--- title: "Kev-4B by ggml-org" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e2e01d00-d647-46fb-89a3-48661fa412ac" updated: "2026-11-13" --- # Kev-4B by ggml-org > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes decisions through the /v1/systemone API of a llama server, from a GGUF copy of a 4.2B model. For developers who want a small local decision model. Kev-4B by ggml-org is a kit in the Developer 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 ggml-org ## About ### What it is Kev-4B is a GGUF copy of a 4.2B text classification model, made by ggml-org. The card calls it a decision model, used through the /v1/systemone API. It was converted automatically from jaredpalmer/kev-4b, which builds on Qwen3.5-4B-Base. ### Who it suits It suits developers who want a small local model that makes a decision, rather than writing free text. The card gives few other details. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Call a local decision model through the systemone API | A routing step in an app | | Research | Researchers testing llama tooling | Try a decision model served locally | Check how the endpoint behaves | | IT | Teams keeping data in-house | Run a classifier on their own hardware | An internal triage helper | If you need a chat model that writes long answers, you can skip it. ### Where to use it - The Llama app - The llama command in a terminal on Windows, macOS or Linux - Python on the same machines, for downloading the files by hand Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Decisions through the API | Developers | A result from the /v1/systemone endpoint | | Text classification | Researchers | A label for a piece of text | ### How to set it up - The Llama app or the llama command - Python, only if you download the files by hand 1. Install the Llama app or the llama command. 2. Run the serve command below. It fetches the model by its Hugging Face name. 3. Optionally download the files by hand with the Hugging Face tools. ```bash llama serve -hf ggml-org/Kev-4B-GGUF ``` ```sh pip install -U huggingface_hub hf download ggml-org/Kev-4B-GGUF ``` ### How to use it Start the server with the serve command, then call it through the /v1/systemone API, which the card names as the way to use this model. The card gives no request format, so check the llama tools. ```bash llama serve -hf ggml-org/Kev-4B-GGUF ``` ### Example You start the server with the serve command and send a request to the /v1/systemone endpoint. The card shows no sample request or reply, so the shape of the answer comes from the llama tools you use. ### For newcomers and experts If you are new, a decision model picks an answer from a set of options instead of writing a paragraph. The first small thing to try is the serve command above. If you know the field, this is a 4.2B model that is about 2.1 GB at 4-bit by Hugging Face's estimate, with Qwen3.5-4B-Base as an upstream source. The card gives no benchmark, label set or request schema, so treat it as a converted checkpoint and test it yourself. Run llama serve with the model name → Files come from Hugging Face → The server starts on your machine → Call the /v1/systemone endpoint ### Good to know - Conversion: an automatic tool from ggml-org made this copy from the source model. - Sources: jaredpalmer/kev-4b and Qwen3.5-4B-Base. - Detail: the card has no scores or usage notes beyond the endpoint name. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download the model from Hugging Face, and the card names no other access. ## README ````markdown # Kev-4B by ggml-org Makes decisions through the /v1/systemone API of a llama server, from a GGUF copy of a 4.2B model. For developers who want a small local decision model. Made by ggml-org 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: Kev-4B is a GGUF copy of a 4.2B text classification model, made by ggml-org. The card calls it a decision model, used through the /v1/systemone API. It was converted automatically from jaredpalmer/kev-4b, which builds on Qwen3.5-4B-Base. Who it suits: It suits developers who want a small local model that makes a decision, rather than writing free text. The card gives few other details. