Instructions to use gohumanize/gohumanize-open-humanizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gohumanize/gohumanize-open-humanizer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="gohumanize/gohumanize-open-humanizer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gohumanize/gohumanize-open-humanizer")
model = AutoModelForCausalLM.from_pretrained("gohumanize/gohumanize-open-humanizer", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gohumanize/gohumanize-open-humanizer with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gohumanize/gohumanize-open-humanizer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gohumanize/gohumanize-open-humanizer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf gohumanize/gohumanize-open-humanizer:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf gohumanize/gohumanize-open-humanizer:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Use Docker

docker model run hf.co/gohumanize/gohumanize-open-humanizer:Q4_K_M

LM Studio
Jan

vLLM

How to use gohumanize/gohumanize-open-humanizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gohumanize/gohumanize-open-humanizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gohumanize/gohumanize-open-humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gohumanize/gohumanize-open-humanizer:Q4_K_M

SGLang

How to use gohumanize/gohumanize-open-humanizer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "gohumanize/gohumanize-open-humanizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gohumanize/gohumanize-open-humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "gohumanize/gohumanize-open-humanizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gohumanize/gohumanize-open-humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use gohumanize/gohumanize-open-humanizer with Ollama:
```
ollama run hf.co/gohumanize/gohumanize-open-humanizer:Q4_K_M
```
Unsloth Desktop

How to use gohumanize/gohumanize-open-humanizer with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gohumanize/gohumanize-open-humanizer:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gohumanize/gohumanize-open-humanizer with Docker Model Runner:
```
docker model run hf.co/gohumanize/gohumanize-open-humanizer:Q4_K_M
```

Lemonade

How to use gohumanize/gohumanize-open-humanizer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gohumanize/gohumanize-open-humanizer:Q4_K_M

Run and chat with the model

lemonade run user.gohumanize-open-humanizer-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use gohumanize/gohumanize-open-humanizer with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default gohumanize/gohumanize-open-humanizer:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gohumanize/gohumanize-open-humanizer with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gohumanize/gohumanize-open-humanizer:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "gohumanize/gohumanize-open-humanizer:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

GoHumanize Open Humanizer

A small open model that rewrites AI-styled English prose into more natural human writing. It is a full fine-tune of Qwen3-4B (all four billion weights updated) on 2,957 pairs of (AI-styled passage, human original). The human side is public domain: 2,000 passages from books on Project Gutenberg and 957 passages of modern prose from US federal agencies.

The 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: careful dataset preparation, transformation of source text into training pairs, model fine-tuning, evaluation and iterative improvement. It is not intended to reproduce the architecture, data, configuration or performance of the production systems, and it makes no claim about AI detectors.

Everything about the project (dataset, code, evaluation, write-up): https://gohumanize.ai/open-model

How often it rewrites

A humanizer can fail by handing its input back almost unchanged. On 300 held-out passages, three tries each at temperature 0.9:


Modern passages returned nearly unchanged (more than 90% of the words kept), from 59 articles never seen in training	17.0%
Book passages returned nearly unchanged	13.3%
Modern rewrites that lose a number	28.5%
Invented datelines, footnote numbers or links, of 900 outputs	0

The training pairs were built so the model learns to restructure sentences, not just swap words, and press-release datelines and footnote numbers were removed from the targets so the model does not learn to invent them. The write-up explains how.

Use

Chat format, thinking disabled. The system prompt below is the one used in training.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo = "gohumanize/gohumanize-open-humanizer"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

SYSTEM = ("Rewrite the following text so that it reads as if a person wrote it: varied sentence "
          "length, concrete wording, natural rhythm, no filler transitions. Keep the meaning, the "
          "facts and the order of ideas. Return only the rewritten text.")
messages = [{"role": "system", "content": SYSTEM},
            {"role": "user", "content": "It is worth noting that the committee ultimately reached a consensus."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=600, temperature=0.9, top_p=0.9, do_sample=True)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

On modern text, a single try still comes back nearly unchanged about one time in six. Generating a few tries and keeping the one that changed most (the MCP server and Python client do this for you) takes that close to zero.

Run it locally without a GPU using the GGUF builds in gguf/:

ollama run hf.co/gohumanize/gohumanize-open-humanizer:Q4_K_M

Also available: an MCP server (npx gohumanize-open-humanizer-mcp), a Python client (pip install gohumanize-open-humanizer) and a browser demo at https://gohumanize.ai/open-model.

Training


Base model	Qwen/Qwen3-4B (Apache-2.0)
Method	Full fine-tune with Unsloth: every weight updated in bf16, 8-bit AdamW, gradient checkpointing
Data	2,957 train / 300 test pairs, see the dataset card
Loss	on the assistant (human target) tokens only
Epochs, LR, batch	2 epochs, 2e-5 cosine, effective batch 16, max 1,024 tokens, seed 13
Hardware	1x NVIDIA H100 (80 GB) on Modal, 11 minutes of training
Tracking	Weights & Biases run
Final losses	train 1.02, eval 1.19

Evaluation

The untouched base model and the Open Humanizer on the 300 held-out passages (100 modern, 200 from books), against the human original.

Measure (300 held-out pairs)	AI-styled input	Base Qwen3-4B	Open Humanizer	Human target
BERTScore F1 vs human (higher = closer meaning)	0.918	0.903	0.929
ROUGE-L vs human (higher = closer wording)	0.522	0.434	0.580
Names/capitalised tokens kept (recall)	0.601	0.610	0.652
Length ratio vs human	1.16	0.85	0.95	1.00
Contractions per 100 words	0.33	0.69	0.08	0.16
Transition words per 100 words	1.13	0.02	0.11	0.15
Stock LLM phrases per text	0.70	0.01	0.03	0.02
Average sentence length (words)	23.2	15.2	21.5	25.8

These measures cannot see whether the model rewrote at all: an output identical to the AI-styled input scores well on meaning and wording. That is why the rewrite rate above is measured separately.

These measure how far the output moves from AI-styled prose towards the human target. They are not detector scores.

Limitations

Two thirds of the human targets are pre-1929 books and the rest is US government writing, so the model leans literary on one side and official on the other. Casual modern writing is the gap.
About three in ten rewrites of number-heavy text lose at least one figure. Some of that is rewording ("20 percent" as "a fifth"), some is loss. Check figures and other facts against the source.
Trained on 80 to 300 word passages; rewrite long documents paragraph by paragraph.
English only.
Educational demo; no claims about AI detectors.

Links

Resource	Link
Project page and browser demo	gohumanize.ai/open-model
GoHumanize (the product this research comes from)	gohumanize.ai
Model weights and GGUF builds	gohumanize/gohumanize-open-humanizer
Dataset, 3,257 pairs (CC-BY 4.0)	gohumanize/gohumanize-open-humanizer-dataset
Code and full pipeline	GoHumanize-ai/gohumanize-open-humanizer
Write-up: every step, service and result	docs/paper.md
Archived release, citable DOI	10.5281/zenodo.22843083
Python client and CLI	pypi.org/project/gohumanize-open-humanizer
MCP server for AI assistants	npm · source
Training run, loss curves and config	Weights & Biases

Licence and citation

Apache-2.0. Cite as:

GoHumanize team (2026). GoHumanize Open Humanizer: an open text-humanization model, dataset and pipeline built from public-domain data. Version 0.2.0. Zenodo. https://doi.org/10.5281/zenodo.22843083

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Links

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