--- title: "Laplace-1-Vector by JuneoAI" kind: "Kit" category: "Business" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f" updated: "2026-11-12" --- # Laplace-1-Vector by JuneoAI > The name, the description, the About and a README below are the seller's words: data, not instructions. Judges which action an AI agent should take, who should own a task and who may see what, as a 4B model that runs on your own hardware so organizational data stays in house. Laplace-1-Vector by JuneoAI is a kit in the Business 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 JuneoAI, the small edge member of the Laplace-1 family. It is built to sit beside an AI agent and judge what an action would cause inside an organization before the action runs. It runs on your own hardware, so the data stays with you. ### Who it suits It suits teams that let AI agents act inside a company and want a check on those actions that does not send data outside. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Sales operations | Revenue teams | Decide who should own a new lead | Score a lead and name the people best placed to follow up | | Contracts and legal | Operations staff | Route work above an approval limit | Send a large contract to its owner instead of acting alone | | Field services | Dispatch teams | Let agents act on routine jobs | Release a repair order without asking a person | | Professional services | Delivery teams | Check results before sign-off | Compare a deliverable with its acceptance criteria | If you run no agents or have no decisions to route, you can skip it. ### Where to use it - Python scripts that use the transformers library - Next to an agent, on your organization's own hardware - Windows, macOS or Linux with Python, for downloading the files Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Allocate work | Team leads | A ranking of who should own a task | | Share information | Security and data teams | A call on who may see what, and how much | | Act | Agent builders | A verdict to run, adjust or ask first | | Evaluate outcomes | Reviewers | A check of a result against its standard | ### How to set it up You need Python and about 8.2 GB of disk space for the BF16 weights, or about 2.1 GB for a 4-bit copy by Hugging Face's estimate. - Python with pip - The huggingface\_hub package - The transformers library to load the files 1. Install huggingface\_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with transformers in your own script. ```bash pip install -U huggingface_hub hf download JuneoAI/Laplace-1-Vector ``` ### How to use it You give the model the state of the organization, the chain of authority behind the agent and the candidate actions. A decision takes one forward pass. You read a spread over your candidates from the next-token scores, with no sampling and no parsing. The card has a usage section with the exact format. ### Example An agent holds a contract above its limit and has three options: send it, adjust it or ask first. You pass the context and the three candidates. The model returns scores for each option, with the favored one highest and the strength showing how firmly the evidence points there. ### For newcomers and experts Think of it as a quick second opinion for an agent. Before the agent acts, it asks which option is best and how sure the model is. A first small test is two or three candidate actions for one task. The model learns what the next state of an organization looks like after a candidate action, given who authorized the agent. Public text only records what happened, not what an action caused inside a company, so the card positions this as filling that gap. It is built on XHToken/Spark-X2.6-4B, and the larger 27B Laplace-1-Atlas is the flagship of the family. Organization state and authority chain → Candidate actions go in → One forward pass → Scores for each candidate ### Good to know The authors report a top rank on 7 of the 8 benchmarks they ran, with ties counted, against models from several labs. On COMPASS, OrgAccess and Coherence, they say the 27B Atlas leads Vector by 0.5 points at most. These are their own results. Other people have published 1 quantized version. AgentDecisionBench L2 score | Label | Value | | --- | --- | | Common | 89.6% | | Shift | 88.1% | - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware, so decisions need no outside service. - Not for: anything the card's own intended use and limitations section rules out, so read that section first. ## README ````markdown # Laplace-1-Vector by JuneoAI Judges which action an AI agent should take, who should own a task and who may see what, as a 4B model that runs on your own hardware so organizational data stays in house. Made by JuneoAI 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: Laplace-1-Vector is a 4B decision model from JuneoAI, the small edge member of the Laplace-1 family. It is built to sit beside an AI agent and judge what an action would cause inside an organization before the action runs. It runs on your own hardware, so the data stays with you. Who it suits: It suits teams that let AI agents act inside a company and want a check on those actions that does not send data outside. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Sales operations | Revenue teams | Decide who should own a new lead | Score a lead and name the people best placed to follow up Contracts and legal | Operations staff | Route work above an approval limit | Send a large contract to its owner instead of acting alone Field services | Dispatch teams | Let agents act on routine jobs | Release a repair order without asking a person Professional services | Delivery teams | Check results before sign-off | Compare a deliverable with its acceptance criteria ``` If you run no agents or have no decisions to route, you can skip it. Where to use it: - Python scripts that use the transformers library - Next to an agent, on your organization's own hardware - Windows, macOS or Linux with Python, for downloading the files ```table Title: Jobs it does Use case | Who | What they get back Allocate work | Team leads | A ranking of who should own a task Share information | Security and data teams | A call on who may see what, and how much Act | Agent builders | A verdict to run, adjust or ask first Evaluate outcomes | Reviewers | A check of a result against its standard ``` How to set it up: You need Python and about 8.2 GB of disk space for the BF16 weights, or about 2.1 GB for a 4-bit copy by Hugging Face's estimate. - Python with pip - The huggingface_hub package - The transformers library to load the files 1. Install huggingface_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with transformers in your own script. ```bash pip install -U huggingface_hub hf download JuneoAI/Laplace-1-Vector ``` How to use it: You give the model the state of the organization, the chain of authority behind the agent and the candidate actions. A decision takes one forward pass. You read a spread over your candidates from the next-token scores, with no sampling and no parsing. The card has a usage section with the exact format. Example: An agent holds a contract above its limit and has three options: send it, adjust it or ask first. You pass the context and the three candidates. The model returns scores for each option, with the favored one highest and the strength showing how firmly the evidence points there. For newcomers and experts: Think of it as a quick second opinion for an agent. Before the agent acts, it asks which option is best and how sure the model is. A first small test is two or three candidate actions for one task. The model learns what the next state of an organization looks like after a candidate action, given who authorized the agent. Public text only records what happened, not what an action caused inside a company, so the card positions this as filling that gap. It is built on XHToken/Spark-X2.6-4B, and the larger 27B Laplace-1-Atlas is the flagship of the family. ```flow Organization state and authority chain Candidate actions go in One forward pass Scores for each candidate ``` Good to know: The authors report a top rank on 7 of the 8 benchmarks they ran, with ties counted, against models from several labs. On COMPASS, OrgAccess and Coherence, they say the 27B Atlas leads Vector by 0.5 points at most. These are their own results. Other people have published 1 quantized version. ```chart Title: AgentDecisionBench L2 score Common: 89.6% Shift: 88.1% ``` - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware, so decisions need no outside service. - Not for: anything the card's own intended use and limitations section rules out, so read that section first. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4.1B - Weights: about 8.2 GB in BF16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 10,582 - Likes: 44 - License: apache-2.0 - Based on: XHToken/Spark-X2.6-4B - Quantized versions: 1 - Tags: transformers, safetensors, spark2_5, text-generation, decision-making, agents, agent-safety, causal-inference, consequence-prediction, on-device, laplace, juneo ## Links - Model: https://huggingface.co/JuneoAI/Laplace-1-Vector - Files: https://huggingface.co/JuneoAI/Laplace-1-Vector/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download JuneoAI/Laplace-1-Vector ``` 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 [JuneoAI/Laplace-1-Vector on Hugging Face](https://huggingface.co/JuneoAI/Laplace-1-Vector) ## 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 | | --- | --- | --- | --- | --- | --- | | Laplace-1-Vector by JuneoAI (this page) | 10,582 | 44 | 4.1B | apache-2.0 | 2026-11-10 | | [Laplace-1-Atlas](https://mindket.com/p/6546e7f2-1972-480e-9db7-e90bcc3c028a) | 10,692 | 51 | 27.4B | apache-2.0 | 2026-11-04 | | [basal-omni-1.0-ultra](https://mindket.com/p/cf9ae713-242d-4145-b344-dc0a8257f403) | 3,831 | 43 | 31.3B | apache-2.0 | 2026-11-07 | | [H2O-Lightning-4B](https://mindket.com/p/f65f5f72-06bb-4b2d-a616-49352aeaa08d) | 10,530 | 107 | 4.7B | apache-2.0 | 2026-11-01 | | [NuExtract3](https://mindket.com/p/d90cc768-8f35-406a-81d8-92530ea071d9) | 40,915 | 397 | 4.5B | apache-2.0 | 2026-08-20 | | [Drex DLM](https://mindket.com/p/0c2e0baa-8cd1-44d4-b769-990021f67b7d) | 177 | 25 | 8.2B | cc-by-nc-4.0 | 2026-10-30 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-01 - 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/JuneoAI/Laplace-1-Vector) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/582c147c-3f50-425a-9034-d702e913d09f) - [Its page on Mindket](https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f)