The open model for marketing work
Doctors have an AI model built for medicine. Lawyers have one for law. Coders have several. Marketers have none. We are building one in the open and giving it away.
Free to download. Free to use. Free to sell what you build with it.
The gap
Every serious field has its own model. Marketing does not.
More than half of company AI models will be built for one field by 2027, according to Gartner, up from 1% in 2024. Marketing is the biggest discretionary spend in business, and the tools marketers use, Jasper and Copy.ai among them, all rent a general model from someone else.
What it does
Five jobs marketers do every day
It will not out-think Claude or Gemini, and we will not pretend it does. It is built to do a handful of repetitive jobs quickly, cheaply, and the same way every time.
creative_tag
Tag ad creative
Show it an ad. It tells you the hook, the angle, who it speaks to, what it offers, and where it sits in the funnel. Always the same labels, so you can count them.
ad_copy
Write ad copy
Say what you are selling and to whom. Get a headline, body text, and button label that already fit the platform's character limits.
metric_diagnosis
Diagnose a campaign
Paste your numbers. It names what is broken and what to fix first. If there is too little data to tell, it says so rather than guessing.
lead_qualify
Score a lead
Give it what you know about a lead. Get a score, a band, and the exact signals behind it. It never invents a reason it cannot point to.
chat
Answer questions
Ask anything about marketing. Get a direct answer, or a question back when a number it needs is missing.
ad_policy
Predict ad rejectionsmaybe
Would this creative be rejected, and why. Nothing does this today. We are still deciding whether it makes the first release.
Works with the tools you already use
It speaks the same language as the OpenAI library, so switching to it means changing one line. Or skip us entirely, download the model, and run it on your own computer.
from openai import OpenAI
client = OpenAI(base_url="https://api.gloofy.ai/v1")
client.chat.completions.create(
model="gloofy-1-nano",
messages=[{"role": "user",
"content": "Tag: Still paying 3% on card payments?"}],
)
# {"hook": "question", "angle": "pain_relief",
# "persona": "business_owner", "offer": "lead_magnet",
# "funnel_stage": "consideration"}The numbers
Every specification, and what each one means
Model makers publish a table like this and rarely explain it. Here is ours, with plain English underneath. None of these are measured yet, so treat them as the target we are building towards.
| Specification | gloofy-1 | gloofy-1-nano | gloofy-0 |
|---|---|---|---|
| Parameters | 8.2B | 1.7B | 561M |
| Architecture | Dense | Dense | Dense |
| Context window | 32,768 tokens | 32,768 tokens | 2,048 tokens |
| Vocabulary | 151,936 | 151,936 | 65,536 |
| Download (4-bit) | ~5 GB | ~1.1 GB | ~0.6 GB |
| Runs on a laptop | 16 GB RAM | 8 GB RAM | Anything |
| Licence | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Built from | Qwen3-8B | Qwen3-1.7B | Nothing |
Parameters
The adjustable numbers inside the model, learned during training. More usually means smarter, but slower and dearer to run. Ours has 8.2 billion. The largest open model today has 2.8 trillion.
Context window
How much text it can hold in mind at once. 32,768 tokens is roughly 90 pages. Go past it and the earliest text falls out of view.
Tokens
Models cannot read letters. Text is chopped into word-sized pieces first, and a token is one piece. Roughly four characters of English each.
Dense vs mixture-of-experts
Dense means every parameter is used for every word. Mixture-of-experts models switch on only a slice at a time, which is how a 2.8-trillion model can be affordable to run. Ours is dense because at this size it is simpler and faster.
Quantization
Storing each number with less detail so the file shrinks. Four-bit makes the download about four times smaller and costs a little accuracy. It is why an 8-billion model fits on a laptop.
Apache 2.0
A licence that lets you use it commercially, change it, and ship it inside your own product, without asking us or paying anyone.
Models
Three sizes, one of them a toy
Named now so the plan can be judged later. The weights go on Hugging Face for anyone to download, because a model you cannot have is not infrastructure.
gloofy-1
8.2BThe main one. Does all five jobs and holds a conversation.
gloofy-1-nano
1.7BSorting and labelling only. Small enough to run on a laptop, fast enough to tag a thousand ads while you wait.
gloofy-0
561MBuilt from nothing as a way to learn how. Published because the process is worth showing, not because you should use it.
marketing-bench
The test comes out before the model
Medicine has a shared exam for AI models, written by 262 doctors. Law has one. Marketing has nothing, so we are writing it, and publishing it first so we cannot quietly change the questions to suit our own answers.
Published before the model
The first scores recorded are from models we did not train. Any improvement later is measured against a number that existed before we tried.
Shows where we lose
Claude and Gemini sit on the same leaderboard. Where they beat gloofy, the table says so.
Wider than our model
It covers marketing work in general, not only the parts we happen to be good at. A test written around its own author is advertising, not measurement.
Anyone can rerun it
The questions and the scoring code are public. If you doubt a number, you can check it yourself.
Build log
Built in the open, from the first line of code
This began by writing a language model from scratch on a laptop, because you should not publish one without knowing what is inside it. Every step is written up with its real numbers, including the parts that failed.
A tokenizer, 50 million tokens, and a model that tells stories
Wrote the text-splitting algorithm from scratch, then trained six model designs against each other and let the results pick the winner. Wide and shallow beat deep and narrow. The winner trained for six hours and started writing stories with a beginning, a middle, and an end.
A language model from absolutely nothing
A small model trained on Shakespeare, on a laptop, in under five minutes. Nobody told it that plays put speaker names in capitals. It worked that out on its own from one megabyte of text.
Promises
Written down early so we can be held to them
The weights stay free
Published on Hugging Face for anyone to download. If we ever switch the website off, the model still works on your own machine.
No paywall
There is no paid plan and none is planned. If that ever changes, the free downloads do not.
No customer data
Nothing from any company or customer is used to train it. Every example is written by us or already public.
Honest about its size
This is a small model. It will not beat Claude or Gemini at general thinking, and this site will never suggest it does.