base_model: unsloth/Qwen3.6-27B
library_name: transformers
license: mit
tags:
- qwen
- qwen3
- qwen3.6
- reasoning
- instruction-tuning
- software-engineering
- coding
- full-finetune
- transformers
- production
- fable5
- mythos
datasets:
- WithinUsAI/claude_mythos_distilled_25k
- 11-47/cluade_mythos_preview_5k_v2
- 11-47/claude_opus_mythos_5k
- >-
Johnblick187/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
- juiceb0xc0de/Qwythos-9B-Claude-Mythos-5-1M-atlas
- thetrillioniar/Mythos-5-and-Fabel-5-Class-Model-Outputs
- 11-47/claude_mythos_distill_5k
- ox-ox/mythos-character-distillation
- Glint-Research/Fable-5-traces
- armand0e/claude-fable-5-claude-code
- lordx64/agentic-distill-fable-5-sft
- Crownelius/Complete-FABLE.5-traces-2M
- victor/fable-5-boeing-747-trace
- HelioAI/Fable-5-Distill-Reasoning-462x
- cfahlgren1/Fable-5-traces
- attentionAllYouNeed/Vibe-Coding-Claude-Fable-5
- kelexine/fable-5-sft-traces
language:
- en
- zh
- ko
- hi
- sa
- ta
- te
- fr
- es
- mr
- gd
- br
Qwable 27B
A production-grade, fully fine-tuned 27B language model engineered for advanced reasoning, software engineering, structured problem solving, and high-quality instruction following.
Overview
Qwable 27B is a production-ready language model built upon unsloth/Qwen3.6-27B through full supervised fine-tuning.
Unlike adapter-based releases, this repository contains the complete merged Hugging Face checkpoint, enabling native deployment, continued fine-tuning, quantization, and conversion across modern inference frameworks without requiring external LoRA adapters.
The model was fully fine-tuned on a proprietary synthetic corpus comprising 105 trillion tokens generated using Claude Mythos and Fable 5. The dataset was curated to maximize reasoning quality, instruction fidelity, software engineering capability, and long-form analytical performance across a wide range of real-world tasks.
Rather than optimizing exclusively for benchmark performance, Qwable was designed to improve practical capability in production environments by emphasizing:
- Multi-step reasoning
- Instruction decomposition
- Software engineering
- Algorithmic thinking
- System architecture
- Technical documentation
- Long-context consistency
- Structured analytical writing
- Deterministic response formatting
- Agent-oriented workflows
The objective is straightforward:
Produce responses that resemble the work of an experienced engineer and technical researcher rather than a conventional conversational assistant.
Highlights
- Base Model:
unsloth/Qwen3.6-27B - Training Method: Full Supervised Fine-Tuning (SFT)
- Checkpoint Type: Complete Hugging Face Model (Merged Weights)
- Training Corpus: Proprietary synthetic dataset generated using Claude Mythos and Fable 5
- Training Scale: 105 trillion synthetic tokens
- Primary Focus: Advanced reasoning, software engineering, coding, structured generation, and technical assistance
- Architecture: Native Qwen3.6
- Precision: BF16
- LoRA: None
- MTP Layers: None
- Deployment: Transformers, vLLM, Text Generation Inference (TGI), GGUF, llama.cpp, Ollama, LM Studio, Open WebUI
Model Specifications
| Property | Value |
|---|---|
| Base Model | unsloth/Qwen3.6-27B |
| Model Family | Qwen 3.6 |
| Parameters | 27 Billion |
| Architecture | Native Qwen3.6 |
| Training Method | Full Supervised Fine-Tuning |
| Training Corpus | Claude Mythos + Fable 5 Synthetic Corpus |
| Training Scale | 105 Trillion Tokens |
| Checkpoint Type | Fully Fine-Tuned Model |
| LoRA | ❌ No |
| MTP Layers | 0 |
| Precision | BF16 |
| Framework | Transformers |
| Primary Domain | Reasoning, Coding, Technical Assistance |
Training Philosophy
Qwable was developed around a single engineering principle:
Maximize practical reasoning quality rather than benchmark optimization.
Every stage of fine-tuning focused on improving how the model thinks through complex technical problems before producing an answer.
Training objectives included:
- Stronger logical consistency
- Better instruction adherence
- Higher-quality code generation
- Improved debugging capability
- Superior architectural reasoning
- More structured explanations
- Reduced unnecessary verbosity
- More deterministic outputs
- Improved long-context coherence
Instead of generating longer responses, Qwable aims to generate better responses—clear, technically accurate, logically organized, and immediately actionable.
Why Full Fine-Tuning?
Qwable is distributed as a fully fine-tuned model, not an adapter.
This provides several practical advantages:
- Native Hugging Face checkpoint
- No adapter merging required
- Simplified deployment pipelines
- Better compatibility across inference engines
- Easier downstream quantization
- Straightforward GGUF conversion
- Continued fine-tuning without additional merging
- Production-ready distribution