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deepseek-v4.1IMAGE

Mac mini 本地跑 11 个开源模型改写葛底斯堡演说 19019 次

作者在 Mac mini 上本地测试 11 个开源权重模型,DeepSeek V4.1 Flash 与 GLM-5.3 Flash 看似稳定,但输出逐渐回归林肯原文,疑似记忆而非稳定性;大模型需从 SSD 流式加载,耗时约 36/45 小时。

Pratik Gandhi
@pratikg2 天前收录原推发布于 2026.10.27
Mac mini 本地跑 11 个开源模型改写葛底斯堡演说 19019 次
Pratik Gandhi @pratikg原推发布于 2026.10.27
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Last week I said I still want models open even though anyone can strip the safety training off. This weekend, running local AI on my Mac mini, 11 open-weight models rewrote the Gettysburg Address 19,019 times and the two biggest kept finding their way back to Lincoln. What I learned: 1) GLM-5.3 Flash from @Zai_org and DeepSeek V4.1 Flash from @deepseek_ai looked rock stable until I read the outputs. 86 of GLM's 1,000 rewrites open with "Four score", and by round 1,000 both were closer to Lincoln's original wording than at round 1. Part of what looked like stability was memory. 2) The small models had nothing to fall back on and drifted off. DeepSeek-V2-Lite turned the 278-word speech into 13 words about advancing your career, and Qwen3.5 0.8B ended up on project planning. I think it's the same pull the big models felt (each one slides toward whatever its training data made most familiar), it just landed on workplace topics instead of Lincoln. 3) A single score would have hidden all of this. I only caught it because I could open the files and read them. 4) Running locally meant the weights never changed mid-run and I paid nothing per rewrite. Anyone with a Mac can rerun it and check my numbers. 5) The two biggest models don't fit in 64GB, so they streamed off my SSD and ate about 36 of the 45 hours. They still ran. Most of the others finished 1,000 rewrites in under an hour. The smallest did 350 tok/s in under 1GB of memory, and Qwen3-Next 80B, an 80B mixture-of-experts, did 73 tok/s in 45GB. 6) Using a famous text baked memorization into the result. The next run adds a text no model could have seen, so I can separate stability from recall. The very reason why I want weights open. A benchmark score wouldn't have shown this and no API would let me run 19,019 rewrites for free. All of it is public, so you can check my work or find what I missed. If you've run long chained tests on your own machine, I'd love to hear what you found.

这条是引用着另一条写的:x.com/pratikg/status/2112217547366088851

x.com/pratikg/status/2114753169070707188采集于 2026.10.27 12:09