Qwen3.5-9B-NVFP4

This is a quantized version of Qwen/Qwen3.5-9B. This model accepts text and images as inputs and generates text as outputs. The weights and activations were quantized to FP4 using llm-compressor with 512 calibration samples from nvidia/Nemotron-Post-Training-Dataset-v2, reducing the model size from 18.0 GB to 11.5 GB (~1.6x reduction) while maintaining 97.3% average accuracy recovery.


Quantization Details

  • Scheme: NVFP4
  • Calibration: 512 samples (256 reasoning-on + 256 reasoning-off) from Nemotron-Post-Training-Dataset-v2
  • Max sequence length: 4096

Inference

This model is supported in vLLM 0.17.0. To serve the model:

vllm serve Kbenkhaled/Qwen3.5-9B-NVFP4 \
    --reasoning-parser qwen3 \
    --enable-prefix-caching

Evaluation

Evaluated with lm-evaluation-harness, 0-shot, thinking mode ON.

Benchmark Qwen3.5-9B Qwen3.5-9B-NVFP4 (this model) Recovery
GPQA Diamond 78.79% 74.24% 94.2%
IFEval 94.48% 92.69% 98.1%
MMLU-Redux 91.80% 91.39% 99.6%
Average 88.36% 86.11% 97.3%
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Dataset used to train Kbenkhaled/Qwen3.5-9B-NVFP4