--- title: "gemma-3-270m-it-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/87dfd2c3-99d8-46f6-b32a-d49c2830280f" updated: "2026-11-13" --- # gemma-3-270m-it-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text from prompts as a 268M instruction-tuned Gemma 3 model in GGUF format, small enough for laptops and desktops. For developers who want a tiny local model to run or fine-tune. gemma-3-270m-it-GGUF 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 unsloth ## About ### What it is gemma-3-270m-it-GGUF is Unsloth's GGUF copy of Google's instruction-tuned Gemma 3 model with 268 million parameters. It reads a text prompt and writes text back, such as an answer or a summary. The card offers a free fine-tuning notebook for this size. ### Who it suits It suits developers and learners who want a tiny model on their own computer, and people who want a cheap model to practice fine-tuning on. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a small text model locally | Answering short questions inside a tool | | Education | Learners | Practice fine-tuning on a tiny model | Following the free notebook for this size | | Research | Experimenters | Test prompts and sampling settings on a small model | Comparing replies at different settings | You can skip it if you need the larger context of the bigger Gemma 3 sizes, which reach 128K tokens. ### Where to use it - Runtimes that load GGUF files - Google Colab, through the free notebook the card links for fine-tuning - Windows, macOS or Linux, with Python for the download step | Use case | Who | What they get back | | --- | --- | --- | | Question answering | Developers | A short written answer to your prompt | | Summarizing | Writers and developers | A shorter version of a document you paste in | | Fine-tuning practice | Learners | A small model to adapt to your own examples | ### How to set it up - Python, to run the Hugging Face download tool - The huggingface\_hub package - A runtime that can load GGUF files - Room for about 134 MB of model file, as a 4-bit estimate 1. Install the Hugging Face hub package. 2. Download the repository files to your computer. 3. Load the gguf file in your runtime and apply the sampling settings below. ```sh pip install -U huggingface_hub hf download unsloth/gemma-3-270m-it-GGUF ``` The card asks you to use these sampling settings. ```text temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0 ``` ### How to use it You load the model in your GGUF runtime, set the sampling values above, and type a prompt. The card also points to an Unsloth guide on running Gemma 3 correctly. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | Sampling settings | The values in the code block above, which the card says to use | | Unsloth notebook | A free fine-tuning notebook for this model size | ### Example You paste a paragraph and ask, "Summarize this in two sentences." The model writes a short summary back as plain text. ### For newcomers and experts If you are new, this model is tiny and fits modest hardware. Download the files, load them in a runtime, apply the settings above, and try a short question. If you know the field, it is the instruction-tuned 270M member of the Gemma 3 family with a 32K-token input context, converted to GGUF. The trade-off is size, since the family runs up to 27B. You type a prompt → The runtime loads the GGUF file → The model samples with the card's settings → Text comes back ### Good to know Input context by Gemma 3 size, in thousand tokens | Label | Value | | --- | --- | | 270M and 1B sizes | 32 K tokens | | 4B, 12B and 27B sizes | 128 K tokens | - Size: about 134 MB at 4-bit as an estimate - Family: Gemma 3 from Google DeepMind, with multilingual support in over 140 languages - License: gemma, under Google's Gemma terms - Cost to run: none named - Data it reaches: the prompts you give it, and the files you download from Hugging Face - Not for: the shown card text names none, but the Gemma terms of use apply ## README ````markdown # gemma-3-270m-it-GGUF Generates text from prompts as a 268M instruction-tuned Gemma 3 model in GGUF format, small enough for laptops and desktops. For developers who want a tiny local model to run or fine-tune. 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: gemma-3-270m-it-GGUF is Unsloth's GGUF copy of Google's instruction-tuned Gemma 3 model with 268 million parameters. It reads a text prompt and writes text back, such as an answer or a summary. The card offers a free fine-tuning notebook for this size. Who it suits: It suits developers and learners who want a tiny model on their own computer, and people who want a cheap model to practice fine-tuning on. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a small text model locally | Answering short questions inside a tool Education | Learners | Practice fine-tuning on a tiny model | Following the free notebook for this size Research | Experimenters | Test prompts and sampling settings on a small model | Comparing replies at different settings ``` You can skip it if you need the larger context of the bigger Gemma 3 sizes, which reach 128K tokens. Where to use it: - Runtimes that load GGUF files - Google Colab, through the free notebook the card links for fine-tuning - Windows, macOS or Linux, with Python for the download step ```table Use case | Who | What they get back Question answering | Developers | A short written answer to your prompt Summarizing | Writers and developers | A shorter version of a document you paste in Fine-tuning practice | Learners | A small model to adapt to your own examples ``` How to set it up: - Python, to run the Hugging Face download tool - The huggingface_hub package - A runtime that can load GGUF files - Room for about 134 MB of model file, as a 4-bit estimate 1. Install the Hugging Face hub package. 