humanizer

A 12B model that rewrites AI-written drafts (emails, essays, reports, forum posts; English and Chinese) so they read like a person wrote them. It is trained to keep every number, unit, date, name and quote, and to add nothing. It runs locally. No AI detector was used anywhere in training.

Usage without the app · 不用 App 怎么用 · AGENTS.md (for AI agents) · GitHub · Desktop app (macOS, Windows) · Install guide · 中文说明

Setting this up with an AI agent? Point it at AGENTS.md: exact files, server command, prompt byte for byte, and a self-test.

Quick start

App: download the .dmg (Mac with Apple silicon) or the Windows installer from Releases. On first run it picks a model size for your memory and downloads it once; after that it works offline.

Command line, for long documents and agents: pipx install git+https://github.com/sgaofen/humanize-model, then hz paper.md -o paper.out.md (also .txt and .docx). It uses the app or a llama-server, keeps headings, code, tables and links, rewrites the prose piece by piece and flags any piece where a number went missing. See USAGE.md, section 14.

Without the app: pick a file below, then follow Usage without the app. The full guide, with a batch script, long documents, Chinese and troubleshooting, is USAGE.md (中文).

Files

File	Size	For
humanizer-12b-Q8_0.gguf	12,669,630,368 bytes (about 12.7 GB)	32 GB of memory or more. Recommended.
humanizer-12b-Q6_K.gguf	10,029,799,616 bytes (about 10.0 GB)	16 GB of memory.
humanizer-12b-Q4_K_M.gguf	7,625,160,896 bytes (about 7.6 GB)	The smallest 12B file, when memory or disk is tight.
humanizer-12b-bf16.gguf	23,832,049,568 bytes (about 23.8 GB)	Unquantised weights as one GGUF, for reference or for quantising yourself.
model.safetensors + config.json, generation_config.json, tokenizer.json, tokenizer_config.json	about 24 GB (bf16)	transformers, vLLM, converting to MLX.
prompt_format.json	tiny	The instruction and separator, verbatim.

In all three GGUF files the token embeddings and the output layer stay at 8-bit; Q6_K and Q4_K_M are also imatrix-calibrated on our own rewriting data. How close each is to bf16: KL over about 33,000 tokens of drafts and rewrites from the evaluation set (no overlap with the calibration data), and the fact judge from Results on all 420 English rewrites:

File	Mean KL vs. bf16	Top token same as bf16	Perplexity	No factual problem (English)
bf16 (reference)				368 / 420
Q8_0	0.0015	98.4%	+0.3%	376 / 420
Q6_K	0.0031	97.7%	+0.6%	364 / 420
Q4_K_M	0.0215	93.9%	+2.5%	363 / 419

Compared draft by draft with bf16, all three files are within noise on the fact judge. sha256 checksums are in USAGE.md.

Prompt format

Text completion, not chat. No system prompt, no turn markers. Send exactly this text and let the model continue:

Rewrite the text below so it reads like a person wrote it, not a language model.

Reorganize it as you see fit. Vary sentence length on purpose. Cut hedging,
throat-clearing, and any sentence that only announces what comes next.
Prefer the concrete word over the abstract one. It is fine to sound uneven.

Every fact, number, unit, date, name and quotation must survive unchanged.

<YOUR DRAFT, with leading and trailing whitespace removed>

### Rewritten:

• prompt = instr + "\n\n" + draft.strip() + "\n\n### Rewritten:\n\n"; both strings are in prompt_format.json. Reproduce it byte for byte: the first 16 hex characters of sha256(prompt for the draft "X") must be cc51d66b4c593fbe.
• Use the same English instruction for Chinese drafts.
• Stop on EOS only. No stop strings, especially not "###".
• Sampling: temperature 1.0, top-p 0.95, nothing else (top-k 0, min-p 0, repetition penalty 1.0). llama.cpp defaults to top-k 40 and min-p 0.05, and the bundled generation_config.json sets top-k 64, so switch them off explicitly.
• Context 8192 tokens for instruction + draft + rewrite. Split long documents at paragraph breaks (USAGE.md).
• Built-in defaults: since 2026-10-04 every GGUF file also stores these sampling settings in its metadata, so llama.cpp and apps built on it use them when a request sets none.
• Chat front ends: since 2026-10-04 the GGUF files carry a chat template that builds exactly this prompt from the last user message (system prompts and earlier turns are ignored). llama-server's /v1/chat/completions (with --jinja, the default in recent builds) then works, one draft per message: with the same seed it gave the same rewrite as the completion endpoint. Chat apps that use the file's template, such as LM Studio's Chat tab, should work the same way (not tested by us). Files downloaded earlier have no template, and the safetensors weights have none either.

