vadery/Qwen3.5-27B-W8A8

Qwen3.5-27B W8A8 (INT8 weights + INT8 dynamic activations) — MTP-preserved

W8A8 INT8 quantization of Qwen/Qwen3.5-27B, produced with llm-compressor (SmoothQuant + GPTQ). The built-in MTP (Multi-Token Prediction) head is preserved as BF16 and works at 93 % acceptance under vLLM ≥ 0.17.

Quick start

pip install "vllm>=0.17"
huggingface-cli download vadery/Qwen3.5-27B-W8A8 --local-dir ./Qwen3.5-27B-W8A8

vllm serve ./Qwen3.5-27B-W8A8 \
  --max-model-len 262144 \
  --dtype bfloat16 \
  --gpu-memory-utilization 0.92 \
  --trust-remote-code \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":1}'

No additional patches required — both config.json.quantization_config.ignore (covers MTP Linear modules) and actorder field are already fixed.

Performance (single H200 SXM, vLLM 0.17.1, temperature=0)

Same quantization recipe as the GRM-2.6-Plus fine-tune (vadery/qwen36-27b-ft-grm-w8a8) — performance numbers are within noise.

Architecture preserved

Quantization recipe

SmoothQuantModifier(smoothing_strength=0.8, mappings=SQ_MAPPINGS,
                    ignore=[...vision, mtp, linear_attn, embed, lm_head...])
GPTQModifier(targets="Linear", scheme="W8A8",
             ignore=[same as above],
             dampening_frac=0.01)

SmoothQuant mappings explicitly cover only the 16 full-attention layers (indices 3, 7, …, 63 out of 64) plus MLP on every layer — to avoid SmoothQuant trying to fuse into the linear_attn projections which have non-standard shapes.

Calibration: 512 samples × 2048 tokens from HuggingFaceH4/ultrachat_200k.

Post-process steps (already applied; documented for reproducers)

llm-compressor 0.10 drops the MTP tensors from the saved state and writes a quantization_config.ignore that doesn't cover MTP Linear modules. We post-process:

1. 1.Restore MTP tensors — copy 15 mtp.* tensors from the BF16 source model-*.safetensors shards into the W8A8 single-shard safetensors.
2. 2.Patch `config.json` — add 8 MTP Linear module names to quantization_config.ignore and clear the spurious actorder=static field, so vLLM treats the MTP head as un-quantized BF16 on load.

Without these two steps, vLLM either drops the MTP head (0 % acceptance) or loads garbage values (also 0 % acceptance after weights are corrupted on load).

File size

Reasoning + tool calling

Same parser flags as the BF16 source:

• —--reasoning-parser qwen3 — separates <think> segments into reasoning field
• —--tool-call-parser qwen3_coder + --enable-auto-tool-choice — OpenAI tool-call API

Notes

• —vLLM ≥ 0.17 required (qwen3_5_mtp speculative method only landed there).
• —transformers ≥ 5.x is required for qwen3_5 model_type.
• —Tested on H200 (compute capability 9.0). H100 should also work.
• —The Qwen3.5 series emits <think> blocks by default — give max_tokens >= 4096 or pass "chat_template_kwargs": {"enable_thinking": false} to skip.

License

Inherits Apache 2.0 from the base model.