TBNAI
Oc-e-Non-v1: A language model that gets to the f*cking point
Publication: top scoring links : multi
Author: /u/-p-e-w-
Source published: 06/11/2026, 15:23:37 UTC
Briefing created: 07/11/2026, 04:44:26 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/11/2026, 00:47:26 UTC.
Briefings
Oc-e-Non-v1 is a deliberately terse language model version of Qwen3-4B-Instruct-2507, designed to reduce average response length by 50%. The model outputs concise answers to examples like '1+1' (2) and 'Are dogs mammals?' (Yes), demonstrating a pattern of brevity. It was created by the user /u/-p-e-w- to showcase a 'light-hearted' experiment using the ancient Qwen3-4B model, though the creator notes reasoning effort control is not implemented in Heretic. The post was submitted to r/LocalLLaMA on November 6, 2026, but the specific impact on the model's performance or broader AI discourse remains unverified by the source.
Key points
- Oc-e-Non-v1 is a tersified version of Qwen3-4B-Instruct-2507 that reduces the mean response length by 50%.
- The model outputs concise answers to examples such as '1+1' resulting in '2' and 'Are dogs mammals?' resulting in 'Yes'.
Context
The post was submitted by /u/-p-e-w- to r/LocalLLaMA on November 6, 2026, proposing Oc-e-Non-v1 as a 'shining example' for future LLMs. The creator notes the model uses the ancient Qwen3-4B because reasoning effort control is not implemented in Heretic yet, and the post is intended as a light-hearted demonstration while awaiting Qwen4.
Limitations
The source is a Reddit post from November 2026, making it a preprint with no peer review or independent verification. The creator explicitly states the model is 'intended as a light-hearted post' and that reasoning effort control is not implemented, so the model's actual performance or safety implications are unverified by the evidence.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 07/11/2026, 04:44:26 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
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