Usage without the app

All snippets below build the prompt from prompt_format.json and use the sampling above. More detail for each runtime, a script that rewrites a whole folder, and a troubleshooting table: USAGE.md.

llama.cpp (recommended; macOS, Windows, Linux)

brew install llama.cpp            # or: winget install llama.cpp / a zip from github.com/ggml-org/llama.cpp/releases
pip install -U "huggingface_hub[cli]"
hf download jialinyyzz/humanizer humanizer-12b-Q8_0.gguf prompt_format.json --local-dir ./humanizer-model
#   16 GB machine: humanizer-12b-Q6_K.gguf instead
llama-server -m ./humanizer-model/humanizer-12b-Q8_0.gguf -c 8192 -np 1 -ngl 99 --host 127.0.0.1 --port 8080

import json, urllib.request

PF = json.load(open("humanizer-model/prompt_format.json", encoding="utf-8"))

def humanize(draft: str, url: str = "http://127.0.0.1:8080") -> str:
    body = {"prompt": PF["instr"] + "\n\n" + draft.strip() + PF["sep"],
            "temperature": 1.0, "top_p": 0.95, "top_k": 0, "min_p": 0, "repeat_penalty": 1.0,
            "n_predict": 2048}                    # no "stop": the model ends at EOS
    req = urllib.request.Request(url + "/completion", json.dumps(body).encode("utf-8"),
                                 {"Content-Type": "application/json"})
    with urllib.request.urlopen(req, timeout=900) as r:
        return json.load(r)["content"].strip()

print(humanize(open("draft.txt", encoding="utf-8").read()))

/completion works with every download. /v1/chat/completions also works with the GGUF files uploaded on or after 2026-10-04 (built-in chat template, see above), one draft per request. With curl and jq:

jq -n --rawfile d draft.txt --slurpfile f humanizer-model/prompt_format.json \
  '{prompt: ($f[0].instr + "\n\n" + ($d | sub("^\\s+"; "") | sub("\\s+$"; "")) + $f[0].sep),
    temperature: 1.0, top_p: 0.95, top_k: 0, min_p: 0, repeat_penalty: 1.0, n_predict: 2048}' \
| curl -s http://127.0.0.1:8080/completion -d @- | jq -r .content

MLX (Apple silicon, mlx-lm 0.32 or newer)

pip install -U "mlx-lm>=0.32" huggingface_hub
mlx_lm.convert --hf-path jialinyyzz/humanizer --mlx-path humanizer-mlx-8bit -q --q-bits 8 --q-group-size 64

import json
from huggingface_hub import hf_hub_download
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

PF = json.load(open(hf_hub_download("jialinyyzz/humanizer", "prompt_format.json"), encoding="utf-8"))
model, tok = load("humanizer-mlx-8bit")
draft = open("draft.txt", encoding="utf-8").read()
print(generate(model, tok, prompt=PF["instr"] + "\n\n" + draft.strip() + PF["sep"], max_tokens=2048,
               sampler=make_sampler(temp=1.0, top_p=0.95)).strip())

Pass a plain string; never apply the chat template (the mlx_lm.generate CLI needs --ignore-chat-template).

transformers (CUDA)

import json, torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jialinyyzz/humanizer"
PF = json.load(open(hf_hub_download(repo, "prompt_format.json"), encoding="utf-8"))
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")

draft = open("draft.txt", encoding="utf-8").read()
ids = tok(PF["instr"] + "\n\n" + draft.strip() + PF["sep"], return_tensors="pt").to(model.device)
out = model.generate(**ids, do_sample=True, temperature=1.0, top_p=0.95,
                     top_k=0,                    # switches off the top-k 64 in generation_config.json
                     max_new_tokens=2048)
print(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True).strip())

The bf16 weights are about 24 GB. Saved with transformers 5.14.1; on 4.x write torch_dtype= instead of dtype=.

vLLM

import json
from huggingface_hub import hf_hub_download
from vllm import LLM, SamplingParams

repo = "jialinyyzz/humanizer"
PF = json.load(open(hf_hub_download(repo, "prompt_format.json"), encoding="utf-8"))
llm = LLM(model=repo, dtype="bfloat16", max_model_len=8192,
          limit_mm_per_prompt={"image": 0, "audio": 0, "video": 0})   # text only
params = SamplingParams(temperature=1.0, top_p=0.95, top_k=-1, min_p=0.0,
                        repetition_penalty=1.0, max_tokens=2048)      # top_k=-1: off

drafts = [open(p, encoding="utf-8").read() for p in ["draft1.txt", "draft2.txt"]]
for r in llm.generate([PF["instr"] + "\n\n" + d.strip() + PF["sep"] for d in drafts], params):
    print(r.outputs[0].text.strip(), "\n---")

This mirrors how our evaluation outputs were generated. As a server: vllm serve jialinyyzz/humanizer --dtype bfloat16 --max-model-len 8192 --generation-config vllm, then /v1/completions (never /v1/chat/completions) with the same parameters; --generation-config vllm keeps the top-k 64 from generation_config.json out of the defaults.

