--- title: "G4-MeroMero-v2-31B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fa7aa1a5-6446-449f-a185-f94e72247aaa" updated: "2026-11-13" --- # G4-MeroMero-v2-31B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 31.3B model by zerofata built on Gemma 4 31B instruct, for people who want to try this tune or compare it with its base. The card text says little about its purpose. G4-MeroMero-v2-31B 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 zerofata ## About ### What it is G4-MeroMero-v2-31B is a 31.3B parameter model by zerofata, built on google/gemma-4-31B-it. The files are in Safetensors format. The card text this page was built from is mostly page styling and does not say what the tune is for, so read the full card before relying on it. ### Who it suits It suits people with the hardware for a 31B model who want to try zerofata's version of the Gemma 4 31B instruct model or compare it with the original. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Model testers | Compare this tune with its base model on the same prompts | A tester runs one prompt set on both and reads the differences | | Software | Developers | Try it as a candidate for a local or self-hosted assistant | A developer loads a 4-bit copy and checks the replies | | Hobby | Local model enthusiasts | Download and test a community tune of a large model | An enthusiast tries a quantized copy published by another person | If you have limited memory or need documented behavior and tested results, you can skip it for now. ### Where to use it - Your own Windows, macOS or Linux computer, after downloading the files with Python - Runners and servers that load Safetensors weights - Quantized copies, which other people have published for this model | Use case | Who | What they get back | | --- | --- | --- | | Try the tune | Hobbyist | Text replies to the prompts you give | | Compare with the base | Researcher | Side by side outputs from two models | | Prototype an assistant | Developer | A first sense of how the model replies | ### How to set it up - Python on your computer, to run pip and the Hugging Face tools - Disk space, about 62.5 GB for the BF16 weights, or about 15.6 GB at 4-bit by Hugging Face's estimate - A runner that loads Safetensors weights and enough memory for a 31B model 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in your runner, following the steps on the base model's page for Gemma 4 31B instruct. The card text gives no run steps of its own. ```sh pip install -U huggingface_hub hf download zerofata/G4-MeroMero-v2-31B ``` ### How to use it Since it builds on an instruction-tuned model, you would give it a written prompt and read the text it writes back, but the card does not confirm this. The only command shown is the download step. ```sh hf download zerofata/G4-MeroMero-v2-31B ``` | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | pip install -U huggingface\_hub | Installs the package that provides the hf command | ### Example You load the model and send a short written request, such as asking for a three line summary of a paragraph you paste in. You then read the reply and compare it with what the base model writes. ### For newcomers and experts A tune takes an existing model and changes how it behaves, usually by training it further on new examples. A small first step is to read the full card on Hugging Face, since the text used for this page does not explain what this tune aims at. For experts, this is a 31.3B parameter model built on google/gemma-4-31B-it, stored in Safetensors. No training method, dataset or benchmark appears in the text used here, so treat its behavior as untested until you run your own checks. Google publishes gemma-4-31B-it → zerofata builds G4-MeroMero-v2-31B on it → You download the files with hf → A runner loads the weights → You send prompts and read the text back ### Good to know - Gaps: the card text used here gives no purpose, training details, usage steps or limits - Size: 31.3B parameters, about 62.5 GB in BF16 and about 15.6 GB at 4-bit by Hugging Face's estimate - License: Apache 2.0 - Cost to run: none named, apart from hardware with plenty of memory - Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access - Not for: relying on it without reading the full card, since no limits or intended uses are stated here ## README ````markdown # G4-MeroMero-v2-31B A 31.3B model by zerofata built on Gemma 4 31B instruct, for people who want to try this tune or compare it with its base. The card text says little about its purpose. Made by zerofata 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: G4-MeroMero-v2-31B is a 31.3B parameter model by zerofata, built on google/gemma-4-31B-it. The files are in Safetensors format. The card text this page was built from is mostly page styling and does not say what the tune is for, so read the full card before relying on it. Who it suits: It suits people with the hardware for a 31B model who want to try zerofata's version of the Gemma 4 31B instruct model or compare it with the original. ```table Industry | Who uses it | How they use it | Example Research | Model testers | Compare this tune with its base model on the same prompts | A tester runs one prompt set on both and reads the differences Software | Developers | Try it as a candidate for a local or self-hosted assistant | A developer loads a 4-bit copy and checks the replies Hobby | Local model enthusiasts | Download and test a community tune of a large model | An enthusiast tries a quantized copy published by another person ``` If you have limited memory or need documented behavior and tested results, you can skip it for now. Where to use it: - Your own Windows, macOS or Linux computer, after downloading the files with Python - Runners and servers that load Safetensors weights - Quantized copies, which other people have published for this model ```table Use case | Who | What they get back Try the tune | Hobbyist | Text replies to the prompts you give Compare with the base | Researcher | Side by side outputs from two models Prototype an assistant | Developer | A first sense of how the model replies ``` How to set it up: - Python on your computer, to run pip and the Hugging Face tools - Disk space, about 62.5 GB for the BF16 weights, or about 15.6 GB at 4-bit by Hugging Face's estimate - A runner that loads Safetensors weights and enough memory for a 31B model 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in your runner, following the steps on the base model's page for Gemma 4 31B instruct. The card text gives no run steps of its own. ```sh pip install -U huggingface_hub hf download zerofata/G4-MeroMero-v2-31B ``` How to use it: Since it builds on an instruction-tuned model, you would give it a written prompt and read the text it writes back, but the card does not confirm this. The only command shown is the download step. ```sh hf download zerofata/G4-MeroMero-v2-31B ``` ```table Tool | What it does hf download | Fetches the model files to your computer pip install -U huggingface_hub | Installs the package that provides the hf command ``` Example: You load the model and send a short written request, such as asking for a three line summary of a paragraph you paste in. You then read the reply and compare it with what the base model writes. For newcomers and experts: A tune takes an existing model and changes how it behaves, usually by training it further on new examples. A small first step is to read the full card on Hugging Face, since the text used for this page does not explain what this tune aims at. For experts, this is a 31.3B parameter model built on google/gemma-4-31B-it, stored in Safetensors. No training method, dataset or benchmark appears in the text used here, so treat its behavior as untested until you run your own checks. ```flow Google publishes gemma-4-31B-it zerofata builds G4-MeroMero-v2-31B on it You download the files with hf A runner loads the weights You send prompts and read the text back ``` Good to know: - Gaps: the card text used here gives no purpose, training details, usage steps or limits - Size: 31.3B parameters, about 62.5 GB in BF16 and about 15.6 GB at 4-bit by Hugging Face's estimate - License: Apache 2.0 - Cost to run: none named, apart from hardware with plenty of memory - Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access - Not for: relying on it without reading the full card, since no limits or intended uses are stated here ## On Hugging Face, 13 November 2026 - 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: 390 - Likes: 97 - License: apache-2.0 - Based on: google/gemma-4-31B-it - Quantized versions: 14 - Fine-tunes: 1 - Tags: safetensors, gemma4 ## Links - Model: https://huggingface.co/zerofata/G4-MeroMero-v2-31B - Files: https://huggingface.co/zerofata/G4-MeroMero-v2-31B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download zerofata/G4-MeroMero-v2-31B ``` 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 [zerofata/G4-MeroMero-v2-31B on Hugging Face](https://huggingface.co/zerofata/G4-MeroMero-v2-31B) ## 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 | | --- | --- | --- | --- | --- | --- | | G4-MeroMero-v2-31B (this page) | 390 | 97 | 31.3B | apache-2.0 | 2026-08-03 | | [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 | | [gemma-4-26B-A4B-it](https://mindket.com/p/bb1256de-5c41-4dac-abe1-958b57da0176) | 5,304,019 | 1,758 | 25.8B | apache-2.0 | 2026-07-20 | | [gemma-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B-it](https://mindket.com/p/52ee89d2-d2e7-4750-b100-90e089c3ec76) | 3,017,059 | 1,129 | 5.1B | apache-2.0 | 2026-11-03 | | [gemma-4-E4B](https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785) | 449,316 | 508 | 8B | apache-2.0 | 2026-11-03 | 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: 2026-08-03 - First published: 2026-07-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open zerofata/G4-MeroMero-v2-31B on Hugging Face](https://huggingface.co/zerofata/G4-MeroMero-v2-31B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fa7aa1a5-6446-449f-a185-f94e72247aaa) - [Its page on Mindket](https://mindket.com/p/fa7aa1a5-6446-449f-a185-f94e72247aaa)