Qwen3.6-27B-TQ3_4S

TQ3_4S Release

Runtime Compatibility

• TurboQuant runtime fork: turbo-tan/llama.cpp-tq3
• LM Studio setup: docs/backend/LMStudio.md

Files

Local Validation

• RTX 5060 Ti 16 GB

• llama-perplexity --chunks 10 -c 2048
• PPL = 6.2452 +/- 0.16138
• prompt eval = 712.02 tok/s

• 32k context fits
• 64k context fits
• 128k context does not fit

Runtime Notes

• Use a TurboQuant-capable llama.cpp build for best performance.
• For llama.cpp, the intended runtime is the turbo-tan/llama.cpp-tq3 fork.
• The upstream family is multimodal-capable, but the public 27B repos used here do not currently expose a separate GGUF mmproj artifact.
• For llama.cpp chat usage, keep --jinja enabled so the bundled chat template is honored.
• Upstream guidance recommends keeping at least 128K context when possible for reasoning-heavy workloads. On smaller local GPUs, reduce context as needed to fit memory.
• Upstream default sampling guidance differs between thinking and non-thinking mode; follow the official Qwen card if you are trying to reproduce base-model behavior.

Recommended llama.cpp Settings

Example

Qwen3.6 Base Model

[!Note] The upstream Qwen repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

Those upstream artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, and related runtimes.

Qwen3.6 Highlights

• Agentic Coding: the model handles frontend workflows and repository-level reasoning with greater fluency and precision.
• Thinking Preservation: the model family retains reasoning context across historical turns to reduce overhead during iterative work.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training and Post-training
• Architecture: qwen35
• Parameters: 27B
• Layers: 64
• Embedding dimension: 5120
• FFN dimension: 17408
• Hidden layout: 16 × (3 × (Gated DeltaNet -> FFN) -> 1 × (Gated Attention -> FFN))
• Gated DeltaNet heads: 48 for V, 16 for QK, head dim 128
• Gated Attention heads: 24 for Q, 4 for KV, head dim 256
• RoPE dim: 64
• Native context: 262,144

Selected Upstream Benchmark Highlights

• SWE-bench Verified: 77.2
• Terminal-Bench 2.0: 59.3
• SkillsBench Avg5: 48.2
• GPQA Diamond: 87.8
• AIME26: 94.1
• MMMU: 82.9
• AndroidWorld: 70.3

Sources

• Upstream base model: Qwen/Qwen3.6-27B
• Upstream GGUF source used for conversion: unsloth/Qwen3.6-27B-GGUF
• Upstream blog and benchmark context: Qwen3.6-27B model card
• TurboQuant runtime fork: turbo-tan/llama.cpp-tq3