--- title: "Laplace-1-Vector" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fa25983a-e6a7-4f63-a4d1-12224e3e2331" updated: "2026-11-12" --- # Laplace-1-Vector > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores the options an agent could take, or predicts what happens next, before the action runs. A demo and MCP server for the 4B Laplace-1-Vector decision model, for agent builders. Laplace-1-Vector is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by JuneoAI ## About ### What it is Laplace-1-Vector is a 4B decision model from Juneo Lab that judges what an agent is about to do before it does it. This Space is a demo of it, and it also runs as an MCP server. The model scores options and predicts outcomes, and it never carries out an action. ### Who it suits Developers who build AI agents and want a check before an action runs, and teams that mix people and agents and want to compare options first. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI agents | Agent developers | Score the actions an agent may take before one runs | Rate a sub-agent request that reaches past its authority | | Operations | Teams of people and agents | Compare ways to split work between people and agents | Score candidate task splits for one project | | Purchasing | Teams that deal with suppliers | Judge how to answer a supplier agent that asks for data | Score the replies your policy allows | | Research | Machine learning researchers | Study a causal-interventional decision model | Run it on a state and read the spread over candidates | Anyone who wants a model that performs the action itself can skip it. ### Where to use it It works in these places. - A web browser, on the Space's page at Hugging Face - An MCP client, because the Space also runs as an MCP server - Windows, with Python 3.12 - macOS, with Python 3.12 - Linux, with Python 3.12 | Use case | Who | What they get back | | --- | --- | --- | | Score candidates | Agent developers | A probability for each candidate your application allows | | Predict the next observation | Agent designers | The generated outcome, shown before the action happens | | Call it as tools from an MCP client | Developers | Two tools, score\_candidates and predict\_observation | ### How to set it up Using the Space online needs only a browser. A copy on your own computer needs the following. - Python 3.12 and pip - The huggingface\_hub package, which gives you the hf command - Disk space for the repository, which holds 4 files 1. Install the huggingface\_hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install Gradio at the version the Space uses, then install the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download JuneoAI/Laplace-1-Vector --repo-type space --local-dir Laplace-1-Vector cd Laplace-1-Vector pip install gradio==6.17.3 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space's page, or run the app on your computer and open the page Gradio shows. Pick one of two modes, scoring candidates or predicting the next observation. The source names the MCP tools but gives no connection settings. ```sh python app.py ``` | Tool | What it does | | --- | --- | | score\_candidates | Takes a state, a question and your allowed candidates, and returns a distribution over exactly those candidates | | predict\_observation | Takes environment rules, context and an action, and generates the next observation | ### Example You describe a state where a sub-agent asks for more access than it was given, add a question about what to do, and list the responses your application allows. It returns a spread of probabilities over exactly those responses, from one forward pass. ### For newcomers and experts Before an AI agent acts, this model can tell you how likely each of your allowed options is. The first small thing to try is one of the examples set inside the demo. The model is described as causal-interventional. Scoring uses one forward pass over your candidates. The probabilities are relative to the list you supply and are not a calibrated chance of being correct. The next-observation mode generates text from the rules, context and action you give it. You give a state, a question and candidates โ†’ The model runs one forward pass โ†’ It returns a probability for each candidate โ†’ Your application decides what to do ### Good to know The model scores and predicts, so your own code stays in charge of what runs. - License: apache-2 - Cost to run: none named - Data it reaches: the state, rules and context you type or send - Not for: carrying out actions, and its numbers are not a calibrated chance of being correct - Hardware: the Space is set to zero-a10g at Hugging Face, and what your own computer needs is not stated ## README ````markdown # Laplace-1-Vector Scores the options an agent could take, or predicts what happens next, before the action runs. A demo and MCP server for the 4B Laplace-1-Vector decision model, for agent builders. Made by JuneoAI 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: Laplace-1-Vector is a 4B decision model from Juneo Lab that judges what an agent is about to do before it does it. This Space is a demo of it, and it also runs as an MCP server. The model scores options and predicts outcomes, and it never carries out an action. Who it suits: Developers who build AI agents and want a check before an