Mindket

stuntd

Newversion 1 · updated

Overview

A browser demo that answers questions two ways, with the base Laya model zero-shot and with a small head that stuntd trained for each decision, across support triage, banking and a coding-agent command gate.

stuntd is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python 3.12, with pip
  • The Python packages that requirements.txt lists
Runs in
  • Web browser: the Space's page on Hugging Face, with nothing to install
  • Windows, macOS or Linux: a local copy that runs on Python 3.12
  • Hugging Face Spaces: where the hosted demo runs, on the zero-a10g hardware tier
Start with
pip install -U huggingface_hub
hf download pollix/stuntd --repo-type space --local-dir stuntd
cd stuntd

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

stuntd is a browser demo that answers one question two ways. The base Laya model answers zero-shot, and a head that stuntd trained for that decision answers too. It runs three demos from the stuntd project, covering support triage, banking and a command gate for coding agents. For each question it shows the confidence and the threshold that decide which answer wins.

Who it suits

It suits people who want to see whether a small trained head beats a zero-shot model on a narrow decision.

Who might use this demo
IndustryWho uses itHow they use itExample
Customer supportSupport teamsSee ticket triage decided two waysPick a support triage question
BankingProduct teamsCompare answers on banking questionsOpen a banking example and read both answers
Developer toolsAgent buildersCheck a command gate for a coding agentTry an example command in the gate demo
Machine learningResearchersJudge a trained head against its base modelCompare the confidence with the threshold

If you want a general assistant, you can skip it, since it answers questions only inside its three demos.

Where to use it

You can use it online or run a copy yourself.

  • Web browser: the Space's page on Hugging Face, with nothing to install
  • Windows, macOS or Linux: a local copy that runs on Python 3.12
  • Hugging Face Spaces: where the hosted demo runs, on the zero-a10g hardware tier
What the demo is good for
Use caseWhoWhat they get back
Compare zero-shot and trainedResearchersTwo answers to one question, side by side
See the decision ruleEngineersThe confidence and the threshold that pick the answer
Try realistic examplesProduct teamsExample states that were kept out of training

How to set it up

The hosted demo needs nothing. A local copy needs Python 3.12 and pip.

  • Python 3.12, with pip
  • The Python packages that requirements.txt lists
  1. Install the huggingface_hub package.
  2. Download the Space's files into a folder.
  3. Move into that folder.
  4. Install Gradio 6.28.0.
  5. Install the packages from requirements.txt.
  6. Start the app with Python.
pip install -U huggingface_hub
hf download pollix/stuntd --repo-type space --local-dir stuntd
cd stuntd
pip install gradio==6.28.0
pip install -r requirements.txt
python app.py

How to use it

Open the Space online, or start your copy with the command below. Then pick one of the three demos, choose an example question and compare the two answers.

python app.py
The parts of the demo
ToolWhat it does
Support triage demoDecides how to route a support question
Banking demoAnswers a banking question
Coding-agent command gateDecides on a command that a coding agent wants to run
heads folderHolds a head file and a meta file for each site of each demo
presets.jsonHolds the example states that the demos offer

Example

You choose a banking demo and pick one of its example questions. The page shows what base Laya answers zero-shot, what the trained head answers, and the confidence and threshold that decide which of the two answers.

For newcomers and experts

If you are new to this, pick one demo and read the two answers for a few examples. Notice where they agree and where they split.

If you know the field, each head was trained with stuntd's train command on 3000 generated rows per demo, with 2400 used for training and 600 held out, over 24 epochs. The example states come from each demo client's seed and are absent from the training rows.

  1. You pick a demo and an example
  2. Base Laya answers zero-shot
  3. The trained head answers
  4. Confidence and threshold pick the answer

Good to know

The examples were kept out of training, so they test the heads fairly.