Ollama

Ollama ignores the chat template stored in the GGUF and would apply its own Gemma template, which breaks this model. Use this Modelfile: its template takes the last user message as the draft and builds the prompt above (rendered with Go's text/template, byte for byte the same; not run in Ollama by us). Needs an Ollama version that supports Gemma 4 models.

FROM ./humanizer-12b-Q8_0.gguf
TEMPLATE """{{- $draft := "" }}{{- range .Messages }}{{- if eq .Role "user" }}{{- $draft = .Content }}{{- end }}{{- end }}Rewrite the text below so it reads like a person wrote it, not a language model.

Reorganize it as you see fit. Vary sentence length on purpose. Cut hedging,
throat-clearing, and any sentence that only announces what comes next.
Prefer the concrete word over the abstract one. It is fine to sound uneven.

Every fact, number, unit, date, name and quotation must survive unchanged.

{{ $draft }}

### Rewritten:{{ "\n\n" }}"""
PARAMETER temperature 1.0
PARAMETER top_p 0.95
PARAMETER top_k 0
PARAMETER min_p 0
PARAMETER repeat_penalty 1.0
PARAMETER num_ctx 8192
PARAMETER num_predict 2048

ollama create humanizer -f Modelfile
ollama run humanizer      # one draft per message, no leading or trailing blank lines
# or raw mode, which skips the template:
jq -n --rawfile d draft.txt --slurpfile f humanizer-model/prompt_format.json \
  '{model: "humanizer", raw: true, stream: false,
    prompt: ($f[0].instr + "\n\n" + ($d | sub("^\\s+"; "") | sub("\\s+$"; "")) + $f[0].sep),
    options: {temperature: 1.0, top_p: 0.95, top_k: 0, min_p: 0, repeat_penalty: 1.0,
              num_ctx: 8192, num_predict: 2048}}' \
| curl -s http://127.0.0.1:11434/api/generate -d @- | jq -r .response

LM Studio

Untested by us. Load the GGUF with an 8192-token context; in the model's sampling settings set Temperature 1.0, Top P 0.95, Top K 0, Min P 0, Repeat Penalty 1.0 and remove stop strings. With a file downloaded on or after 2026-10-04, the Chat tab should work: leave the system prompt empty and paste one draft per message (the built-in template ignores system prompts and earlier turns). With any download, start the local server and send the full prompt to the text-completion endpoint /v1/completions:

import json, urllib.request

PF = json.load(open("humanizer-model/prompt_format.json", encoding="utf-8"))
draft = open("draft.txt", encoding="utf-8").read()
body = {"model": "humanizer-12b-q8_0",           # replace with the model id shown in LM Studio
        "prompt": PF["instr"] + "\n\n" + draft.strip() + PF["sep"],
        "temperature": 1.0, "top_p": 0.95, "top_k": 0, "min_p": 0, "repeat_penalty": 1.0, "max_tokens": 2048}
req = urllib.request.Request("http://127.0.0.1:1234/v1/completions", json.dumps(body).encode("utf-8"),
                             {"Content-Type": "application/json"})
print(json.load(urllib.request.urlopen(req, timeout=900))["choices"][0]["text"].strip())

If a chat reply greets you, repeats the instruction or doesn't stop, the file has no built-in template (downloaded before 2026-10-04): download it again or use /v1/completions.

More

USAGE.md also covers: a one-shot llama-completion run, a script that rewrites a whole folder (splitting long files, resampling over-copied pieces, listing numbers to check), long documents, Chinese specifics, a quality checklist and troubleshooting. To verify a setup, run the self-test in AGENTS.md.

Before and after

Drafts from the held-out evaluation set. For each we generated 8 samples with the Q8_0 file (llama.cpp, the app's settings) and picked the one that reads best among those the fact judge passed; it is shown unedited, and all 8 samples are on GitHub. These are picks: across the whole set the judge flagged 44 of the 420 English rewrites, 135 problems in all, 125 of them a single word or phrase. Read the result before you send it, especially numbers, dates and names. Results over the whole set are below.

More examples (a forum answer, a Zhihu answer) are in the GitHub README.

Results

Evaluation set: 312 drafts (210 English, 102 Chinese), 18 genres, written from scratch by GLM-5.3, GPT-5.6 luna and Claude Sonnet (about a third each), never used in training. Two samples per draft.

AI detection (external check only): 95% judged human. Originality.ai, API v3, AI Allowance 0% (strictest), 2026-10-02, 210 English drafts, first sample each, bf16 weights: 11 of 210 rewrites flagged as AI.

