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