--- title: "basal-omni-1.0-ultra" kind: "Kit" category: "Business" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cf9ae713-242d-4145-b344-dc0a8257f403" updated: "2026-11-13" --- # basal-omni-1.0-ultra > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers typed questions about a message, document or agent trace with a calibrated probability for each allowed answer, in one pass without generating text. An experimental 31B model for Polish and English decisions. basal-omni-1.0-ultra is a kit in the Business 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 Remek ## About ### What it is basal-omni-1.0-ultra is an experimental 31B model built on Google Gemma 4, the largest in the basal-omni line. It does not write answers. It reads a state, such as a message, a document, retrieved passages or an agent trace, and answers a typed question about it. The reply is a probability for each allowed option. ### Who it suits Teams who need fast, calibrated decisions on Polish or English text, for example routing, labeling or judging content. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a decision step to a pipeline | Pick the right tool for an agent request | | Operations | Process owners | Label or route documents automatically | Classify an incoming contract | | Research | ML researchers | Study calibrated decision models | Compare it with the older basal-1.5-max | If you need a model that writes text, you can skip it, since it only picks among options you give. ### Where to use it - The basal inference engine, which serves the typed questions over its API - Agent pipelines that choose among OpenAI or MCP tool definitions - Decision tasks on Polish and English text, plus images in the same request | Use case | Who | What they get back | | --- | --- | --- | | Tool choice | Agent builders | The tool to call, not its arguments | | Yes or no checks | Developers | A probability for yes and for no | | Scoring and choosing | Analysts | A probability for each option, up to 255 of them | ### How to set it up - Python with pip, to fetch the files - The basal engine, which the card names as the way to serve the model - A large GPU, since the card's results were served on one H200 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Serve them with the basal engine, following its instructions on GitHub. ```sh pip install -U huggingface_hub hf download Remek/basal-omni-1.0-ultra ``` ### How to use it You send the engine a state and a typed question with the allowed answers. It returns a probability for each one. The question types the card names are in the table. Images go in the same request when the server runs with the media option on. | Tool | What it does | | --- | --- | | choice | Picks among the options you give | | noul | Answers yes or no | | score | Rates the state on a scale | | tool | Picks a tool from OpenAI or MCP tool definitions | ### Example You pass a customer message as the state and ask a yes or no question about whether it is a complaint. The model returns a probability for yes and for no, so you can act automatically when it is sure and send it to a person when it is not. ### For newcomers and experts If you are new, think of it as a fast classifier that also tells you how sure it is. It cannot answer outside the options you give it. For experts, it is inspired by System 1 style decision models. It handles images zero-shot through Gemma 4's own image encoder, and takes no audio. You send a state and a typed question → The model reads them in one forward pass → It returns a probability per allowed option → Your code acts or defers to a person ### Good to know The card's Werdykt v1 benchmark compares three models on hidden Polish and English tasks. Werdykt v1 macro score from the card | Label | Value | | --- | --- | | basal-omni-1.0-ultra | 0.879 score | | basal-omni-1.0-max | 0.823 score | | basal-1.5-max | 0.773 score | - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a large GPU and the basal engine. - Data it reaches: downloading reaches Hugging Face. The model only reads the state you send it. - Not for: writing text, audio input, or exact rules. The card says it is experimental, and its rules category scored 0.550, so compute exact thresholds and dates in code. ## README ````markdown # basal-omni-1.0-ultra Answers typed questions about a message, document or agent trace with a calibrated probability for each allowed answer, in one pass without generating text. An experimental 31B model for Polish and English decisions. Made by Remek 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: basal-omni-1.0-ultra is an experimental 31B model built on Google Gemma 4, the largest in the basal-omni line. It does not write answers. It reads a state, such as a message, a document, retrieved passages or an agent trace, and answers a typed question about it. The reply is a probability for each allowed option. Who it suits: Teams who need fast, calibrated decisions on Polish or English text, for example routing, labeling or judging content. