---
license: apache-2.0
base_model: DJLougen/Qwable-5-27B-Coder
tags:
  - code
  - agentic
  - distillation
  - demonstration
  - gguf
  - quantized
language:
  - en
pipeline_tag: text-generation
---

# Qwable-5-27B-Coder-GGUF

GGUF quantizations of [DJLougen/Qwable-5-27B-Coder](https://huggingface.co/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](https://huggingface.co/DJLougen/Qwable-5-27B-Coder). 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