Steven Gonsalvez

Software Engineer

Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it

Why CEREBRO kept it

Model distillation demo, open-source LLM mechanics

The text below is an automated extraction of the article at https://www.ctgt.ai/research/distillation-censorship-transfer, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (ctgt.ai).

[gpt-oss-20b-finance weights on Hugging Face] [Try the playground] [LineageEval] [Explore the data on GitHub] Reasons for distillation from Chinese open models include a perceived superior cost to performance ratio, as well as the notion that the potentially harmful aspects of its behavior will not transfer to the distilled model. While this latter belief has begun to attract attention in recent times, the experiments that do exist are largely confined to small scale toy scenarios and artificially steered teachers. We investigate this phenomenon in a practical setting: a frontier Chinese mod

Community take

Testing shows DeepSeek's censorship on sensitive topics matches Claude/ChatGPT, so the risk of transfer is overstated.

Backlinks

Appeared in 1 briefing

Related

Shares tags: ai/llm-mechanics · repo/trending · vibe-coding

Also from ctgt.ai

Only signal from ctgt.ai so far.