DJLougen/Qwable-5-27B-Coder
DJLougen/Qwable-5-27B-Coder is a 27 billion parameter model based on Qwen3.6-27B, lightly post-trained on 10 traces from Fable 5 and Kimi 2.7 Coder. This model serves primarily as an educational and illustrative example, demonstrating the impact of minimal training data and aggressive marketing in local AI. It is not recommended for production coding tasks, as its behavioral changes are statistically underdetermined and it lacks robust evaluation.
Loading preview...
Overview
DJLougen/Qwable-5-27B-Coder is a 27 billion parameter model built upon the Qwen3.6-27B base. It was post-trained on a very small dataset of just 10 traces: 5 high-quality traces from a Fable 5 dataset and 5 traces generated by Kimi 2.7 Coder. The training process was exceptionally brief, taking approximately 3 minutes on a single DGX Spark (GB10) setup.
Purpose and Limitations
This model was created as an educational and illustrative demonstration to highlight how easily credibility can be manufactured in the local AI ecosystem through minimal work and aggressive marketing, rather than rigorous evaluation. It serves as a cautionary example against hype-driven releases. Consequently, it is not recommended for production coding models.
Key Takeaways for Users:
- Test models yourself: Do not rely solely on teacher names or polished model cards.
- Demand real evaluations: Look for data volume and clear methodology.
- Be suspicious of buzzwords: Version numbers and strong names are not evidence of capability.
- Prefer open, reproducible evals: Prioritize transparent evaluation over vague claims.
Training Details:
- Base model: Qwen3.6-27B
- Method: Full fine-tune
- Data: 10 traces (5 Fable 5 seeds + 5 Kimi 2.7 Coder generations)
- Wall-clock time: ~3 minutes
Due to the extremely limited training data, any apparent strengths are narrow and statistically underdetermined. No contamination-checked benchmark numbers are provided, aligning with the model's critical stance on unverified claims.