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English(EN) I tried MIT's "weight-space programming" on my own use case: Claude rewrote 8 neurons of Gemma 4 12B to change how it translates one Italian tense

Claude Opus 5.5 编辑 Gemma 4 12B 权重以改进意大利语翻译

一位用户成功复制了麻省理工学院的权重空间编程技术,以修改 Gemma 4 12B 模型在意大利语翻译方面的能力。用户使用 Claude Opus 5.5 作为编辑代理,仅更改了 Gemma 权重中的 8 个神经元,以优化其处理意大利语特定动词时态的方式。这种有针对性的编辑显著提高了所需时态用法的翻译准确性,对不相关的句子影响很小,优于提示工程或 LoRA 微调等其他方法。 AI

影响 展示了一种新颖的、参数数量少的方法,可以在没有传统训练的情况下微调 LLM 的行为。

排序理由 用户驱动的在特定模型上复制研究技术。[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 →

Claude Opus 5.5 编辑 Gemma 4 12B 权重以改进意大利语翻译

本文如何被排名

Signal score
2 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
用户驱动的在特定模型上复制研究技术。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/FeydRowan ·

    我尝试了麻省理工学院的“权重空间编程”来解决自己的用例:Claude 重写了 Gemma 4 12B 的 8 个神经元,以改变其意大利语时态的翻译方式

    <!-- SC_OFF --><div class="md"><p>Yesterday MIT CSAIL showed GPT-6 Astra editing the weights of a small model by hand. It changed a few neurons of Qwen3.5-0.8B, the model stopped looping on math problems, and its scores jumped. No training at all.</p> <p>I wanted to try the same …