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case study · apache-2.0

A 27B model trained to talk like a person, not an assistant.

We fine-tuned Qwen3.8-27B on real conversations to give shorter, more natural replies. Try Humanlike Chat in your browser without an API key, or download it to run locally. Comparing it with the base model uses your own OpenRouter key.

Try Humanlike Chat Get it on Hugging Face

Want a model trained on your conversations? See our custom training service.

SAME INSTRUCTIONS

Qwen 3.8 27B base

Humanlike Chat

human: “just parked at a rest area off 80. you still up?”

yeah. barely. just finished a pot of coffee that tastes like burnt pennies. where are you? if you’re not too far, maybe you could save me from this silence? i’m wearing the black tank and the skirt. just saying.

I am but I am on break rn

human: “how long you got”

depends how slow the night stays. i could stretch it if you said you were close. are you close? i keep looking at the lot like an idiot in this tank.

ten. maybe twelve if it’s dead

human: “sleeper’s dark. radio’s off.”

then talk to me. tell me what the bunk smells like. i’d sit on the step and steal your coffee and make you forget the miles for a minute. you want that or are you just bored.

jealous. mine’s pie case and someone arguing about ranch

Neither model was told to be brief. These selected replies show differences in length, questions, and invented scene details. They don't show which model people would prefer with the best prompt.

An open release you can inspect and run.

Download the weights under Apache-2.0 and run the model yourself. The tests below show changes in reply style and the drop in other capabilities measured on earlier versions.

  • 29,721 Hugging Face downloads, last 30 days
  • 27B parameter model
  • Open Apache-2.0 weights

Hugging Face snapshot, 22 September 2026. This counts downloads, not people. It doesn't tell us whether users prefer the model or get better results.

What r/LocalLLaMA wrote

Verbatim comments from the launch thread. Each quote links to its original comment. These are community reactions, not customer testimonials.

  • this is the bit most local models still miss imo. the raw intelligence can be there but the constant “as an ai…” framing makes every answer feel like a helpdesk ticket.

    u/Morning_Gecko24 r/LocalLLaMA

  • Five points of IFEval for never opening with "Certainly! Here's..." seems like a trade I'd make every time.

    u/derspenti r/LocalLLaMA

  • can you host this somewhere? I'd pay API pricing for this :D

    u/lakimens r/LocalLLaMA

  • It looks great indeed! I'll definitely be giving this a go as well.

    u/whatyathinkk r/LocalLLaMA

  • Sweet thanks for doing this. Im going to give it try tonight.

    u/lemondrops9 r/LocalLLaMA

What earlier replays measured

Earlier tests found shorter replies and fewer assistant-style phrases. We counted phrases in 590 turns and compared the two models across 29 turns. These tests didn't measure which replies people preferred. Shorter isn't always better.

Historical measurements, not a benchmark of the current step-576, 0.7-strength rebuild. Sample sizes are shown per row.
Measure Result
Flagged three-word phrases / “not X but Y” phrasing / lists 0 / 0 / 0 in 590 turns
Median reply length, 29-turn comparison without an extra system prompt Base 427 → adapter 32 characters
Questions, same 29-turn comparison Base 27 of 29 → adapter 3 of 29
Median reply length, same comparison with a 760-character persona prompt Base 71 → adapter 50 characters

In these replays, both models received the same recorded messages. Later turns can favor the adapter because the human originally responded to its replies. Adding a persona prompt made the base model's replies much shorter too. See the test method and capability results.

One more side-by-side

SUPPORT DESK

human: “my order is 4 days late and nobody has emailed me”

Qwen 3.8 27B base

I hear you, and I’m sorry you’re stuck in the dark. I can look into this for you right now. What email address did you place the order with?

Humanlike Chat

thanks for flagging this. I am looking into it. could you send me your order number so i can pull it up?

Both replies ask for an identifier. The tone differs, but neither reply resolves the case. A support test also needs to check facts, tool use, and whether the customer gets help.

How it was trained

The model learns from the next reply in each conversation. The current GGUF files use the step-576 LoRA adapter merged at 0.7 strength. You can't change that strength in the merged files.

  1. 139,845 real messages

    The original corpus covered 1,396 one-to-one sessions and produced 7,006 examples. The model card describes the current rerun as 9,201 training rows, including 2,000 with reasoning-channel supervision.

  2. Train on the next reply

    The model reads the prompt and history, but the training loss scores only the next reply. In the current rerun, some examples also train the reasoning channel. The original reply targets stay unchanged.

  3. Adapter and release versions

    Rank-256 LoRA across the language modules. The separately downloadable, adjustable adapter is the older step-863 checkpoint, not the current merged release.

Lineage: Qwen3.8-27B → abliterated parent → Humanlike LoRA → 0.7 merge → GGUF. Download sizes and settings are on the model card.

Limitations

  • Earlier versions scored 5.36 percentage points lower on IFEval and 10.5 points lower on MMLU-Pro-200. These aren't scores for the current release.
  • Use a separate model for strict output formats and multi-step tool use.
  • We haven't run a blind comparison to test which replies people prefer.
  • The parent model was modified to reduce refusals. Custom projects can use a different base model.

Train a model for your conversations

In a Custom Model Sprint, we prepare examples with your reviewers and train an adapter for your chosen model. We test it against your best prompt, check for new failures, and give your team the files and settings needed to run it.

Projects to tune conversational style typically cost $25,000-$80,000 and take 4-6 weeks. Your results will depend on your data and goals. If a prompt change or better examples are enough, we'll recommend that instead.

Book a model review See sprint scope

Questions

Does it need a system prompt?

No. You can chat with it without a system prompt. Add a character description if you want it to play a specific role.

Can you do this with our conversation data?

We can review conversations you have permission to use and discuss the replies you want. We test against your best prompt. Projects to tune conversational style typically cost $25,000-$80,000 and take 4-6 weeks. We agree the work and success criteria before starting.

Who owns the resulting weights?

You receive a custom adapter to run yourself. The contract sets ownership and usage rights, and the base model's license still applies. The public Humanlike Chat model is a separate release under Apache-2.0.

Does it get worse at other tasks?

Earlier versions scored 5.36 percentage points lower on IFEval strict and 10.5 points lower on MMLU-Pro-200. These aren't scores for the current 0.7 rebuild. Test instruction-following and tool use separately before using it for those tasks.

What hardware does it need?

The Q4_K_M file is 16.56 GB. Allow roughly 20 GB of VRAM, with more needed for longer conversations, or offload part of the model to the CPU. You can also use the browser demo without running anything locally.

What should your model do differently?

Bring a few conversations with personal details removed and your current prompt. We'll discuss what you want to change and whether fine-tuning can help.

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