Update (2026-06-22): Read the base model card before using these. The original release was deliberately under-documented as part of a point about hype versus evidence in local AI. The full recipe and rationale are now on the base card.

What this actually is

GGUF builds of a Qwen3.6-27B base that was post-trained on 10 traces total (5 from a Fable 5 dataset, 5 generated by Kimi 2.7 Coder) in roughly 3 minutes on a single DGX Spark. That is the entire recipe.

It was released to demonstrate how little work it takes to make a model look credible through framing alone, and these quants exist so the demonstration reaches the people who run local in llama.cpp / Ollama / LM Studio.

Why this exists

See the base model card. Short version: as local AI grows, the community has to reward measured evidence over hype, buzzword names, and impressive teacher names. This release is a worked example of the failure mode.

What you should actually do

Test it yourself rather than trusting the card or the teacher names.
Demand real evals: data volume and methodology, not just "distilled from {impressive model}."
Be skeptical of version-numbered names and benchmark-maxxing.
Prefer reproducible, hardware-specific open evals.

Intended use

Educational and illustrative. Not recommended for production coding. No methodology-backed benchmark numbers are provided, by design.

Quantization notes

Fill in the exact quant types you shipped.

Quant	Approx size	Notes
Q4_K_M	TBD
Q5_K_M	TBD
Q6_K	TBD
Q8_0	TBD

Quantization further compounds the caveat on the base card: at n=10 the behavioral delta over base is already narrow and underdetermined, and low-bit quants will shift it further. Do not generalize any apparent strength.

Attribution

Base model: Qwen3.6-27B (see its card for license and terms)
Fine-tune: DJLougen/Qwable-5-27B-Coder
Seed data: Fable 5 dataset, Kimi 2.7 Coder generations

DJLougen/Qwable-5-27B-Coder-GGUF

Qwable-5-27B-Coder-GGUF