OneJev1.6-4B-GGUF

OneJev1.6-4B-GGUF

Quantized GGUF copies of OneJev1.6-4B, from about 2 GB to 8.5 GB, for running the model on your own computer. The card does not say what the base model is for.

Made by mradermacher 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: This repository holds GGUF quantized copies of OneJev1.6-4B, made by mradermacher. The base model is OmniJev/OneJev1.6-4B, with about 4.2B parameters. The files range from about 2.0 GB to 8.5 GB. The card does not describe what the base model was trained for, so check its page first.

Who it suits: It suits people who run small language models on their own hardware and want to pick a file size that fits their memory.

Industry | Who uses it | How they use it | Example
Software | Developers | Try the model locally with a mid-size quant | Load Q4_K_M and test a few prompts
Research | ML hobbyists | Compare quant levels for size and answer quality | Run Q2_K and Q8_0 on the same question
Edge and offline work | Teams with limited memory | Choose a smaller file when the full one is too big | Use Q4_K_S at 2.7 GB where the 8.5 GB f16 file will not fit

Anyone who needs a model with a described purpose or a hosted service can skip it.

Where to use it: You download the files, then run one in any local app that reads GGUF.

• Windows, macOS or Linux, with Python, for fetching the files
• A local app that opens GGUF files, which the card does not name
• Hugging Face, where the files are hosted

Use case | Who | What they get back
Run a small model offline | Developers | A chat model answering from their own machine
Compare quant sizes | ML hobbyists | A feel for how file size trades against answer quality
Fit a model into limited memory | Edge teams | A file small enough for the memory they have

How to set it up: You need a way to fetch the files and an app that can open one.

• Python, for the Hugging Face hub tools
• Free disk space for the quant you pick, using the sizes listed under How to use it
• A local app that reads GGUF files

1. Install the Hugging Face hub tools.
2. Download the repository files.
3. Pick one quant file by size and quality.
4. Open it in your GGUF app. The card points to TheBloke's READMEs for GGUF basics and for joining multi-part files.

pip install -U huggingface_hub
hf download mradermacher/OneJev1.6-4B-GGUF

How to use it: The card gives no run command. You open the quant file you chose in your GGUF app and chat with it. Lower quants are smaller, and the table shows what the card says about each.

Quant type | Size in GB | Note
mmproj-Q8_0 | 0.5 | Multi-modal supplement file
mmproj-f16 | 0.8 | Multi-modal supplement file
Q2_K | 2.0 | Smallest of the main files
Q3_K_M | 2.4 | Marked lower quality
Q4_K_S | 2.7 | Fast, and a card pick
Q4_K_M | 2.8 | Fast, and a card pick
Q6_K | 3.6 | Marked good quality
Q8_0 | 4.6 | Fast, marked best quality
f16 | 8.5 | Full 16 bits per weight, called overkill

Example: You have a laptop with limited memory, so you choose Q4_K_M at 2.8 GB, one of the two files the card calls fast and recommended. You open it in your GGUF app and ask a question. The answer is generated on your own machine.

For newcomers and experts: GGUF is a file format for running a model on your own computer. Quantizing shrinks the model, so a lower number uses less space at some cost to quality. A good first try is Q4_K_S or Q4_K_M, the two the card recommends.

These are static quants only. The author says weighted or imatrix versions may never appear, and invites requests. The card adds that IQ types are often a better pick than non-IQ types of similar size, and that the list is sorted by size, not quality.

Base model OneJev1.6-4B
Quantized into GGUF files
You download one file
A GGUF app loads it
You chat on your own machine

Good to know: The record lists the weights at about 2.1 GB at 4-bit, as an estimate, and the language as English.

• License: Apache-2.0
• Cost to run: none named
• Data it reaches: it downloads files from Hugging Face. The card names no account, web or clipboard access.

On Hugging Face, 13 November 2026

• Library: transformers
• Parameters: 4.2B
• Weights: about 2.1 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 4,292
• Likes: 2
• License: apache-2.0
• Languages: en
• Based on: OmniJev/OneJev1.6-4B
• Tags: transformers, gguf, onejev, system-one, decision-model, calibration, multimodal, gui-agent, video, conversational

Links

• Model: https://huggingface.co/mradermacher/OneJev1.6-4B-GGUF
• Files: https://huggingface.co/mradermacher/OneJev1.6-4B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download mradermacher/OneJev1.6-4B-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.