🧠 Opus DeepSeek Distilled Q4M A distilled Qwen3.6 27B GGUF optimized for local agentic reasoning, tool use, and long chain task execution. ⚠️ Sampling Parameters Please ensure temperature = 0.6 and top p = 0.95 when using this model. This model was trained and validated at these specific parameters. Both too high and too low temperatures cause problems: 🔥 Too high ( 0.6) → Output becomes divergent as token probabilities flatten. This causes malformed tool calls, function name hallucinations, unstable parameter generation, and uncontrollable agent behavior. 🧊 Too low ( This is an important recommendation based on extensive real world testing. Please verify these parameters in your inference framework. 📢 Highlights Area Score vs Qwen3.6 27B q4 k m BenchLocal 6 pack 🏆 86.5 +8.3 GPQA Diamond 198 🔬 83.84% +10.14% BugFind 15 🐛 80 +20 ToolCall 15 🔧 97 +4 InstructFollow 15 📋 94 +17 StructOutput 15 📊 88 +11 MMLU 500 (5 shot) 📚 91.80% ~tied (+0.2%) DataExtract 15 📄 81 2 🚀 Output speed : ~60 tok/s on A100 40GB · ~100 tok/s on RTX PRO 6000 (q4 k m + mtp=3) 🔥 Why This Model? The original Qwen3.6 27B has solid foundational capabilities, but its agent behavior falls short — prone to…
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