Model	Flagged as AI	Judged human
humanizer 12B v2, this release (bf16)	11 / 210 (5%)	95%
humanizer 12B v1, previous release	26 / 210 (12%)	88%

Flagged less than half as often as the previous release: on the same drafts, 20 were flagged only for the previous release and 5 only for this one (paired test, p = 0.004). The humanizer-12b-Q8_0.gguf file measured 15 / 210 (7%), within noise of bf16 (p = 0.48).

Public baseline: the blader/humanizer skill (v3.1.0, 53k GitHub stars), applied by Claude Sonnet to the same 60 drafts: 60 / 60 flagged as AI (median AI score 100%). This release (bf16) on those 60: 4 / 60 flagged. Flagged as AI by genre (all 210 drafts, bf16, same setting): social posts with emoji, hashtags or "1/ 2/" threads 3 / 16, formal policy memos 2 / 13, paper sections 2 / 22, essays 2 / 38, work reports 1 / 20, forum answers 1 / 18, blog posts 0 / 16, Reddit posts 0 / 18, emails 0 / 35, product reviews 0 / 14 (total 11 / 210). The most templated genres are still the hardest. Detectors change; this is one measurement on one date, not a promise.

Fact fidelity. 376 of 420 English rewrites came back with no factual problem from a strict LLM judge (GLM-5.3, one vote per rewrite), measured on the humanizer-12b-Q8_0.gguf file you download. The previous release: 369 of 420.

	v2, this release (Q8_0 file)	v1, previous 12B
No factual problem found (higher is better)	376 / 420	369 / 420
Dropped a format element (lower is better)	28 / 420	35 / 420
Median reuse, overlap with the draft (lower is better)	0.165	0.19
Outputs with reuse > 0.5 (lower is better)	0.2%	1.0%

Where the judge did find a problem, the fix is usually small: a second pass re-read each flagged rewrite against its draft and listed every problem, down to small wording nuances, and more than 9 in 10 of those fixes (125 of 135) are a single word or phrase (for example, the draft's "The remaining 37 complaints" came out as "The other 37% of complaints"). Chinese is still catching up with English: no factual problem in 149 of 204 Chinese rewrites (previous release: 135); where there was one, about 9 in 10 fixes (212 of 236) are a single word or phrase ("本月20日前后", around the 20th of this month, became "20号以前", before the 20th). Still, read the result before you send it, especially numbers, dates and names.

Training

No AI detector was used anywhere in training: not as a reward, not as a filter, not to pick a checkpoint.

1. SFT, 28,598 pairs of AI draft → real human original. The human side is always real human writing (paper abstracts, government reports, student essays, company and mailing-list email, Reddit, Hacker News, Zhihu…); the AI side is a draft a frontier model wrote back from the human text.
2. DPO, 3,918 preference pairs, chosen only on fact fidelity and copying (LLM judge GLM-5.3).
3. GRPO in three rounds, 500 steps in total: 200 steps with a strict single-vote fact judge, then two rounds of 150 steps (v1 was released after the first of these, v2 after the second) (16 drafts × 8 samples per step, temperature 1.0). Reward: an LLM judge reads the whole rewrite against the draft (severe errors, invented content, changed meaning and dropped formatting cost), plus a copy penalty on 5-gram and syntactic-skeleton reuse (free below .22, then linear). Round 3 drew its drafts from a genre-balanced pool of 8,268. In all, RL produced 41,600 rewrites, each scored by an LLM judge against its draft.
4. This release (v2) = the final checkpoint of the last round.

Speed

Measured on an M5 Max. llama.cpp Q8_0 with Metal (what the app uses): about 36–38 tokens/s; a hundred-word email takes about 3.6 s, a Chinese email of about 300 characters about 8.5 s. MLX 8-bit: about 30 tokens/s in English and 38 tokens/s in Chinese; a hundred-word email takes about 9 s.

Limitations

• It can still change a detail: on our set the judge found no factual problem in 376 of 420 English rewrites, and more than 9 in 10 of the fixes it did list are a single word or phrase. Proofread numbers, dates and names.
• Chinese is still catching up with English (no factual problem in 149 of 204 Chinese rewrites; about 9 in 10 fixes are a single word or phrase).
• Templated genres are still the hardest for detectors: emoji/hashtag social posts (3 / 16 flagged) and policy memos (2 / 13).
• Formatting can change: 28 of 420 outputs dropped a format element; paragraph breaks, lists and headings sometimes merge or disappear.
• In casual genres it sometimes adds slang or profanity that wasn't in the draft.
• Detector results change over time. Nothing here guarantees any detector outcome.
• It is a writing tool for your own drafts. Where a school, employer or publication has rules about AI assistance, follow them.

License

Apache License 2.0, for both the weights and the code. Fine-tuned from google/gemma-4-12B, which Google releases under Apache 2.0. This project is not affiliated with or endorsed by Google.