Datasets:

NGARiAI
/

ngari-datasets

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
  - question-answering
size_categories:
  - 1K<n<10K
tags:
  - ngari
  - sovereign-ai
  - edge-ai
  - synthetic-data
  - tool-use
  - distillation
configs:
  - config_name: synthetic_v1
    data_files: ngari_synthetic_v1.json
  - config_name: sft_v4
    data_files: ngari_sft_dataset_v4.json
  - config_name: tool_format
    data_files: ngari_tool_format_dataset.json

NGARi Training Datasets

NGARi-authored training datasets (Apache 2.0), generated on sovereign edge hardware with zero cloud dependency. These power the NGARi edge models: ngari-ft-distilled and ngari-tool.

The NGARi data engine

Data quality is the bottleneck for capable small models. NGARi uses a large teacher model (27B-class, e.g. qwen3.8-27B) as an automated data engine — generating diverse edge-cases, complex instructions, and niche domain knowledge — then filters and curates the output into high-fidelity training sets like these. No raw internet scrapes; every example is synthetic or hand-curated, so lineage is auditable and license-clean.

Datasets

Config	File	Size	Examples	Description
`synthetic_v1`	`ngari_synthetic_v1.json`	1.5 MB	2,000	Synthetic QA examples (qwen3:8b teacher)
`sft_v4`	`ngari_sft_dataset_v4.json`	0.4 MB	588	Hand-curated supervised fine-tune (SFT) data
`tool_format`	`ngari_tool_format_dataset.json`	0.4 MB	500	Tool-calling format data (incl. 20-example eval set)

All files are JSON arrays of chat-format objects ({"messages": [{"role": ..., "content": ...}, ...]}), ready for standard SFT tooling (TRL, Axolotl, llama-factory, etc.).

Loading

from datasets import load_dataset

ds = load_dataset("NGARiAI/ngari-datasets", "synthetic_v1")
sft = load_dataset("NGARiAI/ngari-datasets", "sft_v4")
tools = load_dataset("NGARiAI/ngari-datasets", "tool_format")

Provenance note

The historical dataset name ngari_gemma4_dataset was a misnomer — Google Gemma models were served only on NGARi hardware and never used in training. All data was generated with the Apache-2.0 Qwen2.5 lineage and a qwen3:8b synthetic teacher. Files are named truthfully (ngari_synthetic_v1).

Scope

These datasets are NGARi-authored and Apache 2.0, provided for training, evaluation, and research on sovereign edge AI. They are domain-focused (sovereign agent workflows), not broad-spectrum general-assistant data; use them alongside general domain data for general-purpose training.

Sovereign AI

Generated and verified on user-owned edge hardware with zero cloud dependency. Part of the NGARi Sovereign Business Operating System (SBOS) — see github.com/NGARiAI/ns-bos-kernel for the Apache 2.0 kernel.