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weili-0234/qatfactory-gptq-calibration

QATFactory GPTQ calibration set (calib_512) This calibration dataset is a chosen subset of OpenPerfectBlend. Concretely: 512 rows sampled with random.Random(42).sample(range(n_rows), 512) (then sorted) from openperfectblend_100k_Qwen3.5-9B_think_train.jsonl — a 100K-example OpenPerfectBlend prompt set whose responses (including <think> reasoning traces) were generated by Qwen/Qwen3.5-9B. Each row is the conversation rendered with the model's stock chat template… See the full description on the dataset page: https://huggingface.co/datasets/weili-0234/qatfactory-gptq-calibration.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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QATFactory GPTQ calibration set (calib_512)

This calibration dataset is a chosen subset of [OpenPerfectBlend](https://huggingface.co/datasets/mlabonne/open-perfectblend).

Concretely: 512 rows sampled with random.Random(42).sample(range(n_rows), 512) (then sorted) from openperfectblend_100k_Qwen3.5-9B_think_train.jsonl — a 100K-example OpenPerfectBlend prompt set whose responses (including <think> reasoning traces) were generated by Qwen/Qwen3.5-9B. Each row is the conversation rendered with the model's stock chat template (add_generation_prompt=False) into a single text field.

Fingerprints (from calib_512.meta.json)

  • —calib_512.jsonl md5: d1ff8cce1d785f8e51b71eb237fb7a71
  • —line-indices sha256: 966f4780c1438ebd35c0feaf6dddd258b65fc5e20b44c5b434906dcb7cac4084
  • —rendered-text sha256: 52d75ac687cc4acca8b220f9f67009ac754dea60fc564bea364e46755abac5c2

Used by

GPTQ PTQ baselines of the QATFactory W4A4 study (llm-compressor 0.12.0 GPTQModifier, max_seq_length=8192, num_calibration_samples=512):

Reproduction scripts: gptq_27b.py / gptq_9b.py (togethercomputer/weilix branch weili/qat, experiments/w4a4-qwen35-27b/jbom/ and experiments/mxfp4-qwen35-9b/). Result tables: qatfactory-experiments.