AI · alternatives · 2026
24 alternatives to 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 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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Data Bonsai: a Python package to clean your data with LLMs launched in 2024; newer entries below may have overtaken it.
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PrismML builds 1-bit and ternary Bonsai models that pack advanced reasoning, tool use, and multimodal AI into phones, laptops, and efficient data centers.
Mar 2026 · prismml.com · its alternatives →
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2021 · bonsaibrowser.com · its alternatives →
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Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com · its alternatives →
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I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!
2023 · github.com · its alternatives →
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After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com · its alternatives →
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Hi HN! I'm excited to share Autolabel, an open-source Python library to label and enrich text datasets with any Large Language Model (LLM) of your choice. We built Autolabel because access to clean, labeled data is a huge bottleneck for most ML/data science teams. The most capable LLMs are able to label data with high accuracy, and at a fraction of the cost and time compared to manual labeling. With Autolabel, you can leverage LLMs to label any text dataset with <5 lines of code. We’re eager for your feedback!
2023 · github.com · its alternatives →
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Transform data chaos into scientific clarity with Quilt, the AI-powered platform that accelerates research and ensures compliance for life sciences teams.
2017 · quiltdata.com · its alternatives →
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OpenSheet▲86Hi folks. I've been doing some experiments on how LLMs could get more handy in the day to day of working with files (CSV, Parquet, etc). Earlier last year, I built https://datakit.page and evolved it over and over into an all in-browser experience with help of duckdb-wasm. Got loads of feedbacks and I think it turned into a good shape with being an adhoc local data studio, but I kept hearing two main things/issues: Why can't the AI also change cells in the file we give to it? Why can't we modify this grid ourselves? So besides the whole READ and text-to-SQL flows, what seemed…
Feb 2026 · opensheet.app · its alternatives →
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Introducing 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:…
2025 · github.com · its alternatives →
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Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
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Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · github.com · its alternatives →
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
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G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com · its alternatives →
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Hey HN! We're excited to announce the launch of Tonic Textual, the secure data lakehouse for LLMs. Simply stated, Tonic Textual allows you to build generative AI systems on your own unstructured data without having to spend time extracting and standardizing your data. In minutes you can build automated, scalable unstructured data pipelines that extract, centralize, standardize, and enrich data from your documents into an AI-optimized format ready for embedding, fine-tuning, and ingesting into a vector database. While in-flight, we also scan for sensitive information and protect it via…
2024 · tonic.ai · its alternatives →
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Hey HN! We’re building FinetuneDB (https://finetunedb.com/), an LLM fine-tuning platform. It enables teams to easily create and manage high-quality datasets, and streamlines the entire workflow from fine-tuning to serving and evaluating models with domain experts. You can check out our docs here: (https://docs.finetunedb.com/) FinetuneDB exists because creating and managing high-quality datasets is a real bottleneck when fine-tuning LLMs. The quality of your data directly impacts the performance of your fine-tuned models, and existing tools didn’t offer an easy…
2024 · finetunedb.com · its alternatives →
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We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
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I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com · its alternatives →
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Business, project and billing management solution used by 500,000+ small businesses globally.
2016 · hellobonsai.com · its alternatives →
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Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x…
2023 · github.com · its alternatives →
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When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai · its alternatives →
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Just launched DataFuel.dev on Product Hunt last Sunday, and I landed in the top 3! I built this API after working on an AI chatbot builder. Scraping can be a pain, but we need clean markdown data for fine-tuning or doing RAG with new LLM models. DataFuel API helps you transform websites into LLM-ready data. I've already got my first paying users. Would love your feedback to improve my product and my marketing!
2024 · datafuel.dev · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →