Revolutionizing AI: Quen's 32 Billion Parameter Model Dominates Coding and Math Benchmarks
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On 1littlecoder, we delve into the world of AI with a 32 billion parameter model from Quen that's turning heads in the tech realm. This David of a model is taking on Goliaths like the Deep Seek R1, a behemoth with 671 billion parameters, and holding its own in coding and math benchmarks. It's like watching a plucky underdog outshine the big shots in a high-stakes showdown.
What sets this model apart is its unique blend of reinforcement learning and traditional fine-tuning methods, a recipe for success in the competitive AI landscape. By using outcome-based rewards and accuracy verifiers for math problems, this model is honing its skills with precision. It's like a sharpshooter hitting the bullseye every time, raising the bar for AI performance.
But it doesn't stop there. The team behind this marvel has implemented a code execution server to ensure that the generated code meets predefined test cases, adding an extra layer of quality control. It's akin to a master craftsman meticulously inspecting every detail of their creation to perfection. And the results speak for themselves, with the model continuously improving in both coding and math through reinforcement learning.
This innovative approach not only enhances the model's performance but also focuses on developing its general capabilities, like instruction following, through a tailored reward model. It's like giving the model a crash course in human preferences and behavior, making it more versatile and adaptable. The team's dedication to pushing the boundaries of AI development is evident in their meticulous process and groundbreaking results, setting a new standard for innovation in the field.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Another Chinese 32B LLM matches Deepseek 671B??!!! on Youtube
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Discussion on the performance of the models
Request for tests against full fp32/fp16 vs quantized versions
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Feedback on model testing and speed on slow hardware
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Reference to Barto and Sutton winning the Turing Award
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