Qwable-5-27B-Coder-NVFP4
NVFP4 quantization of DJLougen/Qwable-5-27B-Coder.
Update (2026-06-22): Read the base model card before using this. 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
An NVFP4 build 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.
The release demonstrates how little it takes to make a model look credible through framing alone. This quant exists so the demonstration reaches people running FP4 on supported NVIDIA hardware.
Why this exists
See the base model card. As local AI grows, the community has to reward measured evidence over hype, buzzword names, and impressive teacher names. This is a worked example of the failure mode, released so the reveal makes the point concretely.
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 format/runtime details you shipped.
| Field | Value |
|---|---|
| Format | NVFP4 |
| Target hardware | TBD (e.g. Blackwell FP4) |
| Runtime | TBD |
| Approx size | TBD |
FP4 quantization compounds the caveat on the base card: at n=10 the behavioral delta over base is already narrow and underdetermined, and 4-bit 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