DocumentationDesign Synthetic Data

Retrieval SDG

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NeMo Platform exposes Nemotron embed/rerank Stage 0 (sdg) and Stage 1 (prep) as dedicated Data Designer jobs. They wrap data-designer-retrieval-sdg and do not go through nemo data-designer create.

Prerequisites

  • A text corpus published as a fileset in the job workspace, or an hf:// dataset URI.
  • An Inference Gateway provider for the Stage 0 chat model and, when different, an embedding provider.
  • For GPU mining, a platform model entity with an attached fileset containing the encoder and tokenizer.

Generate (Stage 0, CPU)

nemo data-designer retrieval-generate --workspace default --spec '{
  "corpus": "default/my-docs",
  "provider": "default/nvidia-build",
  "artifact_extraction_model": "nvidia/nemotron-3-nano-30b-a3b",
  "qa_generation_model": "nvidia/nemotron-3-nano-30b-a3b",
  "quality_judge_model": "nvidia/nemotron-3-nano-30b-a3b",
  "embed_model": "nvidia/nemotron-3-embed-1b"
}'

Pass the chat and embedding model names served by Inference Gateway. All four model roles call IGW through provider (workspace/name), with optional chat_provider / embed_provider overrides. Do not pass raw NVIDIA_API_KEY.

Equivalent helper:

nemo data-designer retrieval generate \
  --corpus default/my-docs \
  --provider default/nvidia-build \
  --chat-model nvidia/nemotron-3-nano-30b-a3b \
  --embed-model nvidia/nemotron-3-embed-1b

Output fileset includes Q&A JSONL and generation_result.json.

Preview without a full job:

nemo data-designer retrieval-preview --spec '{"generate":{"corpus":"default/my-docs","provider":"default/nvidia-build","artifact_extraction_model":"nvidia/nemotron-3-nano-30b-a3b","qa_generation_model":"nvidia/nemotron-3-nano-30b-a3b","quality_judge_model":"nvidia/nemotron-3-nano-30b-a3b","embed_model":"nvidia/nemotron-3-embed-1b"},"num_records":1}'

Prepare (Stage 1)

Conversion (CPU) produces eval_beir/ and training JSON. GPU mining is off by default. Skip SDG by pointing sdg_input at a fileset containing an existing generation_result.json, or at hf://nvidia/Retrieval-Synthetic-NVDocs-v1@<revision>.

nemo data-designer retrieval-prepare --spec '{
  "sdg_input": "default/stage0-out"
}'

Enable mining (GPU, ~40GB) with "enable_mining": true. The model field identifies a platform model entity whose fileset is downloaded by nmp-customizer-tasks into the shared job storage. nmp-automodel-training then loads the encoder and tokenizer from that local directory with Hugging Face networking disabled. Conversion-only prepare stays on nmp-cpu-tasks.

All retrieval steps use the same container-backed execution profile so they share job storage across generation, conversion, model staging, and mining. Configure it with data_designer.job_executor_profile, or pass --profile for an individual job. The local platform uses gpu; Kubernetes uses default.

Chain generate then prepare

nemo data-designer retrieval-run --spec '{
  "generate": {
    "corpus": "default/my-docs",
    "provider": "default/nvidia-build",
    "artifact_extraction_model": "nvidia/nemotron-3-nano-30b-a3b",
    "qa_generation_model": "nvidia/nemotron-3-nano-30b-a3b",
    "quality_judge_model": "nvidia/nemotron-3-nano-30b-a3b",
    "embed_model": "nvidia/nemotron-3-embed-1b"
  },
  "prepare": {}
}'

This is jobs-service multi-step execution (CPU then optional GPU), not Data Designer in-config workflow chaining.

SDK

from nemo_platform import NeMoPlatform
from nemo_data_designer_plugin.jobs.retrieval_spec import RetrievalGenerateJobConfig
client = NeMoPlatform(base_url="http://localhost:8080", workspace="default")
client.data_designer.retrieval_generate(
    RetrievalGenerateJobConfig(
        corpus="default/my-docs",
        provider="default/nvidia-build",
        artifact_extraction_model="nvidia/nemotron-3-nano-30b-a3b",
        qa_generation_model="nvidia/nemotron-3-nano-30b-a3b",
        quality_judge_model="nvidia/nemotron-3-nano-30b-a3b",
        embed_model="nvidia/nemotron-3-embed-1b",
    )
)

Next Steps

  • See the Data Designer CLI for configuration and command details.
  • Use the Stage 1 training.jsonl artifact as input to an embedding or reranking customization job.