curator
Synthetic data curation for post-training and structured data extraction
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Synthetic data curation for post-training and structured data extraction
Good fit when
- Ideal for enhancing the performance of existing machine learning models through fine-tuning in natural language processing contexts
- Best suited when you require synthetic datasets to test or train models under varied conditions not present in real-world data
Avoid when
- Not recommended if your needs extend beyond NLP and you do not work with structured text data
- May not be the best choice for simple data generation tasks that do not benefit from complex synthetic dataset creation processes
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install curatorSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Python-based tool focused on generating synthetic datasets for improving machine learning models through fine-tuning and instruction tuning, especially useful in natural language processing contexts.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 19, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
response = trainer.sample("Explain recursion in Python")Source link
Tags
README
🛠️ Installation serverlessly, so this provisions an on demand deployment (takes a few minutes). response = trainer.sample("Explain recursion in Python") print(response) trainer.close() tear down the deployment when done `` FireworksTrainer FireworksTrainer format example() Tinke...
For agents
This page has a .md twin and JSON over the API.