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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](https://huggingface.co/NGARiAI/ngari-ft-distilled)** and **[ngari-tool](https://huggingface.co/NGARiAI/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
```python
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](https://github.com/NGARiAI/ns-bos-kernel)** for the Apache 2.0 kernel.
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