Guidance regarding prepare calibration dataset to perform GPTQ

[ynwang007]

Hi,

I want some guidance regarding how to prepare calibration dataset for quantizing models using GPTQ? Are there some recommended dataset that could be good to start with and will works for majority models (llama, qwen, etc)? Or do we have to use different dataset for different models based on actual task to perform?

In the GPTQ paper https://arxiv.org/pdf/2210.17323, it was using 128 samples from c4 dataset, it also mentions:
"We emphasize that this means that GPTQ does not see any task-specific data, and our results thus remain actually “zero-shot”.

Also from HF document https://huggingface.co/docs/transformers/v4.49.0/en/quantization/gptq, it says:
"You could also pass your own dataset as a list of strings, but it is highly recommended to use the same dataset from the GPTQ paper."

Curious does C4 works well in practice? Does anyone have any experience to share? Thanks a lot!

[Shyboy0499]

Standard practice — for most models a general corpus works fine; use task-specific data only when the target task is narrow.

Recommended starting point:

• ~128 samples × ~2048 tokens is the classic recipe (the GPTQ paper used 128 samples; AutoGPTQ's defaults are similar).
• Corpora that work for most LLMs: wikitext2 (smallest/fastest — the go-to default in AutoGPTQ and transformers examples), c4 / allenai/c4 (more diverse, often better quality), or ptb. Start with wikitext2; if quantized quality is disappointing, try c4.
• Instruction-tuned models: use instruction-formatted text (e.g. from HuggingFaceH4/ultrachat_200k or the model's own training format) rather than raw wikitext — calibrating a chat model on plain prose under-represents the distribution it actually serves.

When to go task-specific:

• General reasoning / open-ended → a general corpus is fine.
• Narrow/structured tasks (JSON generation, code, domain-specific) → sample a few hundred examples from that task's real distribution. GPTQ compensates for the weights' error using what it sees in calibration, so matching the input distribution matters more than the size of the dataset.

Practical recipe:

1. Take 128 samples × 2048 tokens from a general corpus (wikitext2 or c4).
2. Quantize with AutoGPTQ or transformers GPTQ (add trust_remote_code=True if the model needs it).
3. Evaluate perplexity on a held-out set plus a handful of real task prompts — perplexity alone can hide task regressions.
4. If task performance regresses more than acceptable, swap in a few hundred task-formatted samples and re-quantize.

One clarification: GPTQ is weight-only. KV cache and activations stay fp16, so the "128 × 2048" calibration is about compensating weight-quantization error, not about runtime memory — don't confuse it with the calibration data used for KV/activation quant (different thing).