metadata
license: apache-2.0
base_model: Qwen/Qwen3.5-9B
pipeline_tag: text-generation
tags:
- nvfp4
- rtn
- quantization
- compressed-tensors
- vllm
Qwen3.5-9B-NVFP4-RTN
RTN(+act-calib) NVFP4 quantization of Qwen/Qwen3.5-9B in vLLM
compressed-tensors nvfp4-pack-quantized format, full W4A4 schema
(static input-activation scales included; default load serves W4A4 on
SM100+). The weights are byte-exact RTN — no training step was taken.
9B companion of
Qwen3.5-27B-NVFP4-RTN,
produced by the identical recipe.
How this checkpoint was produced — exact reproduction
| Producing repo | tonyzhang-together/QATFactory @ 096f2ae |
| Base model | Qwen/Qwen3.5-9B (dense qwen3_5, bf16) |
| Weights | RTN, no training: base bf16 weights rounded to the NVFP4 lattice by the same exporter used for all QAD checkpoints in this series (verified byte-identical to llm-compressor model_free_ptq at 9B) |
| Activation scales | Forward-only single-GPU calibration (nvfp4_rtn_act_calib.py): the training stack's own input-scale lifecycle (apply_fake_quant -> running amax -> commit at 20 step boundaries -> finalize) over 640 sequences (20x32) of the md5-pinned train corpus openperfectblend_100k_Qwen3.5-9B_think_train.jsonl (md5 0406bb3a7a482352360716a1bc5e9e04), max_seq 8192 |
| Export | scripts/export_nvfp4_vllm.py --source <calib ckpt> --model-assets Qwen3.5-9B (a4schema) |
| Hardware | 1xB200 (jbom-03), 2026-07-27 |
| Role | Weak-PTQ baseline row of the Qwen3.5-9B standardized campaign; results filed in qatfactory-experiments qwen3.5-9b/ |
Inference
vllm >= 0.25.1 serves this checkpoint W4A4 by default on SM100+ (B200):
vllm serve weili-0234/Qwen3.5-9B-NVFP4-RTN --max-model-len 33024