GPTQ (Hessian-corrected PTQ) NVFP4 quantization of Qwen/Qwen3.5-9B in vLLM compressed-tensors nvfp4-pack-quantized format, with calibrated input global scales (W4A4-servable, default load, SM100+). Strong-PTQ baseline of the Qwen3.5-9B standardized campaign; 9B companion of Qwen3.5-27B-NVFP4-GPTQ, produced by the identical recipe.

How this checkpoint was produced — exact reproduction


Tooling	llm-compressor 0.12.0 (`GPTQModifier`), transformers 5.9.0
Base model	Qwen/Qwen3.5-9B (dense `qwen3_5`, bf16)
Recipe	`GPTQModifier(targets="Linear", scheme="NVFP4", ignore=["lm_head", "re:.visual.", "re:.mtp.", "re:.embed_tokens."])`; NVFP4 scheme also calibrates static per-tensor input global scales from the same pass
Calibration	Fixed 512-conversation set `calib_512.jsonl` (md5 `d1ff8cce1d785f8e51b71eb237fb7a71`): `random.Random(42).sample(range(n_rows), 512)`, sorted, from the study's OpenPerfectBlend-derived ChatML train corpus (md5 `0406bb3a7a482352360716a1bc5e9e04`); stock chat template, `max_seq_length=8192`, `num_calibration_samples=512`
Script	`gptq_9b.py` = the 27B script (`experiments/w4a4-qwen35-27b/jbom/gptq_27b.py` in the study workspace) with MODEL/OUT set to 9B
Hardware	1xB200 (jbom-03), 2026-07-27
Role	Strong-PTQ baseline row; results filed in qatfactory-experiments `qwen3.5-9b/`

Inference

vllm serve weili-0234/Qwen3.5-9B-NVFP4-GPTQ --max-model-len 33024