate significant improvements in areas like mathematical reasoning and coding proficiency.
The Qwen team successfully integrated agent capabilities into the reasoning model, allowing it to think critically, use tools, and adapt its reasoning based on environmental feedback. The model has been evaluated across a range of benchmarks, including AIME24, LiveCodeBench, LiveBench, IFEval, and BFCL, designed to assess its mathematical reasoning, coding proficiency, and general problem-solving capabilities. QwQ-32B is available as open-weight on Hugging Face and on ModelScope under an Apache 2.0 license, allowing for both commercial and research uses. Recommended read:
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Ryan Daws@AI News
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Alibaba's Qwen team has introduced QwQ-32B, a 32 billion parameter AI model that rivals the performance of the much larger DeepSeek-R1. This achievement showcases the potential of scaling Reinforcement Learning (RL) on robust foundation models. The Qwen team has successfully integrated agent capabilities into the reasoning model, enabling it to think critically and utilize tools. This highlights that scaled reinforcement learning can lead to significant advancements in AI performance without necessarily requiring immense computational resources.
QwQ-32B demonstrates that RL scaling can dramatically enhance model intelligence without requiring massive parameter counts. QwQ-32B leverages RL techniques through a reward-based, multi-stage training process. This enables deeper reasoning capabilities, typically associated with much larger models. QwQ-32B has achieved performance comparable to DeepSeek-R1 which underscores the potential of RL to bridge the gap between model size and performance. Recommended read:
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@oodaloop.com
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Alibaba's CEO, Eddie Wu, has announced that the company's primary objective is now the pursuit of Artificial General Intelligence (AGI). This strategic shift was communicated to investors during an earnings call where Wu emphasized that Alibaba aims to develop models that "extend the boundaries of intelligence." The decision highlights the increasing influence of AI within the technology sector, particularly in the context of Alibaba's traditional e-commerce business, which includes services like AliExpress and Taobao.
This focus on AGI, a type of AI that matches or surpasses human cognitive capabilities, signifies Alibaba's commitment to innovation in the rapidly evolving AI landscape. Wu believes that if AGI is achieved, the AI industry could potentially become the world's largest. The company has already been active in AI development with its Qwen large language models, with the latest version unveiled in January. Alibaba's revenue has seen an 8% year-over-year increase, marking progress in its AI-driven strategies. Recommended read:
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