Qwopus3.5-27B-v3-AWQ-4bit

AutoAWQ-format 4-bit quantized version of Jackrong/Qwopus3.5-27B-v3.

This checkpoint preserves the hybrid Qwen3.5 DeltaNet + softmax architecture and MTP (Multi-Token Prediction) head from the BF16 source, exporting an AutoAWQ-compatible W4A16 checkpoint for broad runtime compatibility.

Verified Inference

Local export was completed on 2026-04-07 on a single NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB) with:

• auto-round==0.10.2
• transformers==5.3.0
• vllm==0.17.1

What was verified:

• AutoAWQ export completed successfully
• quantization_config.json written with quant_method=awq
• Output uses bits=4, group_size=128, sym=false, zero_point=true
• MTP weights are included in the main model shards

Quantization Strategy

AutoRound AutoAWQ export using W4A16 asymmetric group-wise quantization:

Precision	Layers
INT4 weights + BF16 activations	most quantized linear layers
BF16	lm_head, embed_tokens, self_attn.o_proj, DeltaNet linear_attn.out_proj, DeltaNet in_proj_a/in_proj_b, visual encoder, MTP sidecar

AWQ details:

• weights: INT4
• activations: BF16/FP16 at inference time
• group size: 128
• asymmetric quantization: sym=false, zero_point=true
• format: AutoAWQ gemm
• calibration: 512 samples, 4096 sequence length

Architecture match with the BF16 source:

• model_type=qwen3_5
• 64 text layers (hybrid DeltaNet + softmax, full_attention_interval=4)
• mtp_num_hidden_layers=1
• max_position_embeddings=262144
• hidden_size=5120, intermediate_size=17408
• vocab_size=248320

Usage

vLLM

pip install -U vllm>=0.17.0 transformers>=5.3.0

Standard serving:

vllm serve mconcat/Qwopus3.5-27B-v3-AWQ-4bit \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.85 \
  --max-num-seqs 1 \
  --skip-mm-profiling \
  --reasoning-parser qwen3

With MTP speculative decoding:

vllm serve mconcat/Qwopus3.5-27B-v3-AWQ-4bit \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.85 \
  --max-num-seqs 1 \
  --skip-mm-profiling \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'

Transformers

This export is not intended for plain transformers inference. Use a runtime that understands AutoAWQ-format checkpoints, such as vLLM with AWQ support.

Compatibility

Framework	Supported	Notes
vLLM >= 0.17.0	Yes	Intended serving path for this AutoAWQ export
transformers >= 5.3.0	No	Plain transformers is not the intended inference path for AutoAWQ checkpoints
AutoAWQ-compatible runtimes	Expected	Export format is AutoAWQ-style quant_method=awq, version=gemm
SGLang	Unknown	Not verified

Notes

• This is an AutoAWQ-format export, not a compressed-tensors AWQ format.
• The output keeps self_attn.o_proj and DeltaNet linear_attn.out_proj in BF16 rather than 4-bit.
• MTP weights are included in the model shards (no separate model.mtp.safetensors).
• The model includes a vision encoder (loaded but unused for text-only inference). Use --skip-mm-profiling with vLLM to skip vision encoder profiling.
• Blackwell (SM120) note: If you encounter TMA-related crashes, apply the one-line vLLM patch to disable TMA on Blackwell: change >= 9 to 9 <= x < 12 in vllm/model_executor/layers/fla/ops/utils.py.