2. Download the repository files to your computer. 3. Load the gguf file in your runtime and apply the sampling settings below. ```sh pip install -U huggingface_hub hf download unsloth/gemma-3-270m-it-GGUF ``` The card asks you to use these sampling settings. ```text temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0 ``` How to use it: You load the model in your GGUF runtime, set the sampling values above, and type a prompt. The card also points to an Unsloth guide on running Gemma 3 correctly. ```table Tool | What it does hf download | Fetches the model files to your computer Sampling settings | The values in the code block above, which the card says to use Unsloth notebook | A free fine-tuning notebook for this model size ``` Example: You paste a paragraph and ask, "Summarize this in two sentences." The model writes a short summary back as plain text. For newcomers and experts: If you are new, this model is tiny and fits modest hardware. Download the files, load them in a runtime, apply the settings above, and try a short question. If you know the field, it is the instruction-tuned 270M member of the Gemma 3 family with a 32K-token input context, converted to GGUF. The trade-off is size, since the family runs up to 27B. ```flow You type a prompt The runtime loads the GGUF file The model samples with the card's settings Text comes back ``` Good to know: ```chart Title: Input context by Gemma 3 size, in thousand tokens 270M and 1B sizes: 32 K tokens 4B, 12B and 27B sizes: 128 K tokens ``` - Size: about 134 MB at 4-bit as an estimate - Family: Gemma 3 from Google DeepMind, with multilingual support in over 140 languages - License: gemma, under Google's Gemma terms - Cost to run: none named - Data it reaches: the prompts you give it, and the files you download from Hugging Face - Not for: the shown card text names none, but the Gemma terms of use apply ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 268M - Weights: about 134 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 91,489 - Likes: 181 - License: gemma - Based on: google/gemma-3-270m-it - Quantized versions: 2 - Tags: transformers, gguf, gemma3, unsloth, gemma, google, text-generation, conversational ## Links - Model: https://huggingface.co/unsloth/gemma-3-270m-it-GGUF - Files: https://huggingface.co/unsloth/gemma-3-270m-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-3-270m-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-3-270m-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-3-270m-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-3-270m-it-GGUF (this page) | 91,489 | 181 | 268M | gemma | 2025-08-15 | | [gemma-3-4b-it-GGUF](https://mindket.com/p/643c26b6-4129-4e6b-93c1-b35450442bc5) | 160,720 | 216 | 3.9B | gemma | 2025-08-14 | | [gemma-3-12b-it-GGUF](https://mindket.com/p/8ca21838-e184-4d92-b58a-0cdc0acd6909) | 84,937 | 204 | 11.8B | gemma | 2025-08-14 | | [gemma-3-27b-it-GGUF](https://mindket.com/p/92fcdbbc-a9e1-4d36-a8b5-90224a919f74) | 50,284 | 211 | 27B | gemma | 2025-08-14 | | [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 | | [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 | 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 · reneberlin · 2025-08-14](https://news.ycombinator.com/item?id=44904846): Gemma 3 270M: Compact model for hyper-efficient AI: I am sure with finetuning this can be changed somehow: (base) \~ ollama run hf.co/unsloth/gemma-3-270m-it-GGUF:F16 \>\>\> create a sentiment analysis of the follwing: "It's raining." The sentiment of the provided text is \*negative\*. \>\>\> create a sentiment analysis of the follwing: "It's raining… - [Hacker News · simonw · 2025-08-14](https://news.ycombinator.com/item?id=44903521): Gemma 3 270M: Compact model for hyper-efficient AI: Good call, I'm trying that one just now in LM Studio (by clicking "Use this model -\> LM Studio" on https://huggingface.co/unsloth/gemma-3-270m-it-GGUF and selecting the F16 one). (It did not do noticeably better at my pelican test). Actually it's worse than that, several of my attempts resulted in… - [Hacker News · 0x00cl · 2025-08-14](https://news.ycombinator.com/item?id=44903383): Gemma 3 270M: Compact model for hyper-efficient AI: I see you are using ollamas ggufs. By default it will download Q4\_0 quantization. Try \`gemma3:270m-it-bf16\` instead or you can also use unsloth ggufs \`hf.co/unsloth/gemma-3-270m-it-GGUF:16\` You'll get better results. ## 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: 2025-08-15 - First published: 2025-08-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/gemma-3-270m-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-3-270m-it-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/87dfd2c3-99d8-46f6-b32a-d49c2830280f) - [Its page on Mindket](https://mindket.com/p/87dfd2c3-99d8-46f6-b32a-d49c2830280f)