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Update default calibration dataset and fix indentation - #2483

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chensuyue wants to merge 2 commits into
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@chensuyue

@chensuyue chensuyue commented Oct 13, 2026 •

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Description

  • Update the default calibration dataset from NeelNanda/pile-10k to HuggingFaceH4/ultrachat_200k.
  • Correct indentation for the apply_chat_template function in the get_ultrachat_dataset function.

Type of Change

Bug fix

Related Issues

Fixes or relates to #

Checklist Before Submitting

  • My code has been tested locally.
  • Documentation has been updated as needed.
  • New or updated tests are included where applicable.
  • The CUDA CI has passed. You can trigger it by commenting /azp run Unit-Test-CUDA-AutoRound.

Copilot AI balanced review requested due to automatic review settings October 13, 2026 06:37
…ggingFaceH4/ultrachat_200k

Signed-off-by: chensuyue <suyue.chen@intel.com>

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🟡 Changes recommended

The default path enables remote dataset code execution, and the indentation fix lacks regression coverage.

2 open findings
What changed in this PR

Updates AutoRound’s default calibration dataset to UltraChat and fixes chat-template preprocessing.

Changes:

  • Replaces Pile-10k defaults across calibration, AutoScheme, export, and VLM templates.
  • Corrects UltraChat chat-template indentation.
  • Updates English and Chinese documentation.
File Description
README.md Documents the new default.
README_CN.md Mirrors README changes in Chinese.
docs/​step_by_step.md Updates dataset guidance.
docs/​step_by_step_CN.md Updates Chinese dataset guidance.
auto_round/​export/​utils.py Updates export configuration filtering.
auto_round/​compressors/​mllm/​templates/​phi3_v.json Updates Phi-3-Vision default.
auto_round/​compressors/​mllm/​templates/​llava.json Updates LLaVA default.
auto_round/​compressors/​mllm/​templates/​default.json Updates generic VLM default.
auto_round/​compressors/​mllm/​templates/​cogvlm2.json Updates CogVLM2 default.
auto_round/​compressors/​mllm/​template.py Updates registered VLM defaults.
auto_round/​compressors/​mllm/​README.md Documents the VLM default.
auto_round/​compressors/​diffusion_mixin.py Preserves diffusion dataset substitution.
auto_round/​compressors/​base.py Changes the core calibration default.
auto_round/​calib_dataset.py Changes helper defaults and fixes indentation.
auto_round/​autoround.py Updates the internal builder default.
auto_round/​auto_scheme/​gen_auto_scheme.py Updates AutoScheme’s dataset default.
auto_round/​auto_scheme/​delta_loss.py Updates delta-loss dataset defaults.

🧠 Review effort: Balanced


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def _get_dataset_impl(tokenizer, seqlen, dataset_name="NeelNanda/pile-10k", seed=42, nsamples=512):
def _get_dataset_impl(tokenizer, seqlen, dataset_name="HuggingFaceH4/ultrachat_200k", seed=42, nsamples=512):
elif not is_instruct and apply_chat_template:
logger.info("Tokenizer is not an instruct/chat model, but apply_chat_template=True. Setting to False.")
apply_chat_template = False
apply_chat_template = False
…aset function

Signed-off-by: chensuyue <suyue.chen@intel.com>
@wenhuach21

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We shouldn't change the default dataset at the code level unless it leads to accuracy improvements. If there are other issues involved, you should modify the dataset you use instead.

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accuracy issue

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/azp run Unit-Test-CUDA-AutoRound

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Azure Pipelines successfully started running 1 pipeline(s).

@chensuyue chensuyue closed this Oct 13, 2026
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4 participants