Polygl0t/gigaverbo-v2-synth
GigaVerbo-v2 Synth: A Synthetic Dataset for Portuguese Dataset Summary GigaVerbo-v2 Synth is a large synthetic Portuguese text corpus (~9.3 billion tokens) generated to complement the GigaVerbo-v2 web-sourced dataset. Inspired by approaches like Cosmopedia, this dataset was created to fill gaps in domains where web data is scarce or of lower quality, providing high-quality, diverse educational content. The dataset includes educational texts, tutorials, academic… See the full description on the dataset page: https://huggingface.co/datasets/Polygl0t/gigaverbo-v2-synth.
| Author / organization | Polygl0t |
|---|---|
| Downloads | 1.1K (as reported by Hugging Face) |
| Likes | 1 |
| Created | 15 February 2026 |
| Last updated at source | 5 March 2026 |
Popularity, last 3 days
1,079 now (+44 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- Polygl0t/Tucano2-qwen-3.7B-BaseModel · used to train
- mradermacher/Tucano2-0.6B-Base-GGUFModel · used to train
- Polygl0t/Tucano2-qwen-0.5B-BaseModel · used to train
- Tucano 2 Cool: Better Open Source LLMs for PortuguesePaper · cites
- mradermacher/GigaVerbo-v2-ablation-EDU-Synth-1.5B-GGUFModel · used to train
- Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5BModel · used to train
- mradermacher/GigaVerbo-v2-ablation-Synth-1.5B-GGUFModel · used to train
- Polygl0t/Tucano2-0.6B-BaseModel · used to train
- Polygl0t/Tucano2-qwen-1.5B-BaseModel · used to train
- mangaba-ai/Mangaba-Nano-1MModel · used to train
Recent changes
- 16 Oct 2026 Description: GigaVerbo-v2 Synth: A Synthetic Dataset for Portuguese Dataset Summary GigaVerbo-v2 Synth is a large synthetic Portuguese text corpus (~9.3 billion tokens) generated to complement the GigaVerbo-v2 web-sourced dataset. Inspired by approaches → GigaVerbo-v2 Synth: A Synthetic Dataset for Portuguese Dataset Summary GigaVerbo-v2 Synth is a large synthetic Portuguese text corpus (~9.3 billion tokens) generated to complement the GigaVerbo-v2 web-sourced dataset. Inspired by approaches
Changes detected by Silytics between refreshes of the source.
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