Frank Denisandantirez
4344ab40f1
Broaden the DS4 imatrix prompt dataset with provider-neutral agent/tool traffic, multi-language programming prompts, algorithm recall, Bash scripting, and multilingual translation tasks. Remove duplicate rendered prompts and avoid provider-specific client references in the generated calibration corpus. This improves calibration coverage without claiming to fix a distributed GGUF bug.
DS4 Imatrix Calibration Dataset
This directory contains DS4-rendered chat prompts for collecting activation statistics before building new low-bit GGUF files.
Run:
python3 gguf-tools/imatrix/dataset/build_ds4_imatrix_dataset.py
Generated files:
prompts.jsonl: structured records with messages and rendered prompt text.rendered_prompts.txt: all rendered prompts, separated by visible markers.rendered_prompts_nothink.txt: only prompts ending with</think>.rendered_prompts_think.txt: only prompts ending with<think>.manifest.json: counts, byte totals, and rough token estimate.
The renderer mirrors the server prompt shape:
<｜begin▁of▁sentence｜>system<｜User｜>...<｜Assistant｜><think>
<｜begin▁of▁sentence｜>system<｜User｜>...<｜Assistant｜></think>
Some records include DSML tool schemas, sampled DSML tool calls, and tool-result turns so the imatrix sees the same special-token patterns used by agent clients. The corpus is provider-neutral and also includes multi-language programming prompts, Bash scripting, algorithm recall, English-to-European-language translation, English-to-Chinese translation, reverse translation to English, long-context source excerpts, Metal/C code review tasks, and inference-specific debugging tasks.
For normal imatrix collection, use rendered_prompts.txt so calibration covers
both thinking and non-thinking modes. Split files are provided for ablations.