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Developers | Call a local decision model through the systemone API | A routing step in an app Research | Researchers testing llama tooling | Try a decision model served locally | Check how the endpoint behaves IT | Teams keeping data in-house | Run a classifier on their own hardware | An internal triage helper ``` If you need a chat model that writes long answers, you can skip it. Where to use it: - The Llama app - The llama command in a terminal on Windows, macOS or Linux - Python on the same machines, for downloading the files by hand ```table Title: Jobs it does Use case | Who | What they get back Decisions through the API | Developers | A result from the /v1/systemone endpoint Text classification | Researchers | A label for a piece of text ``` How to set it up: - The Llama app or the llama command - Python, only if you download the files by hand 1. Install the Llama app or the llama command. 2. Run the serve command below. It fetches the model by its Hugging Face name. 3. Optionally download the files by hand with the Hugging Face tools. ```bash llama serve -hf ggml-org/Kev-4B-GGUF ``` ```sh pip install -U huggingface_hub hf download ggml-org/Kev-4B-GGUF ``` How to use it: Start the server with the serve command, then call it through the /v1/systemone API, which the card names as the way to use this model. The card gives no request format, so check the llama tools. ```bash llama serve -hf ggml-org/Kev-4B-GGUF ``` Example: You start the server with the serve command and send a request to the /v1/systemone endpoint. The card shows no sample request or reply, so the shape of the answer comes from the llama tools you use. For newcomers and experts: If you are new, a decision model picks an answer from a set of options instead of writing a paragraph. The first small thing to try is the serve command above. If you know the field, this is a 4.2B model that is about 2.1 GB at 4-bit by Hugging Face's estimate, with Qwen3.5-4B-Base as an upstream source. The card gives no benchmark, label set or request schema, so treat it as a converted checkpoint and test it yourself. ```flow Run llama serve with the model name Files come from Hugging Face The server starts on your machine Call the /v1/systemone endpoint ``` Good to know: - Conversion: an automatic tool from ggml-org made this copy from the source model. - Sources: jaredpalmer/kev-4b and Qwen3.5-4B-Base. - Detail: the card has no scores or usage notes beyond the endpoint name. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download the model from Hugging Face, and the card names no other access. ## On Hugging Face, 13 November 2026 - Task: text classification - Parameters: 4.2B - Weights: about 2.1 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 39,531 - Likes: 34 - License: apache-2.0 - Based on: jaredpalmer/kev-4b - Tags: gguf, quantized, decision-model, text-classification, conversational ## Links - Model: https://huggingface.co/ggml-org/Kev-4B-GGUF - Files: https://huggingface.co/ggml-org/Kev-4B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ggml-org/Kev-4B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ggml-org/Kev-4B-GGUF on Hugging Face](https://huggingface.co/ggml-org/Kev-4B-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | Kev-4B by ggml-org (this page) | 39,531 | 34 | 4.2B | apache-2.0 | 2026-10-19 | | [Laya-GGUF](https://mindket.com/p/ce86ea17-f8a2-43ac-b4ed-5814f58345c3) | 16,039 | 41 | 421M | apache-2.0 | 2026-11-12 | | [Mica v0.1 4B](https://mindket.com/p/55daa34c-73de-47b6-a176-900851a69bf1) | 3,436 | 52 | 4.2B | apache-2.0 | 2026-09-28 | | [jevos-v4](https://mindket.com/p/cb6031a9-f6ec-4eb2-b36a-0543673bb7d9) | 1,475 | 22 | 882M | mit | 2026-10-09 | | [Clef-GGUF](https://mindket.com/p/784cbfd1-ad94-4593-9a6f-42a6a8650f64) | 20,883 | 30 | 27B | apache-2.0 | 2026-11-12 | | [Clef-LoRA-for-Qwen3.8-27B-GGUF](https://mindket.com/p/62efdbd6-4717-4196-92c5-113501a64eed) | 311 | 6 | 668M | apache-2.0 | 2026-10-05 | 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: 2026-10-19 - First published: 2026-10-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ggml-org/Kev-4B-GGUF on Hugging Face](https://huggingface.co/ggml-org/Kev-4B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e2e01d00-d647-46fb-89a3-48661fa412ac) - [Its page on Mindket](https://mindket.com/p/e2e01d00-d647-46fb-89a3-48661fa412ac)