--- title: "Oc-e-Non-v1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5e2d437f-d06b-4a84-9d1e-b2e2eef43d2a" updated: "2026-11-12" --- # Oc-e-Non-v1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers in about half the words of the Qwen3-4B-Instruct-2507 model it comes from, so replies are short and direct. For people who want brief text generation output. Oc-e-Non-v1 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 p-e-w ## About ### What it is Oc-e-Non-v1 is a text generation model that keeps its answers short. It starts from Qwen3-4B-Instruct-2507 and was adjusted with the Heretic tool so replies get to the point. The card says it cuts the average reply length by about half. ### Who it suits It suits people who want brief answers from a small 4B model, such as developers building a chat helper or readers tired of long replies. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a terse assistant to a tool | A coding helper that returns only the code | | Support | Help desk teams | Draft short replies to quick questions | A one word answer to a simple query | | Education | Students and teachers | Get quick factual answers | A fast yes or no on a fact check | People who want long explanations or a friendly, chatty tone can skip it, since the model is built to say less. ### Where to use it - Python with the transformers library, which the card lists - Windows, macOS or Linux, where you download the files with Python - Any machine that can hold a 4B model, about 8 GB in BF16 or about 2 GB at 4-bit by estimate | Use case | Who | What they get back | | --- | --- | --- | | Quick answers | Anyone | A reply such as 2 or Yes instead of a full sentence | | Code snippets | Developers | A bare function with no explanation around it | | Trimmed chat output | App builders | Replies about half as long as the original model gives | ### How to set it up You need these first: - Python - The huggingface\_hub package - The transformers library, since the card lists it - Enough memory for a 4B model 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model with transformers. ```sh pip install -U huggingface_hub hf download p-e-w/Oc-e-Non-v1 ``` ### How to use it Load the model in transformers and send it a normal chat message. It answers in as few words as it can, so ask a plain question and expect a short reply back. ### Example You ask "Are dogs mammals?" The card shows the model replying with just the word Yes. A chattier model adds a sentence about hair and milk. Ask it for a Python check on capital letters and the card shows a bare function with no notes. ### For newcomers and experts A language model predicts text, and most of them like to explain themselves. This one stops sooner. A first thing to try is asking it "What is 1+1?" and seeing a one character answer. For experts, it keeps the Qwen3-4B-Instruct-2507 base and changes it with Heretic, using settings the card lists such as a start layer of 14 and an end layer of 31. The card reports a mean response length of 212.3 against 413.9 for the original, and a PIQA acc\_norm of 0.7535 against 0.7595. You trade a small drop on that test for much shorter output. Start from Qwen3-4B-Instruct-2507 → Adjust it with Heretic settings → Load the result in transformers → Ask a question → Get a short reply ### Good to know Mean response length | Label | Value | | --- | --- | | Oc-e-Non-v1 | 212.3 | | Qwen3-4B-Instruct-2507 | 413.9 | - Size: 4B parameters, with a Safetensors format - Variants: Hugging Face lists 5 quantized versions made by other people - Base model: Qwen3-4B-Instruct-2507, which works in non-thinking mode only - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face and works on the text you give it - Not for: long explanations, since the model is built to keep replies short ## README ````markdown # Oc-e-Non-v1 Answers in about half the words of the Qwen3-4B-Instruct-2507 model it comes from, so replies are short and direct. For people who want brief text generation output. Made by p-e-w 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: Oc-e-Non-v1 is a text generation model that keeps its answers short. It starts from Qwen3-4B-Instruct-2507 and was adjusted with the Heretic tool so replies get to the point. The card says it cuts the average reply length by about half. Who it suits: It suits people who want brief answers from a small 4B model, such as developers building a chat helper or readers tired of long replies. