--- title: "Mesmer AI Detector 4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/be655a6e-0ad4-4c45-a98a-174b1674ac44" updated: "2026-11-13" --- # Mesmer AI Detector 4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Tells whether English text is human-written, AI-written or AI-edited and labels each sentence. For editors, teachers and researchers who want a signal, not proof, about how a text was made. Mesmer AI Detector 4B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by mesmertech ## About ### What it is Mesmer AI Detector 4B checks English text and says whether it looks human-written, AI-written or AI-edited, meaning human writing that an AI polished. It also labels each sentence. It is fine-tuned from Qwen3.5-4B-Base, and a bigger 9B sibling exists for higher accuracy. ### Who it suits It suits editors, teachers and researchers who want a signal about how a piece of English text was produced. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Publishing | Editors | Screen submissions for machine-written passages | Checking a freelance article sentence by sentence | | Education | Teachers | Look at essays alongside other evidence | Reviewing a student essay for AI-edited passages | | Research | Detection researchers | Compare detectors on public benchmarks | Re-running tests on Epoch-style imitation texts | You can skip it if your text is not in English, or if you need a verdict you can treat as proof, since it does flag some human writing. ### Where to use it It runs as a Python script or library on your own machine, with a GPU preferred. The card says CPU works but slowly, and it points to a hosted Space for trying it without a GPU. - A command line, through the detector script - Python code that imports the Detector class - Windows, macOS or Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Whole-text check | Editors | A human, AI-written or AI-edited verdict with probabilities | | Sentence labelling | Teachers | Each sentence marked and shaded by the model | | Benchmark testing | Researchers | Scores to compare against other detectors | ### How to set it up You need Python and the libraries listed in the quickstart. An optional package adds faster GPU kernels, and without it the code falls back to plain PyTorch. Hugging Face lists about 8.4 GB in BF16. - Python - The libraries torch, numpy, tokenizers, safetensors, emoji and huggingface\_hub - Optional fla-core for faster GPU work - A GPU is preferred 1. Install the required Python libraries. 2. Optionally install fla-core for faster kernels. 3. Download the detector script from the repository. 4. Run it with the model name and the text to check. ```bash pip install torch numpy tokenizers safetensors emoji huggingface_hub pip install fla-core # optional: faster GPU kernels (otherwise pure PyTorch; CPU works too, but slowly) hf download mesmertech/mesmer-ai-detector-4b mesmer_detector.py --local-dir . python mesmer_detector.py --model mesmertech/mesmer-ai-detector-4b "Paste the text to check here." ``` ### How to use it Run the script with your text, or load the Detector class in Python. Passing int8=True loads 8-bit weights that take about half the memory. ```python from mesmer_detector import Detector det = Detector.from_pretrained("mesmertech/mesmer-ai-detector-4b") # int8=True: 8-bit weights, about half the memory ``` | Tool | What it does | | --- | --- | | mesmer\_detector.py | Command line script that scores the text you pass in | | Detector | Python class that loads the model for your own code | ### Example You paste a paragraph into the command line script. It returns whether the text reads as human-written, AI-written or AI-edited, with a label for every sentence. ### For newcomers and experts If you are new, treat the result as a hint, not proof. Try it on a paragraph you wrote yourself and one from a chatbot to see how it labels each. If you know the field, it is a 4.2B model with a three-way output. On the Open Pangram in-domain set it scores 0.918 ternary accuracy, close to the closed Pangram 3.2 at 0.920. The card says it flags more human writing than Pangram, for example 0.044 false positives on non-native English essays. English text goes in → Model scores the whole text → Three-way label with probabilities → Each sentence labelled and shaded ### Good to know The card ranks it above every other open-weight detector it compared, and concedes a higher false-flag rate on human text. - Cost to run: no API key, account or paid service is named, but a GPU helps a lot - Data it reaches: the text you paste in, plus files downloaded from Hugging Face - Not for: non-English text, commercial use, or treating a verdict as proof about a person In-domain ternary accuracy | Label | Value | | --- | --- | | Open Pangram | 0.895 | | Mesmer 4B | 0.918 | | Pangram 3.2 closed | 0.920 | | Mesmer 9B | 0.923 | ## README ````markdown # Mesmer AI Detector 4B Tells whether English text is human-written, AI-written or AI-edited and labels each sentence. For editors, teachers and researchers who want a signal, not proof, about how a text was made. Made by mesmertech on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Mesmer AI Detector 4B checks English text and says whether it looks human-written, AI-written or AI-edited, meaning human writing that an AI polished. It also labels each sentence. It is fine-tuned from Qwen3.5-4B-Base, and a bigger 9B sibling exists for higher accuracy. Who it suits: It suits editors, teachers and researchers who want a signal about how a piece of English text was produced. ```table Industry | Who uses it | How they use it | Example Publishing | Editors | Screen submissions for machine-written passages | Checking a freelance article sentence by sentence Education | Teachers | Look at essays alongside other evidence | Reviewing a student essay for AI-edited passages Research | Detection researchers | Compare detectors on public benchmarks | Re-running tests on Epoch-style imitation texts ``` You can skip it if your text is not in English, or if you need a verdict you can treat as proof, since it does flag some human writing. Where to use it: It runs as a Python script or library on your own machine, with a GPU preferred. The card says CPU works but slowly, and it points to a hosted Space for trying it without a GPU. - A command line, through the detector script - Python code that imports the Detector class - Windows, macOS or Linux with Python ```table Use case | Who | What they get back Whole-text check | Editors | A human, AI-written or AI-edited verdict with probabilities Sentence labelling | Teachers | Each sentence marked and shaded by the model Benchmark testing | Researchers | Scores to compare against other detectors ``` How to set it up: You need Python and the libraries listed in the quickstart. An optional package adds faster GPU kernels, and without it the code falls back to plain PyTorch. Hugging Face lists about 8.4 GB in BF16. - Python - The libraries torch, numpy, tokenizers, safetensors, emoji and huggingface_hub - Optional fla-core for faster GPU work - A GPU is preferred 1. Install the required Python libraries. 2. Optionally install fla-core for faster kernels. 3. Download the detector script from the repository. 4. Run it with the model name and the text to check. ```bash pip install torch numpy tokenizers safetensors emoji huggingface_hub pip install fla-core # optional: faster GPU kernels (otherwise pure PyTorch; CPU works too, but slowly) hf download mesmertech/mesmer-ai-detector-4b mesmer_detector.py --local-dir . python mesmer_detector.py --model mesmertech/mesmer-ai-detector-4b "Paste the text to check here." ``` How to use it: Run the script with your text, or load the Detector class in Python. Passing int8=True loads 8-bit weights that take about half the memory. ```python from mesmer_detector import Detector det = Detector.from_pretrained("mesmertech/mesmer-ai-detector-4b") # int8=True: 8-bit weights, about half the memory ``` ```table Tool | What it does mesmer_detector.py | Command line script that scores the text you pass in Detector | Python class that loads the model for your own code ``` Example: You paste a paragraph into the command line script. It returns whether the text reads as human-written, AI-written or AI-edited, with a label for every sentence. For newcomers and experts: If you are new, treat the result as a hint, not proof. Try it on a paragraph you wrote yourself and one from a chatbot to see how it labels each. If you know the field, it is a 4.2B model with a three-way output. On the Open Pangram in-domain set it scores 0.918 ternary accuracy, close to the closed Pangram 3.2 at 0.920. The card says it flags more human writing than Pangram, for example 0.044 false positives on non-native English essays. ```flow English text goes in Model scores the whole text Three-way label with probabilities Each sentence labelled and shaded ``` Good to know: The card ranks it above every other open-weight detector it compared, and concedes a higher false-flag rate on human text. - Cost to run: no API key, account or paid service is named, but a GPU helps a lot - Data it reaches: the text you paste in, plus files downloaded from Hugging Face - Not for: non-English text, commercial use, or treating a verdict as proof about a person ```chart Title: In-domain ternary accuracy Open Pangram: 0.895 Mesmer 4B: 0.918 Pangram 3.2 closed: 0.920 Mesmer 9B: 0.923 ``` ## On Hugging Face, 13 November 2026 - Task: text classification - Parameters: 4.2B - Weights: about 8.4 GB in BF16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 68 - Likes: 3 - License: cc-by-nc-sa-4.0 - Languages: en - Based on: Qwen/Qwen3.5-4B-Base - Tags: safetensors, mesmer_ai_detector, ai-text-detection, ai-generated-text-detection, machine-generated-text-detection, ai-detector, text-classification ## Links - Model: https://huggingface.co/mesmertech/mesmer-ai-detector-4b - Files: https://huggingface.co/mesmertech/mesmer-ai-detector-4b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mesmertech/mesmer-ai-detector-4b ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [mesmertech/mesmer-ai-detector-4b on Hugging Face](https://huggingface.co/mesmertech/mesmer-ai-detector-4b) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Mesmer AI Detector 4B (this page) | 68 | 3 | 4.2B | cc-by-nc-sa-4.0 | 2026-11-05 | | [bge-reranker-v2-m3](https://mindket.com/p/5bc2430e-32f8-4153-8615-2e4e3bad70ea) | 14,685,230 | 1,301 | 568M | apache-2.0 | 2024-06-24 | | [deberta-v3-base-prompt-injection-v2](https://mindket.com/p/bbb131a8-e7e6-491b-b19e-25098c98a48d) | 476,298 | 129 | 184M | apache-2.0 | 2026-07-09 | | [GLiNER2.5-Decide](https://mindket.com/p/30566bb1-36c9-4a7b-8c06-5fcd446ef763) | 89,759 | 457 | 486M | apache-2.0 | 2026-10-08 | | [Kev-4B](https://mindket.com/p/68069a2c-2995-41a5-941c-ff7bf45f95ac) | 12,499 | 175 | — | apache-2.0 | 2026-10-01 | | [pplx-decider-flash-v1.2](https://mindket.com/p/ba207a37-74ec-4e43-aa10-b746d0ba8d5a) | 7,052 | 78 | 4.5B | apache-2.0 | 2026-10-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · mesmertech · 2026-11-05](https://news.ycombinator.com/item?id=50381455): Show HN: Training a SOTA Pangram-like detector on a 4070ti with Claude: This is kinda claude-slop but I was really interested in figuring out a way to break Pangram, and to do that I first needed to have a way to be able to do batches of text. And with a 4070ti in my basement, I thought why not just let Claude go ham on training a pangram-like model... and perhaps an… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mesmertech/mesmer-ai-detector-4b on Hugging Face](https://huggingface.co/mesmertech/mesmer-ai-detector-4b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/be655a6e-0ad4-4c45-a98a-174b1674ac44) - [Its page on Mindket](https://mindket.com/p/be655a6e-0ad4-4c45-a98a-174b1674ac44)