MODEL PROFILE

Qwen3.6-27B-int4-AutoRound

Lorbus · Qwen3.6-27B-int4-AutoRound

A **W4A16 (INT4 weight, FP16 activation) quantization** of `Qwen/Qwen3.6-27B`, produced with Intel's AutoRound. - **Base**: Qwen3.6-27B (27B dense VLM, Apr 21 2026) - **Quant**: INT4 W4A16, group_size 128, symmetric - **Tool**: `auto-round` (default recipe, 200 iters, torch.compile) - **Size**: 18 GB (down from ~54 GB BF16) — **3x reduction** - **MTP preserved**: The native Multi-Token Prediction head is kept in BF16, enabling **native speculative decoding** in vLLM (≈90% draft acceptance in our tests, ~2x throughput) - **Accuracy**: Default AutoRound recipe preserves quality well; layer-norm weights, router layers, RMSNorm, `linear_attn.in_proj_a/b`, and MTP's fusion `fc` are kept unquantized (they're small and benefit from full precision) Requires vLLM that supports Qwen3_5 MTP (most recent nightlies — tested with `eugr/spark-vllm-docker` fork `0.19.1rc1.dev39+g7055d32a7`):

Visual QAmultimodallicense:apache-2.0
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Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:54
OVERVIEW

Model overview

A **W4A16 (INT4 weight, FP16 activation) quantization** of `Qwen/Qwen3.6-27B`, produced with Intel's AutoRound. - **Base**: Qwen3.6-27B (27B dense VLM, Apr 21 2026) - **Quant**: INT4 W4A16, group_size 128, symmetric - **Tool**: `auto-round` (default recipe, 200 iters, torch.compile) - **Size**: 18 GB (down from ~54 GB BF16) — **3x reduction** - **MTP preserved**: The native Multi-Token Prediction head is kept in BF16, enabling **native speculative decoding** in vLLM (≈90% draft acceptance in our tests, ~2x throughput) - **Accuracy**: Default AutoRound recipe preserves quality well; layer-norm weights, router layers, RMSNorm, `linear_attn.in_proj_a/b`, and MTP's fusion `fc` are kept unquantized (they're small and benefit from full precision) Requires vLLM that supports Qwen3_5 MTP (most recent nightlies — tested with `eugr/spark-vllm-docker` fork `0.19.1rc1.dev39+g7055d32a7`):

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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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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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。