action runs, and teams that mix people and agents and want to compare options first. ```table Industry | Who uses it | How they use it | Example AI agents | Agent developers | Score the actions an agent may take before one runs | Rate a sub-agent request that reaches past its authority Operations | Teams of people and agents | Compare ways to split work between people and agents | Score candidate task splits for one project Purchasing | Teams that deal with suppliers | Judge how to answer a supplier agent that asks for data | Score the replies your policy allows Research | Machine learning researchers | Study a causal-interventional decision model | Run it on a state and read the spread over candidates ``` Anyone who wants a model that performs the action itself can skip it. Where to use it: It works in these places. - A web browser, on the Space's page at Hugging Face - An MCP client, because the Space also runs as an MCP server - Windows, with Python 3.12 - macOS, with Python 3.12 - Linux, with Python 3.12 ```table Use case | Who | What they get back Score candidates | Agent developers | A probability for each candidate your application allows Predict the next observation | Agent designers | The generated outcome, shown before the action happens Call it as tools from an MCP client | Developers | Two tools, score_candidates and predict_observation ``` How to set it up: Using the Space online needs only a browser. A copy on your own computer needs the following. - Python 3.12 and pip - The huggingface_hub package, which gives you the hf command - Disk space for the repository, which holds 4 files 1. Install the huggingface_hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install Gradio at the version the Space uses, then install the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download JuneoAI/Laplace-1-Vector --repo-type space --local-dir Laplace-1-Vector cd Laplace-1-Vector pip install gradio==6.17.3 pip install -r requirements.txt python app.py ``` How to use it: Open the Space's page, or run the app on your computer and open the page Gradio shows. Pick one of two modes, scoring candidates or predicting the next observation. The source names the MCP tools but gives no connection settings. ```sh python app.py ``` ```table Tool | What it does score_candidates | Takes a state, a question and your allowed candidates, and returns a distribution over exactly those candidates predict_observation | Takes environment rules, context and an action, and generates the next observation ``` Example: You describe a state where a sub-agent asks for more access than it was given, add a question about what to do, and list the responses your application allows. It returns a spread of probabilities over exactly those responses, from one forward pass. For newcomers and experts: Before an AI agent acts, this model can tell you how likely each of your allowed options is. The first small thing to try is one of the examples set inside the demo. The model is described as causal-interventional. Scoring uses one forward pass over your candidates. The probabilities are relative to the list you supply and are not a calibrated chance of being correct. The next-observation mode generates text from the rules, context and action you give it. ```flow You give a state, a question and candidates The model runs one forward pass It returns a probability for each candidate Your application decides what to do ``` Good to know: The model scores and predicts, so your own code stays in charge of what runs. - License: apache-2 - Cost to run: none named - Data it reaches: the state, rules and context you type or send - Not for: carrying out actions, and its numbers are not a calibrated chance of being correct - Hardware: the Space is set to zero-a10g at Hugging Face, and what your own computer needs is not stated ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/JuneoAI/Laplace-1-Vector - Files: https://huggingface.co/spaces/JuneoAI/Laplace-1-Vector/tree/main ## 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [JuneoAI/Laplace-1-Vector on Hugging Face](https://huggingface.co/spaces/JuneoAI/Laplace-1-Vector) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Laplace-1-Vector (this page) | 1 | gradio | apache-2.0 | 2026-11-10 | | [SkinTokens for Agents](https://mindket.com/p/dfd41dcb-8046-410e-874b-88fca223825f) | 4 | gradio | mit | 2026-11-05 | | [UniMate for Agents](https://mindket.com/p/24ad4fa5-8642-4153-80ba-15b562db41ca) | 3 | gradio | cc-by-nc-4.0 | 2026-11-05 | | [Character Animation for Agents](https://mindket.com/p/e5dd2d25-3567-47c1-aa2a-ee2985516967) | 1 | gradio | cc-by-nc-4.0 | 2026-11-06 | | [projtex ยท Projection Texturing API](https://mindket.com/p/cac5f8ad-c02f-4e91-ad4c-9f4f72fba392) | 5 | docker | mit | 2026-10-29 | | [Obfuscated-Shell Safety Leaderboard](https://mindket.com/p/5ac6c260-b98c-4714-9842-178cf0b8d40b) | 1 | static | cc-by-4.0 | 2026-11-12 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-11-10 - First published: 2026-11-09 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open JuneoAI/Laplace-1-Vector on Hugging Face](https://huggingface.co/spaces/JuneoAI/Laplace-1-Vector) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fa25983a-e6a7-4f63-a4d1-12224e3e2331) - [Its page on Mindket](https://mindket.com/p/fa25983a-e6a7-4f63-a4d1-12224e3e2331)