e-p-armstrong/augmentoolkit
An end-to-end tool for generating fine-tuning datasets, training domain-expert LLMs, and serving them locally.
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Augmentoolkit transforms user documents into training datasets that update an LLM’s knowledge cutoff, enabling domain expertise in any chosen area. It generates fine-tuning data, optionally creates RAG-ready datasets, and can spin up a local inference server for the resulting model. The pipeline works fully offline without external API keys for data generation on most hardware.
Frequently asked
- What is e-p-armstrong/augmentoolkit?
- An end-to-end tool for generating fine-tuning datasets, training domain-expert LLMs, and serving them locally.
- Is augmentoolkit open source?
- Yes — e-p-armstrong/augmentoolkit is open source, released under the MIT license.
- What language is augmentoolkit written in?
- e-p-armstrong/augmentoolkit is primarily written in Python.
- How popular is augmentoolkit?
- e-p-armstrong/augmentoolkit has 1.9k stars on GitHub.
- Where can I find augmentoolkit?
- e-p-armstrong/augmentoolkit is on GitHub at https://github.com/e-p-armstrong/augmentoolkit.