Generated rows per demo
Used for training2400 rows
Held out600 rows
  • Hosting: the hosted copy runs on the zero-a10g hardware tier
  • Training: 24 epochs for each head
  • License: apache-2
  • Cost to run: none named
  • Data it reaches: the example questions you pick and the convaiinnovations/laya model it names

README and files

README

stuntd

A browser demo that answers questions two ways, with the base Laya model zero-shot and with a small head that stuntd trained for each decision, across support triage, banking and a coding-agent command gate.

Made by pollix on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: stuntd is a browser demo that answers one question two ways. The base Laya model answers zero-shot, and a head that stuntd trained for that decision answers too. It runs three demos from the stuntd project, covering support triage, banking and a command gate for coding agents. For each question it shows the confidence and the threshold that decide which answer wins.

Who it suits: It suits people who want to see whether a small trained head beats a zero-shot model on a narrow decision.

Title: Who might use this demo
Industry | Who uses it | How they use it | Example
Customer support | Support teams | See ticket triage decided two ways | Pick a support triage question
Banking | Product teams | Compare answers on banking questions | Open a banking example and read both answers
Developer tools | Agent builders | Check a command gate for a coding agent | Try an example command in the gate demo
Machine learning | Researchers | Judge a trained head against its base model | Compare the confidence with the threshold

If you want a general assistant, you can skip it, since it answers questions only inside its three demos.

Where to use it: You can use it online or run a copy yourself.

  • Web browser: the Space's page on Hugging Face, with nothing to install
  • Windows, macOS or Linux: a local copy that runs on Python 3.12
  • Hugging Face Spaces: where the hosted demo runs, on the zero-a10g hardware tier
Title: What the demo is good for
Use case | Who | What they get back
Compare zero-shot and trained | Researchers | Two answers to one question, side by side
See the decision rule | Engineers | The confidence and the threshold that pick the answer
Try realistic examples | Product teams | Example states that were kept out of training

How to set it up: The hosted demo needs nothing. A local copy needs Python 3.12 and pip.

  • Python 3.12, with pip
  • The Python packages that requirements.txt lists
  1. Install the huggingface_hub package.
  2. Download the Space's files into a folder.
  3. Move into that folder.
  4. Install Gradio 6.28.0.
  5. Install the packages from requirements.txt.
  6. Start the app with Python.
pip install -U huggingface_hub
hf download pollix/stuntd --repo-type space --local-dir stuntd
cd stuntd
pip install gradio==6.28.0
pip install -r requirements.txt
python app.py

How to use it: Open the Space online, or start your copy with the command below. Then pick one of the three demos, choose an example question and compare the two answers.

python app.py
Title: The parts of the demo
Tool | What it does
Support triage demo | Decides how to route a support question
Banking demo | Answers a banking question
Coding-agent command gate | Decides on a command that a coding agent wants to run
heads folder | Holds a head file and a meta file for each site of each demo
presets.json | Holds the example states that the demos offer

Example: You choose a banking demo and pick one of its example questions. The page shows what base Laya answers zero-shot, what the trained head answers, and the confidence and threshold that decide which of the two answers.

For newcomers and experts: If you are new to this, pick one demo and read the two answers for a few examples. Notice where they agree and where they split.

If you know the field, each head was trained with stuntd's train command on 3000 generated rows per demo, with 2400 used for training and 600 held out, over 24 epochs. The example states come from each demo client's seed and are absent from the training rows.

You pick a demo and an example
Base Laya answers zero-shot
The trained head answers
Confidence and threshold pick the answer

Good to know: The examples were kept out of training, so they test the heads fairly.

Title: Generated rows per demo
Used for training: 2400 rows
Held out: 600 rows
  • Hosting: the hosted copy runs on the zero-a10g hardware tier
  • Training: 24 epochs for each head
  • License: apache-2
  • Cost to run: none named
  • Data it reaches: the example questions you pick and the convaiinnovations/laya model it names

On Hugging Face, 13 November 2026

  • Likes: 4
  • Built with: gradio
  • License: apache-2.0

Links

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.

Files
README.md  5 KB

1 file, 5 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

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Numbers as Mindket last read them, on 2026-11-12.

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