CoolFace
Datasetpublic

MaziyarPanahi/Nemotron-Cascade-2-SFT-Data-Small

Nemotron-Cascade-2-SFT-Data-Small A 20% random sample of nvidia/Nemotron-Cascade-2-SFT-Data, merged into a single train split with 4,898,804 rows. Subsets included (all merged) Original subset Files sampled ~Rows sampled math math_notool, math_proof, math_tool ~1,045,266 science science ~544,383 chat chat_part_1 – chat_part_4 ~2,794,866 instruction_following instruction_following ~163,869 safety safety ~693 conversational_agent… See the full description on the dataset page: https://huggingface.co/datasets/MaziyarPanahi/Nemotron-Cascade-2-SFT-Data-Small.

sourceHugging Faceotherupdated 6mo agoView on Hugging Face
4likes43downloads
Dataset Card

Nemotron-Cascade-2-SFT-Data-Small

A 20% random sample of nvidia/Nemotron-Cascade-2-SFT-Data, merged into a single train split with 4,898,804 rows.

Subsets included (all merged)

Original subsetFiles sampled~Rows sampled
mathmathnotool, mathproof, math_tool~1,045,266
sciencescience~544,383
chatchatpart1 – chatpart4~2,794,866
instruction_followinginstruction_following~163,869
safetysafety~693
conversational_agentconversational_agent~164,264
swesweagentic, sweagentless~88,174
terminal_agentterminal_agent~97,289

Schema

python
{
  "domain":    str,   # e.g. "math_notool", "chat", "swe_agentic"
  "source":    str,   # upstream data source
  "messages":  list[{"role": str, "content": str}],
  "generator": str,   # model that generated the response
}

Usage

python
from datasets import load_dataset
ds = load_dataset("MaziyarPanahi/Nemotron-Cascade-2-SFT-Data-Small", split="train")

Sampling details

  • —Sample rate: 20% Bernoulli per source file
  • —Random seed: 42
  • —Output format: Parquet (zstd compressed, 500K rows/shard, 10 shards, ~35 GB)