Qwable-5-27B-Coder-GGUF
GGUF quantizations of DJLougen/Qwable-5-27B-Coder.
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