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a decision step to a pipeline | Pick the right tool for an agent request Operations | Process owners | Label or route documents automatically | Classify an incoming contract Research | ML researchers | Study calibrated decision models | Compare it with the older basal-1.5-max ``` If you need a model that writes text, you can skip it, since it only picks among options you give. Where to use it: - The basal inference engine, which serves the typed questions over its API - Agent pipelines that choose among OpenAI or MCP tool definitions - Decision tasks on Polish and English text, plus images in the same request ```table Use case | Who | What they get back Tool choice | Agent builders | The tool to call, not its arguments Yes or no checks | Developers | A probability for yes and for no Scoring and choosing | Analysts | A probability for each option, up to 255 of them ``` How to set it up: - Python with pip, to fetch the files - The basal engine, which the card names as the way to serve the model - A large GPU, since the card's results were served on one H200 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Serve them with the basal engine, following its instructions on GitHub. ```sh pip install -U huggingface_hub hf download Remek/basal-omni-1.0-ultra ``` How to use it: You send the engine a state and a typed question with the allowed answers. It returns a probability for each one. The question types the card names are in the table. Images go in the same request when the server runs with the media option on. ```table Tool | What it does choice | Picks among the options you give noul | Answers yes or no score | Rates the state on a scale tool | Picks a tool from OpenAI or MCP tool definitions ``` Example: You pass a customer message as the state and ask a yes or no question about whether it is a complaint. The model returns a probability for yes and for no, so you can act automatically when it is sure and send it to a person when it is not. For newcomers and experts: If you are new, think of it as a fast classifier that also tells you how sure it is. It cannot answer outside the options you give it. For experts, it is inspired by System 1 style decision models. It handles images zero-shot through Gemma 4's own image encoder, and takes no audio. ```flow You send a state and a typed question The model reads them in one forward pass It returns a probability per allowed option Your code acts or defers to a person ``` Good to know: The card's Werdykt v1 benchmark compares three models on hidden Polish and English tasks. ```chart Title: Werdykt v1 macro score from the card basal-omni-1.0-ultra: 0.879 score basal-omni-1.0-max: 0.823 score basal-1.5-max: 0.773 score ``` - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a large GPU and the basal engine. - Data it reaches: downloading reaches Hugging Face. The model only reads the state you send it. - Not for: writing text, audio input, or exact rules. The card says it is experimental, and its rules category scored 0.550, so compute exact thresholds and dates in code. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 31.3B - Weights: about 62.5 GB in BF16; about 15.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,831 - Likes: 43 - License: apache-2.0 - Languages: pl, en - Based on: google/gemma-4-31B-it - Quantized versions: 3 - Tags: transformers, safetensors, gemma4, image-text-to-text, typed-decisions, decision-making, classification, calibration, polish, experimental, decision-model, text-generation ## Links - Model: https://huggingface.co/Remek/basal-omni-1.0-ultra - Files: https://huggingface.co/Remek/basal-omni-1.0-ultra/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Remek/basal-omni-1.0-ultra ``` 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 [Remek/basal-omni-1.0-ultra on Hugging Face](https://huggingface.co/Remek/basal-omni-1.0-ultra) ## 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 | | --- | --- | --- | --- | --- | --- | | basal-omni-1.0-ultra (this page) | 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 | | [Laplace-1-Atlas](https://mindket.com/p/6546e7f2-1972-480e-9db7-e90bcc3c028a) | 10,692 | 51 | 27.4B | apache-2.0 | 2026-11-04 | | [Drex DLM](https://mindket.com/p/0c2e0baa-8cd1-44d4-b769-990021f67b7d) | 177 | 25 | 8.2B | cc-by-nc-4.0 | 2026-10-30 | | [Laplace-1-Vector by JuneoAI](https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f) | 10,582 | 44 | 4.1B | apache-2.0 | 2026-11-10 | | [Amos 1 large](https://mindket.com/p/60f345c2-0270-4488-bd7b-da2061172f0f) | 694 | 28 | — | apache-2.0 | 2026-11-11 | 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 · lukasm · 2026-10-18](https://news.ycombinator.com/item?id=50140075): Basal-Omni: An open-weight 31B model for AI decision-making (nr 1 on JevBench) ## 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: 2026-11-07 - First published: 2026-10-15 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Remek/basal-omni-1.0-ultra on Hugging Face](https://huggingface.co/Remek/basal-omni-1.0-ultra) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cf9ae713-242d-4145-b344-dc0a8257f403) - [Its page on Mindket](https://mindket.com/p/cf9ae713-242d-4145-b344-dc0a8257f403)