Data Bonsai: a Python package to clean your data with LLMs
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
Data Bonsai: a Python package to clean your data with LLMs launched on April 27, 2024 with 47 votes, #678 of 4,347 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Data Bonsai is a Python package that uses large language models to clean and prepare data for machine learning projects. It handles categorization, text transformation, and conversion to structured formats while validating LLM outputs. The package includes a batch mode feature that processes multiple rows efficiently by sending prompts and schemas once rather than repeating them for each entry, significantly reducing token usage. It is designed for developers preparing datasets for fine-tuning.
written from the facts on this page · September 2026
From the sources
clean & curate your data with LLMs. Contribute to alvin-r/databonsai development by creating an account on GitHub.
In the maker’s words, at launch
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured…
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Bonsai – A Competitive Ternary Weight LLM2025 · github.com · ▲11Introducing Bonsai 0.5B, one of the first ternary-weight LLMs to rival full-precision models of similar size, such as Qwen 2.5 0.5B and MobileLLM 0.5B. Trained on just 3.8B tokens, using 1,000x less data than other models, Bonsai redefines what’s possible for ultra-efficient training in low-bit models. Next, we're building larger and more powerful ternary-weight models for the edge. Technical Report: https://github.com/deepgrove-ai/Bonsai/blob/main/paper/Bonsa... Model (Unpacked): https://huggingface.co/deepgrove/Bonsai Reach us:…
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