--- title: "gemma-4-E4B-it-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fa9df3ff-5b05-4e15-984f-04ccfc125e3e" updated: "2026-11-12" --- # gemma-4-E4B-it-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4-bit GGUF copy of Google's Gemma 4 E4B instruction model, packaged by Unsloth for running on your own machine. It reads text and images and answers in text. gemma-4-E4B-it-GGUF 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 unsloth ## About ### What it is This is a GGUF copy of Gemma 4 E4B, the instruction-tuned model from Google DeepMind, packaged by Unsloth. It takes text and images and replies in text. The 4-bit weights are estimated at about 3.8 GB, so it can run on an ordinary computer. ### Who it suits Developers and hobbyists who want a small multimodal model on their own hardware, from laptops to phones. The card lists text generation, coding and reasoning as its strengths. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Run a small local model with image input and function calling | Build a local assistant that answers questions about a screenshot | | Research | ML practitioners | Test a compact instruction model in GGUF form | Compare its reasoning against a larger model | | Local AI | Hobbyists | Chat with a model on a laptop without a cloud account | Load it in Unsloth Studio and ask coding questions | | Multilingual work | Teams in many languages | Use one model that supports over 140 languages | Ask the same question in several languages | You can skip it if you need a hosted service or a model that runs only on large servers. ### Where to use it - Unsloth Studio, where the card says Gemma 4 can run and be fine-tuned - llama.cpp and other tools that read GGUF files - Windows, macOS and Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Hobbyists | Text answers from a model on your own machine | | Image questions | Developers | Text answers about an image you send | | Tool calling | Agent builders | Replies that can call functions, which the card says Gemma 4 supports natively | ### How to set it up You need Python for the download and a program that can load GGUF files. - Python on Windows, macOS or Linux - Room for the 4-bit weights, estimated at about 3.8 GB - A GGUF-capable tool such as Unsloth Studio or llama.cpp 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the model files. 4. Load them in your GGUF tool. ```bash pip install -U huggingface_hub hf download unsloth/gemma-4-E4B-it-GGUF ``` ### How to use it The card shows no run command and points to a separate guide from Unsloth for running Gemma 4. In a chat tool, you write a message, attach an image if you like, and read the reply. The card notes a system role for setting the model's behavior. ### Example You attach a photo of a handwritten shopping list and ask the model to turn it into a typed list. It reads the image and answers with the items as text. ### For newcomers and experts A GGUF file is a model packed so ordinary computers can run it. A first small step is to download the files, open them in Unsloth Studio and ask a short question. Gemma 4 comes in dense and mixture-of-experts variants. E2B and E4B are built for phones and laptops and handle 128K tokens of context. The larger sizes handle 256K. The card lists configurable thinking modes, native function calling and native system prompt support. Unsloth also points to its Dynamic 2.0 quantization benchmarks. Download the GGUF files → Load them in a GGUF-capable tool → Send text and optional images → Read the text reply Context window in thousands of tokens | Label | Value | | --- | --- | | Small models such as E4B | 128 K tokens | | Medium models | 256 K tokens | ### Good to know - The model has 7.5B parameters, and the 4-bit weights are about 3.8 GB by estimate. - Audio input is listed for the E2B and E4B models, and video is among the supported inputs. - The card says to re-download if you have an older copy, to get a newer chat template and llama.cpp fixes. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and whatever text or images you give it. - Not for: the card names no limits. ## README ````markdown # gemma-4-E4B-it-GGUF A 4-bit GGUF copy of Google's Gemma 4 E4B instruction model, packaged by Unsloth for running on your own machine. It reads text and images and answers in text. Made by unsloth 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 is a GGUF copy of Gemma 4 E4B, the instruction-tuned model from Google DeepMind, packaged by Unsloth. It takes text and images and replies in text. The 4-bit weights are estimated at about 3.8 GB, so it can run on an ordinary computer. Who it suits: Developers and hobbyists who want a small multimodal model on their own hardware, from laptops to phones. The card lists text generation, coding and reasoning as its strengths. ```table Industry | Who uses it | How they use it | Example Software | App developers | Run a small local model with image input and function calling | Build a local assistant that answers questions about a screenshot Research | ML practitioners | Test a compact instruction model in GGUF form | Compare its reasoning against a larger model Local AI | Hobbyists | Chat with a model on a laptop without a cloud account | Load it in Unsloth Studio and ask coding questions Multilingual work | Teams in many languages | Use one model that supports over 140 languages | Ask the same question in several languages ``` You can skip it if you need a hosted service or a model that runs only on large servers. Where to use it: - Unsloth Studio, where the card says Gemma 4 can run and be fine-tuned - llama.cpp and other tools that read GGUF files - Windows, macOS and Linux with Python, for downloading the files ```table Use case | Who | What they get back Local chat | Hobbyists | Text answers from a model on your own machine Image questions | Developers | Text answers about an image you send Tool calling | Agent builders | Replies that can call functions, which the card says Gemma 4 supports natively ``` How to set it up: You need Python for the download and a program that can load GGUF files. - Python on Windows, macOS or Linux - Room for the 4-bit weights, estimated at about 3.8 GB - A GGUF-capable tool such as Unsloth Studio or llama.cpp 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the model files. 4. Load them in your GGUF tool. ```bash pip install -U huggingface_hub hf download unsloth/gemma-4-E4B-it-GGUF ``` How to use it: The card shows no run command and points to a separate guide from Unsloth for running Gemma 4. In a chat tool, you write a message, attach an image if you like, and read the reply. The card notes a system role for setting the model's behavior. Example: You attach a photo of a handwritten shopping list and ask the model to turn it into a typed list. It reads the image and answers with the items as text. For newcomers and experts: A GGUF file is a model packed so ordinary computers can run it. A first small step is to download the files, open them in Unsloth Studio and ask a short question. Gemma 4 comes in dense and mixture-of-experts variants. E2B and E4B are built for phones and laptops and handle 128K tokens of context. The larger sizes handle 256K. The card lists configurable thinking modes, native function calling and native system prompt support. Unsloth also points to its Dynamic 2.0 quantization benchmarks. ```flow Download the GGUF files Load them in a GGUF-capable tool Send text and optional images Read the text reply ``` ```chart Title: Context window in thousands of tokens Small models such as E4B: 128 K tokens Medium models: 256 K tokens ``` Good to know: - The model has 7.5B parameters, and the 4-bit weights are about 3.8 GB by estimate. - Audio input is listed for the E2B and E4B models, and video is among the supported inputs. - The card says to re-download if you have an older copy, to get a newer chat template and llama.cpp fixes. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and whatever text or images you give it. - Not for: the card names no limits. ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 7.5B - Weights: about 3.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 702,365 - Likes: 687 - License: apache-2.0 - Based on: google/gemma-4-E4B-it - Quantized versions: 5 - Fine-tunes: 1 - Tags: gguf, gemma4, unsloth, gemma, google, image-text-to-text, conversational ## Links - Model: https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF - Files: https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/gemma-4-E4B-it-GGUF ``` 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 [unsloth/gemma-4-E4B-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-4-E4B-it-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 | | --- | --- | --- | --- | --- | --- | | gemma-4-E4B-it-GGUF (this page) | 702,365 | 687 | 7.5B | apache-2.0 | 2026-07-17 | | [gemma-4-12b-it-GGUF](https://mindket.com/p/5c808b31-349e-4069-ad4e-f80ffc3a1bee) | 2,534,530 | 963 | 11.9B | apache-2.0 | 2026-07-17 | | [gemma-4-E2B-it-GGUF](https://mindket.com/p/be304d82-a02d-4163-94a6-2fab2d7a7210) | 674,565 | 356 | 4.6B | apache-2.0 | 2026-07-17 | | [gemma-4-31B-it-GGUF](https://mindket.com/p/f257bf61-ef21-40bb-9e67-e4d77bf5e0e0) | 428,266 | 677 | 30.7B | apache-2.0 | 2026-07-17 | | [gemma-4-31B-it-qat-GGUF](https://mindket.com/p/808c5b4b-b27d-43fd-bf03-d1970ccfbce4) | 79,933 | 226 | 30.7B | apache-2.0 | 2026-07-17 | | [gemma-4-12B-it-qat-GGUF](https://mindket.com/p/97ef5b61-d969-4212-aa91-1789e4ce409f) | 196,871 | 617 | 11.9B | apache-2.0 | 2026-07-17 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · whythismatters · 2026-05-05](https://news.ycombinator.com/item?id=48020429): New Gemma chat template update by Google - [Bluesky · fuckface "squeaky" from:me · 2026-05-27](https://bsky.app/profile/skeet.best/post/3mmspxwisds2k): what if I told you that not only is stuff like this available in an irrevocable, personally-downloadable digital format, but it's so optimized that it can run entirely from many mid-range laptops - [Bluesky · Ryo Hanafusa · 2026-05-02](https://bsky.app/profile/fussa-rwxrwxrwx.bsky.social/post/3mktnseb5lc2f): Gemma 4 (E4B-it Q4\_K\_M) works well on Claude Code. It’s still an issue that the context window size is small due to my VRAM limitations. huggingface.co/unsloth/gemm... ## 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: 2026-07-17 - First published: 2026-04-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/gemma-4-E4B-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fa9df3ff-5b05-4e15-984f-04ccfc125e3e) - [Its page on Mindket](https://mindket.com/p/fa9df3ff-5b05-4e15-984f-04ccfc125e3e)