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
特点摘要待依据来源补齐,不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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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。