A Qwen 3.8 27B tune made to sound human — and to stop writing like an assistant

On September 11, a post on r/LocalLLaMA introduced a model built by someone openly tired of talking to assistants. Qwen3.8-27B-Humanlike-Chat drew 602 points and 192 comments, and its author, the account behind the LessThanThreeAI project, states the motive plainly: "I made this because I was getting genuinely annoyed at trying to have a normal conversation with LLMs. Even with prompting and various tricks, most models I've tried still have this 'AI assistant' vibe to them that is so familiar: too helpful, polished, verbose, using words we never use in conversation."

Changing habits, not scores

What the author wanted was register, not reasoning. "The goal wasn't to make Qwen smarter or improve benchmark scores. I was trying to change its conversational habits, to make it stop turning every reply into an explanation, agreeing with everything, and writing stuff just to keep the conversation 'going.'" The method was a rank-256 LoRA trained on 125,217 "obfuscated human-to-human messages" across 1,396 chat conversations. The release is unusually complete for a community tune. Its own metadata frames it:

• Base: huihui-ai/Huihui-Qwen3.8-27B-abliterated, released checkpoint 863
• Formats: merged GGUFs (BF16 in two shards, plus Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0) and a standalone F32 LoRA adapter
• Context and license: 262,144 tokens, apache-2.0
• Adoption: 12,311 downloads, 34 likes, roughly 443 GB of repository storage, with FINDINGS.md, EXAMPLES.md and SERVING.md beside the weights
• Demo: a Hugging Face Space and a free rate-limited OpenAI-compatible endpoint serving the id qwen3.8-27b-humanlike-chat

The number nobody has

The author does not claim the tune got smarter. The only benchmark figure attached to the project is a loss. "There may be a tradeoff," he writes. "An earlier iteration scored five percentage points lower than its Huihui parent on IFEval, an instruction-following benchmark. I haven't rerun that benchmark on this version of the checkpoint, and I haven't tested coding performance, so I don't want to pretend that number applies here." Nobody else has measured it. A rank-256 LoRA on an abliterated base also inherits that base's refusal behaviour, so "humanlike" describes conversational register, not capability and not safety. The dataset's "obfuscated" provenance raises a consent question the post never answers, and some of the demo's charm may come from its system prompt and reasoning effort rather than from the weights. Meanwhile adoption is the only hype proxy: roughly 600 Reddit points against 12,311 downloads and 34 likes.

What would settle it

Checkpoint 863 is a snapshot of an ongoing loop, so the author's caveats are the release notes. Re-running IFEval and a coding eval on this checkpoint, and naming where the messages came from, would turn a likable demo into a measurable one. Until that happens, anyone weighing the download should note the same arithmetic that governed last week's half-terabyte argument: charming weights still have to fit somewhere.