MODEL PROFILE

Qwen3.6-27B-GPTQ-4bit

btbtyler09 · Qwen3.6-27B-GPTQ-4bit

GPTQ 4-bit quantization of Qwen/Qwen3.6-27B, a 27B-parameter dense multimodal model. Uses **FOEM** (First-Order Error Matters, AAAI 2026) for outlier-aware error compensation, enabling pure 4-bit quantization without per-module FP16 exclusions. Includes the full vision encoder and MTP (Multi-Token Prediction) module for image understanding and speculative decoding support. - **Architecture**: Qwen3_5ForConditionalGeneration (multimodal: text + vision; dense sibling of `qwen3_5_moe`) - **Total parameters**: ~27B - **Layers**: 64 (48 linear-attention + 16 full-attention, repeating 3:1 pattern) - **Hidden size**: 5120, intermediate size: 17408 (dense MLP — no MoE) - **Context length**: 262,144 tokens - **Vision encoder**: 27-block ViT, BF16 (333 tensors) - **MTP module**: 1-layer speculative decoding head, BF16 (15 tensors)

Visual QAmultimodallicense:apache-2.0
Context window262,144 Token
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:17:03
OVERVIEW

Model overview

GPTQ 4-bit quantization of Qwen/Qwen3.6-27B, a 27B-parameter dense multimodal model. Uses **FOEM** (First-Order Error Matters, AAAI 2026) for outlier-aware error compensation, enabling pure 4-bit quantization without per-module FP16 exclusions. Includes the full vision encoder and MTP (Multi-Token Prediction) module for image understanding and speculative decoding support. - **Architecture**: Qwen3_5ForConditionalGeneration (multimodal: text + vision; dense sibling of `qwen3_5_moe`) - **Total parameters**: ~27B - **Layers**: 64 (48 linear-attention + 16 full-attention, repeating 3:1 pattern) - **Hidden size**: 5120, intermediate size: 17408 (dense MLP — no MoE) - **Context length**: 262,144 tokens - **Vision encoder**: 27-block ViT, BF16 (333 tensors) - **MTP module**: 1-layer speculative decoding head, BF16 (15 tensors)

Key features

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PRICING & COST

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INDEPENDENT BENCHMARKS

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。