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Avesed/Qwen3.6-27B-INT4-W4A16
Qwen3.6-27B-INT4-W4A16 is a text generation model from Avesed. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
INT4 weight-only (W4A16) quantization of Qwen/Qwen3.6-27B — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a vision tower and an MTP head.
Downloads · 30 days
242
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How the weights are stored.
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From the Hugging Face model README
INT4 weight-only (W4A16) quantization of Qwen/Qwen3.6-27B — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a vision tower and an MTP head.
self_attn, and the GatedDeltaNet out_proj.in_proj_a/in_proj_b, lm_head, embeddings, vision tower, MTP head.compressed-tensors (pack-quantized). Full recipe: recipe.yaml.Served as W4A16 (vLLM, thinking enabled, temperature 0.6):
| Benchmark | Score |
|---|---|
| GSM8K | 96.8% (242/250) |
| MMLU-Pro | 82.4% (412/500) |
# W4A16 — int4 weights, fp16 activations
vllm serve Avesed/Qwen3.6-27B-INT4-W4A16 \
--tensor-parallel-size 2 --trust-remote-code --reasoning-parser qwen3
On Ampere the same checkpoint can be served as W4A8 (int4 weights + int8 dynamic activations — faster prefill / batched serving) with the vllm-ampere-optimized fork:
vllm serve Avesed/Qwen3.6-27B-INT4-W4A16 \
--tensor-parallel-size 2 --marlin-input-dtype int8 --trust-remote-code --reasoning-parser qwen3
Quantized with vllm-ampere-optimized/quantize.