【Hacker News搬运】赞比亚2-7B

[hackernews]

Title: Zamba2-7B

赞比亚2-7B

Text:

hn link

Url: https://www.zyphra.com/post/zamba2-7b

很抱歉，作为一个AI，我无法直接访问外部网站，如您提供的链接。因此，我无法直接抓取或分析该网站的内容。

不过，如果您能提供该网页的内容或者其关键信息，我可以帮助您进行分析和总结。如果您需要帮助将非中文内容翻译成中文，并在此基础上进行分析和总结，您可以将内容粘贴在这里，我会尽力帮助您。

Post by: dataminer

Comments:

jwitthuhn: For anyone else looking for the weights which as far as I can tell are not linked in the article:<p>Base model: <a href="https://huggingface.co/Zyphra/Zamba2-7B" rel="nofollow">https://huggingface.co/Zyphra/Zamba2-7B</a><p>Instruct tuned: <a href="https://huggingface.co/Zyphra/Zamba2-7B-Instruct" rel="nofollow">https://huggingface.co/Zyphra/Zamba2-7B-Instruct</a>

jwitthuhn: 对于任何其他正在寻找权重的人，据我所知，这些权重在文章中没有链接：<p>基本模型：<a href=“https:”huggingface.co“Zyphra”Zamba2-7B“rel=”nofollow“>https:”&#x2F；huggingface.co；Zyphra；Zamba2-7B</a><p>指令已调整：<a href=“https:&#x2F；huggingface.co&#x2M；Zyphra&#x2O；Zamba2-7B-指令”rel=“nofollow”>https:&#x2F；huggingface.co；Zyphra；Zamba2-7B说明书</a>

potatoman22: I wonder how much of the performance gains can be attributed to their improved dataset rather than their architecture. That would be an expensive experiment.

potatoman22: 我想知道性能的提高在多大程度上归功于他们改进的数据集，而不是他们的架构。这将是一个昂贵的实验。

adt: <a href="https://lifearchitect.ai/models-table/" rel="nofollow">https://lifearchitect.ai/models-table/</a>

adt: <a href=“https:”lifearchitect.ai“models table”rel=“nofollow”>https:”&#x2F；生命建筑师.ai；型号表</一

arnaudsm: I'm tired of LLM releases that cherry pick benchmarks. How does it compare to SOTA qwen2.5/phi3.5 ?<p>Anyone knows an up to date independent leaderboard? Lmsys and livebench used to be great but skipped most major models recently.

arnaudsm: 我；我厌倦了LLM发布的挑挑拣拣基准测试。它与SOTA qwen2.5相比如何；phi3.5<p> 有人知道最新的独立排行榜吗？Lmsys和livebench曾经很棒，但最近跳过了大多数主要型号。

SubiculumCode: When they say that they use two attention heads, are each attention head directed at different aspects of the data?<p>In memory research there is this idea that there is a dual representation of every event...a more verbatim representation, and more context weighted representation. As we develop over early childhood, our verbatim memory representations increase in fidelity and strength against interference, but peaks around 6 to 10 years, depending on the specifics. As this verbatim memory matures, another aspect of memory representations improves: some have called it gist memory, or semantic context. Increases in memory performance continue into adolescence primarily due to increases in the ability to use context and gist (broad representations that capture the details by inference or an event) to increase accuracy overall, but also greater likelihood of committing false alarms to lures primed by semantically related material during learning...expressly because there becomes greater reliance on context to support recall accuracy.<p>So I could imagine such a system in a LLM where attention is directed to exact representations in one head, and another that keeps its attention on a coarser grain of information that anchors information. However, I am not that familiar with LLMs to know if that is just silly analogizing.

SubiculumCode: 当他们说他们使用两个注意力头时，每个注意力头都指向数据的不同方面吗<p> 在记忆研究中，有一种观点认为，每个事件都有双重表征。。。更逐字的表示和更上下文加权的表示。随着我们在幼儿时期的发展，我们的逐字记忆表现在保真度和抗干扰能力方面都有所提高，但根据具体情况，在6到10年左右达到峰值。随着这种逐字记忆的成熟，记忆表征的另一个方面得到了改善：有些人称之为要点记忆或语义上下文。记忆表现的提高一直持续到青春期，主要是由于使用上下文和要点（通过推理或事件捕捉细节的广义表示）的能力提高，以提高整体准确性，但在学习过程中，对语义相关材料引发的诱饵发出假警报的可能性也更大。。。这显然是因为越来越依赖于上下文来支持回忆准确性<p> 因此，我可以想象在LLM中有一个这样的系统，在这个系统中，注意力被引导到一个头脑中的精确表示，而另一个头脑则将注意力集中在锚定信息的更粗的信息上。然而，我对法学硕士不太熟悉，不知道这是否只是愚蠢的类比。