原贴链接

帖子仅包含一个图片链接，无实质可翻译内容

讨论总结

此讨论源于Deepseek V3基准测试相关话题，还涉及Qwen 2.5 72B。大家对不同模型的性能、潜力等方面表达了看法，包括对Deepseek V3 Lite能力的期待、模型发展方向的质疑，也提到了开放研发的重要性，整体讨论氛围比较理性，大家各抒己见。

主要观点

  1. 👍 对Deepseek V3 Lite的能力表示期待
    • 支持理由：Deepseek V3基准测试结果让部分人对V3 Lite能力好奇
    • 反对声音：无
  2. 🔥 强调竞争和开放研发促使新事物发展
    • 正方观点：竞争与开放研发使各方受益，类似微型黄金时代或文艺复兴
    • 反方观点：无
  3. 💡 认为Qwen2.5 - 72B在自己使用场景下非常优秀，无视基准测试
    • 解释：通过特定测试，在编码等查询中表现最佳，在特定配置下运行效果好，基准测试与自己使用场景无关
  4. 🤔 DeepSeek V3性能提升幅度小但规模增加过大
    • 正方观点：从数据上看性能提升和规模增加不成正比
    • 反方观点：MoE模型有特殊用途，总参数多不代表激活参数多，性能有提升且能服务多用户
  5. 😕 MoE模型总是表现更差
    • 正方观点：认为除基准测试外综合性能差
    • 反方观点：无

金句与有趣评论

  1. “😂 Of course, everyone is waiting for an AGI model with 1B parameters that you can run on Raspberry Pi at 100 tk/s! LOL”
    • 亮点：以幽默调侃的方式表达对特定参数AGI模型在树莓派高效运行的期待
  2. “🤔 Seriously, 10 - 20% increase in performance but at a cost of 500 - 1000% increase in size? That’s bullshit.”
    • 亮点：简洁指出DeepSeek V3性能提升与规模增加的不合理之处
  3. “👀 Honestly, Qwen2.5 - 72B is so good I simply ignore all benchmarks now because outputting a preset of problem solutions which have absolutely no relation to how I use these models is irrelevant to me.”
    • 亮点：强调在自身使用场景下Qwen2.5 - 72B优秀到可无视基准测试
  4. “😎 While your point (the relative performance vs. model size) of some models is outstanding is very true, it’s also worth tactfully noting that in many ways it is the very PRESENCE of "competition" and "open R&D" that enables some new / much better things to "evolve" (standing on the shoulders of giants) from the research / effort / experiments others have done to push the boundaries of whatever – compute, amount of training data, model size, model architecture, etc.”
    • 亮点：阐述竞争和开放研发在模型发展中的重要意义
  5. “💡 You’re basically just cramming data into parameters.”
    • 亮点：对向参数填塞数据的做法提出质疑

情感分析

总体情感倾向为理性探讨，主要分歧点在于对不同模型性能、发展方向以及特定模型类型（如MoE模型）的看法。可能原因是大家使用模型的场景、关注重点不同，例如有的关注本地运行，有的关注服务器运行或基准测试结果等。

趋势与预测

  • 新兴话题：不同模型与o1的比较可能成为后续讨论点。
  • 潜在影响：对模型研发方向可能产生影响，促使开发者考虑不同的性能提升方式、参数利用效率等，也会影响用户对不同模型的选择倾向。

详细内容：

标题：Reddit 热议 Deepseek V3 与 Qwen 2.5 72B 模型性能

在 Reddit 上，一则关于“Deepseek V3 benchmarks are a reminder that Qwen 2.5 72B is the real king and everyone else is joking!”的帖子引发了广泛关注，获得了众多点赞和大量评论。该帖子主要围绕 Deepseek V3 和 Qwen 2.5 72B 这两个模型的性能展开讨论，涉及模型大小、运行效率、适用场景等多个方面。

讨论焦点与观点分析： 有人认为 Deepseek V3 具有很大的潜力，但也有人质疑其实际应用价值。比如，有人说“Tbh this makes me more interested in what a Deepseek V3 Lite will be capable of”，但也有人回应“you should not assume it will ever exist. V2 lite was a research artifact for testing the MLA+MoE design, not a gift to the community. They learned enough then, probably.” 有人指出 70 - 100/123 B 范围的模型仍有巨大潜力，例如“ The 70 - 100/123 B range still has huge untapped potential. More parameters than that is wasteful”。 有用户分享了自己的使用体验，“I’ve got subscriptions to mistral, deepseek v3 and a bunch of local models. CONSISTENTLY Qwen2.5 - 72B is the top model for my query involving Rails, Tailwind, Vue, modern coding practices, suggestions for improvements.”，还有人表示“ I’ve been using Qwen 2.5 - 72B and it has improve my throughput for simple coding task a lot quicker.” 对于模型的运行条件，存在不同看法。有人认为 Deepseek V3 对硬件要求过高，运行成本高昂，比如“Seriously, 10 - 20% increase in performance but at a cost of 500 - 1000% increase in size? That’s bullshit.”，而有人则解释“DeepSeek V3 has 850% more total parameters than Qwen 2.5 72B, but it actually has 50% less activated parameters. So a big increase in capabilities, while also reducing active parameter count by 50%. That sounds pretty good! ”

讨论中的共识在于大家都在积极探讨模型的优劣，试图找到最适合自己需求的模型。特别有见地的观点如“ It’s kind of like a micro “golden age” / renaissance in that we see some non - academic entities (organizations / businesses) pursuing the same kind of openness of R&D / collaboration / synergy that is usually more often associated with academic / scientific R&D than adversely competitive / closed enterprises so we’ve (overall) made huge progress in just 1..2..10… years.”丰富了讨论的深度和广度。

总之，这次 Reddit 上的讨论充分展现了大家对模型性能的关注和思考，也反映出在技术发展过程中，人们对于如何选择和应用合适模型的探索永无止境。