Open Model & Research
A small open model that rewrites AI-styled text into more natural human prose, built from public-domain books and published with everything needed to study or reproduce it: dataset, code, evaluation and a step-by-step write-up.
The GoHumanize Open Humanizer is a public research and educational model created to demonstrate the general approach used to develop AI text humanization systems. It is separate from the production models used by GoHumanize.ai, but it reflects many of the same high-level principles we follow when developing our technology, including careful dataset preparation, transformation of source text into training pairs, model fine-tuning, evaluation, and iterative improvement.
By publishing the model, dataset, code, methodology, and development process, we aim to provide developers and researchers with a practical example of how a humanization model can be built and studied. The open model is not intended to reproduce the exact architecture, datasets, training configuration, or performance of GoHumanize.ai's production systems.
The Open Humanizer makes no claim about passing AI detectors. Its purpose is to show how such a tool is developed so that others can learn from and build on the work.
Qwen3-4B fine-tune on Hugging Face (Apache-2.0), with the LoRA adapter and a GGUF build for Ollama and LM Studio.
2,000 training and 200 test pairs of AI-styled text and its human original, from 47 public-domain books (CC-BY 4.0).
Try the model right here on this page, in the box below.
The complete pipeline on GitHub: sourcing, cleaning, AI-fication, training, evaluation and serving.
A paper-style explanation of every step, every service used and why, with results and limitations.
Call the model from AI assistants and agents with one npm package.
pip install gohumanize-open-humanizer: a small client and command-line tool.
A permanent, citable snapshot of the code, paper and dataset on Zenodo: doi.org/10.5281/zenodo.22843083.
Paste AI-styled English text (up to 400 words) and see the open model's rewrite. The endpoint sleeps when idle, so the first request can take two to three minutes.
50 / 400 words
Passages of 80 to 300 words from 47 English-language books on Project Gutenberg, all published before 1929. Translations were excluded so no newer copyright is involved. Boilerplate and typographic artefacts were stripped.
Each human passage was rewritten in the register typical of large language models (formal, smooth, hedged, connective phrases) by three different generators and four rotating style prompts. The human original stays the target.
Qwen3-4B was trained with QLoRA (a 4-bit base model and a small rank-16 adapter) on the 2,000 pairs, with the loss applied only to the human target. Training ran on a single rented GPU for about an hour.
The fine-tuned model is compared with the untouched base model on 200 held-out pairs: how much meaning survives (BERTScore, ROUGE-L, name recall) and how the style moves towards the human target (contractions, transition words, stock phrases, sentence length).
Weights, dataset and demo on Hugging Face, code on GitHub, an archived copy with a DOI on Zenodo, an MCP server on npm and a client on PyPI, so every piece can be reused independently.
Each step relies on an off-the-shelf service. The write-up explains each in more depth; in short:
Free public-domain books; the only source of human text in the dataset.
Three different language models produce the AI-styled side of each pair, so the model does not learn the quirks of one provider.
On-demand GPUs for training and for serving the demo endpoint; it scales to zero when idle.
Records every training run (loss curves, settings) so results are traceable.
Hosts the model weights, the dataset and the browser demo.
Archives a release snapshot with a permanent DOI for citation.
If you use the model or the dataset, please cite:
GoHumanize team (2026). GoHumanize Open Humanizer: an open text-humanization model, dataset and pipeline built from public-domain data. Version 0.1.0. Zenodo. https://doi.org/10.5281/zenodo.22843083
Model and code: Apache-2.0. Dataset: CC-BY 4.0.