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a terse assistant to a tool | A coding helper that returns only the code Support | Help desk teams | Draft short replies to quick questions | A one word answer to a simple query Education | Students and teachers | Get quick factual answers | A fast yes or no on a fact check ``` People who want long explanations or a friendly, chatty tone can skip it, since the model is built to say less. Where to use it: - Python with the transformers library, which the card lists - Windows, macOS or Linux, where you download the files with Python - Any machine that can hold a 4B model, about 8 GB in BF16 or about 2 GB at 4-bit by estimate ```table Use case | Who | What they get back Quick answers | Anyone | A reply such as 2 or Yes instead of a full sentence Code snippets | Developers | A bare function with no explanation around it Trimmed chat output | App builders | Replies about half as long as the original model gives ``` How to set it up: You need these first: - Python - The huggingface_hub package - The transformers library, since the card lists it - Enough memory for a 4B model 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model with transformers. ```sh pip install -U huggingface_hub hf download p-e-w/Oc-e-Non-v1 ``` How to use it: Load the model in transformers and send it a normal chat message. It answers in as few words as it can, so ask a plain question and expect a short reply back. Example: You ask "Are dogs mammals?" The card shows the model replying with just the word Yes. A chattier model adds a sentence about hair and milk. Ask it for a Python check on capital letters and the card shows a bare function with no notes. For newcomers and experts: A language model predicts text, and most of them like to explain themselves. This one stops sooner. A first thing to try is asking it "What is 1+1?" and seeing a one character answer. For experts, it keeps the Qwen3-4B-Instruct-2507 base and changes it with Heretic, using settings the card lists such as a start layer of 14 and an end layer of 31. The card reports a mean response length of 212.3 against 413.9 for the original, and a PIQA acc_norm of 0.7535 against 0.7595. You trade a small drop on that test for much shorter output. ```flow Start from Qwen3-4B-Instruct-2507 Adjust it with Heretic settings Load the result in transformers Ask a question Get a short reply ``` Good to know: ```chart Title: Mean response length Oc-e-Non-v1: 212.3 Qwen3-4B-Instruct-2507: 413.9 ``` - Size: 4B parameters, with a Safetensors format - Variants: Hugging Face lists 5 quantized versions made by other people - Base model: Qwen3-4B-Instruct-2507, which works in non-thinking mode only - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face and works on the text you give it - Not for: long explanations, since the model is built to keep replies short ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4B - Weights: about 8 GB in BF16; about 2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,215 - Likes: 24 - License: apache-2.0 - Based on: Qwen/Qwen3-4B-Instruct-2507 - Quantized versions: 5 - Tags: transformers, safetensors, qwen3, text-generation, heretic, terse, conversational ## Links - Model: https://huggingface.co/p-e-w/Oc-e-Non-v1 - Files: https://huggingface.co/p-e-w/Oc-e-Non-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download p-e-w/Oc-e-Non-v1 ``` 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 [p-e-w/Oc-e-Non-v1 on Hugging Face](https://huggingface.co/p-e-w/Oc-e-Non-v1) ## 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 | | --- | --- | --- | --- | --- | --- | | Oc-e-Non-v1 (this page) | 1,215 | 24 | 4B | apache-2.0 | 2026-11-06 | | [Signal 3.8 27B](https://mindket.com/p/554e755a-170e-4370-a46b-9fa53f08a7b4) | 20,222 | 19 | 27.8B | apache-2.0 | 2026-09-15 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [Qwen3-1.7B](https://mindket.com/p/c3175d9e-0ec9-4f84-b948-86f012d7e396) | 3,203,490 | 618 | 2B | apache-2.0 | 2025-07-26 | 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: 2026-11-06 - First published: 2026-11-06 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open p-e-w/Oc-e-Non-v1 on Hugging Face](https://huggingface.co/p-e-w/Oc-e-Non-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5e2d437f-d06b-4a84-9d1e-b2e2eef43d2a) - [Its page on Mindket](https://mindket.com/p/5e2d437f-d06b-4a84-9d1e-b2e2eef43d2a)