nomeda-lab/fattah-golden-superset
Fattah Golden Fattah Golden is a large-scale, model-agnostic supervised fine-tuning (SFT) superset built by Nomeda Labs to train the Fattah family of coding and agentic coding models. The dataset is designed as a labeled superset with no baked-in training ratios. This means the stored dataset is the complete cleaned and annotated corpus. Researchers and practitioners choose their own mixture at training time by filtering on the boolean capability columns. Stats… See the full description on the dataset page: https://huggingface.co/datasets/nomeda-lab/fattah-golden-superset.
| Author / organization | nomeda-lab |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 455 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 31 May 2026 |
| Last updated at source | 1 June 2026 |
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