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How to reproduce STEP Quant@6 results on Qwen3.8-27B with STEP Quant

Calibrate a 6-bit STEP Quant plan, serve it with SGLang, and run the long-generation evaluation suite.

  • Top pick
  • Research
  • Advanced
  • 1 day
  • From the README
  • Any agent

Who it is forA researcher checking the paper's claims who has Qwen checkpoints and four GPUs available.

The problemPaper numbers are hard to trust without rerunning the calibration, serving, and evaluation pipeline on the same model.

The outcomeA calibrated plan file, a running quantized server, and evaluation scores for FP32 and STEP Quant@6 on long-generation tasks.

What you need first

  • Model checkpoints for a supported model, such as Qwen3.8-27B, placed on disk
  • A CUDA toolkit and NVIDIA GPUs (the Qwen example uses four GPUs; the paper used NVIDIA A800 GPUs)
  • Python with the tested stack: SGLang 0.5.12, PyTorch 2.11.0, Triton 3.7.1
  • A clone of the repository, run from its root

Steps

  1. Set model_path in configs/models.json to your Qwen checkpoint directory.
  2. Run bash scripts/reuse_sglang_env.sh /path/to/sglang-env, then bash scripts/reuse_eval_env.sh .venv-serving, then bash scripts/reuse_calibration_env.sh /path/to/qwen-env qwen.
  3. Run .venv-eval/bin/python -m stepquant.reproduction --models qwen --formats stepquant6 --devices 0,1,2,3 to calibrate once and write the plan. (inferred, check the README)
  4. Run CUDA_VISIBLE_DEVICES=0,1,2,3 .venv-serving/bin/python -m stepquant.sglang --model-path /path/to/Qwen3.8-27B --tp-size 4 --state-format stepquant6 --plan artifacts/reproduction/plans/qwen-stepquant6.pt --attention-backend triton --reasoning-parser qwen3 --context-length 131072 --chunked-prefill-size 2048 --mem-fraction-static 0.85 --served-model-name stepquant --host 127.0.0.1 --port 31080 to serve. (inferred, check the README)
  5. Stop the manual server, then run .venv-eval/bin/python -m stepquant.evaluation.suite --models qwen --benchmarks long --formats fp32 stepquant6 --server-python .venv-serving/bin/python --devices 0,1,2,3 --tp 4 and compare the scores to the paper table. (inferred, check the README)

Prompt for your agent

In the STEPQuant repo at <repo_path>, follow the Quick Start in README.md to calibrate the stepquant6 plan for the Qwen checkpoint at <model_path>, then run the evaluation suite with the fp32 and stepquant6 formats. Report the average accuracy for each format and confirm the STEPQuant@6 score is close to the FP32 score in the README results table.

Caveat. The README requires model checkpoints, a CUDA toolkit, and four GPUs for the Qwen example, and the paper's numbers come from NVIDIA A800 GPUs, so your scores may differ.

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