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| license: agpl-3.0 | |
| language: | |
| - en | |
| task_categories: | |
| - text-generation | |
| tags: | |
| - agent-traces | |
| - synthetic | |
| - coding | |
| - reasoning | |
| size_categories: | |
| - 10K<n<100K | |
| # Parable corpus v2 | |
| The training corpus behind the [Parable](https://huggingface.co/collections/AnkitAI/parable) | |
| model series: genuine multi-step agent-session traces, deduplicated, | |
| decontaminated and mixed with general-instruction replay data. | |
| Released so that the models trained on it can be checked rather than taken | |
| on trust. Method and measurements: | |
| [doi:10.5281/zenodo.21676407](https://doi.org/10.5281/zenodo.21676407). | |
| ## Composition | |
| | Source | License | Role | | |
| |---|---|---| | |
| | [Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) | AGPL-3.0 | Claude Fable 5 agent sessions | | |
| | [Roman1111111/gpt5.5-terminal](https://huggingface.co/datasets/Roman1111111/gpt5.5-terminal) | MIT | terminal-session transcripts | | |
| | OpenCoder educational_instruct, tulu-3 mix | see upstream | general-instruction replay (~30%) | | |
| Processing applied: session reconstruction, secret scrubbing, exact and | |
| fuzzy deduplication, and fuzzy train/test decontamination against the | |
| benchmarks reported in the paper, with post-hoc containment measured rather | |
| than assumed. | |
| ## Licensing | |
| **This corpus is released under AGPL-3.0**, inherited from the | |
| Fable-5-traces portion, which is the most restrictive upstream term. Reusing | |
| this corpus carries that obligation forward. | |
| Because the traces originate from third-party assistants, those providers' | |
| terms may also apply to downstream training and distillation independently | |
| of this license. Confirm your use aligns with them before building on this | |
| commercially. | |
| ## Citation | |
| ```bibtex | |
| @misc{aglawe2026agenttrace, | |
| author = {Aglawe, Ankit}, | |
| title = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute}, | |
| year = {2026}, | |
| publisher = {Zenodo}, | |
| doi = {10.5281/zenodo.21676407}, | |
| url = {https://doi.org/10.5281/zenodo.21676407} | |
| } | |
| ``` | |