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English(EN) After Qwenputer, I wondered if a smaller model could do it too. So I built a Forth interpreter into Nemotron 3.5 Lightning's weights

研究人员将Forth解释器集成到Nemotron 3.5 Lightning模型权重中

作者 PulseAugur 编辑部 · [1 个来源] ·

一位研究人员已将Forth解释器直接集成到NVIDIA的Nemotron 3.5 Lightning模型的权重中，使其能够在生成过程中执行Forth代码。这种受Qwenputer项目启发的创新方法允许模型在没有传统工具调用的情况下执行计算，堆栈操作在Mamba状态内进行管理。虽然该实现是一个研究项目，但由于每一步的计算成本，它尚未达到生产就绪状态，但它展示了一种增强模型能力的独特方法，并已在MIT许可下与社区共享。 AI

影响 展示了一种将计算能力直接集成到LLM权重中的新颖方法，可能影响未来的模型架构。

排序理由 研究项目，展示了将Forth解释器新颖地集成到模型权重中。[lever_c_demoted from research: ic=1 ai=1.0]

在 r/LocalLLaMA 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究人员将Forth解释器集成到Nemotron 3.5 Lightning模型权重中

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研究项目，展示了将Forth解释器新颖地集成到模型权重中。[lever_c_demoted from research: ic=1 ai=1.0]
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  1. r/LocalLLaMA TIER_1 English(EN) · /u/Mr_Finious ·

    继Qwenputer之后，我想知道更小的模型是否也能做到。所以我将Forth解释器植入了Nemotron 3.5 Lightning的权重中

    <!-- SC_OFF --><div class="md"><p>I learned RPN on an HP calculator and later spent more time with Forth than was strictly useful. When I saw Qwenputer compile a machine into a language model's weights, I wanted to know two things. Would a stack language fit that idea well? And c…

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