Oc-e-Non-v1

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.

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.

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:

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:

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.