Dataset Preparation

Building Chat Datasets

Utilities for building conversation datasets from NVIDIA Nemotron Post-Training collections and other HuggingFace sources. These scripts produce datasets in standard OpenAI chat format ({"messages": [{"role": ..., "content": ...}]}) and can be used for any downstream fine-tuning task — SFT, distillation, speculative decoding draft-model training, etc.

Files

File	Description
make_nemotron_ptv3_dataset.py	Build a dataset from the Nemotron PT v3 collection using a configurable YAML mix
make_nemotron_ptv2_dataset.py	Build a dataset from Nemotron-Post-Training-Dataset-v2
make_dataset.py	General-purpose mixer for arbitrary HuggingFace datasets (mtbench, sharegpt, magpie, etc.)
conversation_utils.py	Shared utilities: augmentation, role normalization, assistant-turn stripping
add_nemotron_chat.py	Add Nemotron v2 chat conversations to an existing dataset
augmentations.yaml	Augmentation variants (language redirects, style hints) for make_nemotron_pt*.py
nemotron_ptv3_datasets.yaml	Dataset mix config for make_nemotron_ptv3_dataset.py
example_data_config.yaml	Example YAML config for make_dataset.py

Quick Start

Install dependencies

pip install nvidia-modelopt[hf]
hf auth login --token <your token> # required for gated datasets

Build a Nemotron PT v3 dataset

# Synthetic data generation inputs (strips last assistant turn so a model can regenerate it)
python make_nemotron_ptv3_dataset.py --output-dir /tmp/ptv3_gen

# Full conversations for direct SFT training
python make_nemotron_ptv3_dataset.py --mode train --output-dir /tmp/ptv3_train

# Use a custom dataset mix
python make_nemotron_ptv3_dataset.py --config my_mix.yaml --output-dir /tmp/ptv3_custom

Build a Nemotron PT v2 dataset

python make_nemotron_ptv2_dataset.py --output-dir /tmp/ptv2_gen
python make_nemotron_ptv2_dataset.py --mode train --output-dir /tmp/ptv2_train

Build a general-purpose mixed dataset

python make_dataset.py --config example_data_config.yaml --output-dir /tmp/mixed

Dataset Modes

Both make_nemotron_pt*.py scripts support two modes:

Mode	Description	Use case
generate (default)	Strips assistant turns, optionally augments prompts	Input data for synthetic generation (query a target model to produce training responses)
train	Keeps all turns, normalizes to clean OpenAI format	Direct SFT / distillation training

Synthetic Generation Pipeline

The generate mode produces conversation skeletons that are fed to a target model via tools/launcher/common/query.py (vLLM or TRT-LLM). The output becomes training data for a draft model (e.g. EAGLE3 speculative decoding) or a distilled student:

make_nemotron_ptv3_dataset.py --mode generate  →  skeleton.jsonl
        ↓
query.py  (target model generates responses turn-by-turn)
        ↓
training data for draft model / student

Augmentations

augmentations.yaml defines language-redirect and style-hint variants that are applied cyclically across the dataset. Each enabled entry produces one augmented copy of the source rows.

To customize augmentations:

• Disable a variant: add enabled: false
• Add a language redirect: append a user_suffix entry
• Add a system prompt: append a system_prompt entry

augmentations:
  - type: user_suffix
    text: " Please reply in French instead of English."
  - type: system_prompt
    content: "You are a helpful assistant."
    enabled: false   # disable without deleting

Dataset Mix Config (nemotron_ptv3_datasets.yaml)

Edit this file to add, remove, or re-weight datasets without touching the script:

datasets:
  - repo_id: nvidia/Nemotron-Math-v2
    splits: [high_part00, high_part01]
    cap_per_split: 200000
    augment: true

  - repo_id: nvidia/OpenMathReasoning-mini
    splits: [train]
    augment: false   # multilingual — skip language-redirect augmentation

Output Format

Every output row is a JSONL object with a single messages key:

{"messages": [
  {"role": "system",    "content": "You are a helpful assistant."},
  {"role": "user",      "content": "What is 2+2?"},
  {"role": "assistant", "content": "4"}
]}

In generate mode, assistant turns are stripped so the row ends with a user turn.

Tokenizing for Megatron Frameworks

See MEGATRON_DATA_PREP.md for full documentation: general usage with JSONL and Hugging Face Hub datasets, handling of Nemotron Post-Training v3 reasoning_content fields, and ready-to-run tokenization commands for all Nemotron Pre/Post-Training datasets.

Synthetic Test Dataset

synthetic_conversations_1k.jsonl is a 1,000-sample dataset in OpenAI messages format (900 single-turn + 100 two-turn conversations) covering writing, reasoning, math, coding, STEM, extraction, humanities, and roleplay categories.

This dataset was synthesized by Claude (Anthropic) and is licensed under Apache-2.0. It is intended for testing and CI regression — not for production training.

{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}