Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a highly capable reasoning model fine-tuned on top of the powerful Qwen3.5 architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions.
Key Features
Claude-4.6 Opus Reasoning Distillation: Deep distillation and structural imitation of Claude-4.6-Opus reasoning chains
Structured Thinking: Uses `` tags for internal reasoning with "Let me analyze this request carefully: 1..2..3..." pattern
Native Developer Role Support: Fully supports the "developer" role without Jinja template patches
Full Thinking Mode: Preserves complete chain-of-thought reasoning process (thinking=1)
262K Context Window: Full context with no compromises
Coding Agent Optimized: Tested and optimized for Claude Code and OpenCode environments
The imatrix quantizations (IQ2_S, IQ3_M, IQ4_XS) use a custom importance matrix derived from OpenCode sessions with the following composition:
50% Reasoning - Complex problem-solving and analytical tasks
30% Tools - Command execution, file operations, and tool usage
20% TypeScript - TypeScript code generation and analysis
This calibration ensures optimal performance for coding agent workloads, maintaining reasoning quality while minimizing file size.
TQ3_1S - Turbo Quantization
TQ3_1S is a ternary quantization (TQ) optimized for speed:
Type: Ternary Quantization - uses three values {-1, 0, 1}
Architecture: WHT-rotated 3-bit with dual half-precision scaling
Performance: Extremely fast on AVX2 CPUs (up to 2x faster than standard Q4_K)
Best for: Users wanting a 3-bit quantization with maximum speed
Ternary quantization uses optimized 8-level Lloyd-Max centroids and Walsh-Hadamard Transform rotation for efficient weight distribution. Note: Actual BPW varies by layer (typically ~3.5-4.0) - verify for your specific model.
Usage with llama.cpp
Basic Usage
bash
1# Download a quantization2huggingface-cli download superbudvar/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS.gguf
34# Run with llama-cli5llama-cli -m Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-IQ4_XS.gguf -c 4096 -t 8
1<|im_start|>system
2You are a helpful assistant.<|im_end|>
3<|im_start|>user
4Write a hello world program in Python.<|im_end|>
5<|im_start|>assistant
Hardware Requirements
VRAM Requirements (Approximate)
Quantization
VRAM/RAM Required
Recommended GPU
IQ2_S
~10 GB
RTX 3060 / RX 6600 XT
IQ3_M
~13 GB
RTX 3060 12GB / RX 6700 XT
TQ3_1S
~14 GB
RTX 3070 / RX 6750 XT
IQ4_XS
~16 GB
RTX 3070 / RX 6800 XT
Q8_0
~29 GB
RTX 3090 / RX 7900 XT
CPU Inference
All quantizations work efficiently on modern CPUs with AVX2 or AVX-512 support. TQ3_1S is particularly optimized for AVX2 CPUs.
Example Reasoning Output
The model demonstrates structured thinking with clear step-by-step reasoning:
text
1Let me analyze this request carefully:
231. Identify the core objective of the problem.
42. Break the task into clearly defined subcomponents.
53. Evaluate constraints and edge cases.
64. Formulate a step-by-step solution plan.
75. Execute the reasoning sequentially and verify consistency.
This streamlined reasoning paradigm reduces redundant cognitive loops while preserving deep analytical capacity.
