--- title: "Ling-3.0-tiny-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/74814513-c3e9-449b-a9d4-ce6e631d6c99" updated: "2026-11-13" --- # Ling-3.0-tiny-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF copy of inclusionAI's Ling-3.0-tiny, a small mixture-of-experts reasoning model with 7.9B total and 1.3B active parameters, for running locally at low cost. Ling-3.0-tiny-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 inclusionAI ## About ### What it is This repository holds a GGUF copy of Ling-3.0-tiny, a small reasoning model from inclusionAI. It is a mixture-of-experts model with 7.9B parameters in total and only 1.3B active for each token. The aim is reasoning and agent-style work at a low running cost, so it can run on local or limited hardware. The GGUF format is the one that local runners such as llama.cpp read. ### Who it suits People who want a compact reasoning model on their own machine, such as developers building local assistants and hobbyists who run models at home. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Add a small local reasoning model to a tool | An offline assistant inside a desktop app | | Research | Model tinkerers | Test how a mixture-of-experts model behaves on modest hardware | Comparing speed with a larger dense model | | Education | Students and teachers | Practice working with open models locally | A classroom demo with no cloud account | | Hobbyists | Local AI users | Run a 4-bit copy at home | Chatting with it on a spare laptop | You can skip it if you want the original full-precision weights, or a hosted chat service with nothing to download. ### Where to use it It is for local runners that load GGUF files, on any computer that can run them. - GGUF-capable runners such as llama.cpp - Windows, macOS and Linux with Python for the download step - Desktops and laptops with modest memory | Use case | Who | What they get back | | --- | --- | --- | | Local reasoning chat | Hobbyists | Step-by-step answers from a model on their own PC | | Agent experiments | Developers | A low-cost model to try in agent-style workflows | | Resource-limited deployment | Engineers | A model with only 1.3B parameters active per token | ### How to set it up You need Python and the Hugging Face tools to fetch the files, then a runner that opens GGUF files. The weights are about 3.9 GB at 4-bit, an estimate from Hugging Face. - Python with pip - A GGUF-capable runner - About 3.9 GB of disk for the 4-bit copy 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Open the GGUF file in your runner. ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny-GGUF ``` ### How to use it Load the downloaded GGUF file in your runner and chat with it as you would with any local model. The card gives no run command and sends you to the original Ling-3.0-tiny model card for details. ### Example You load the file and ask it to work through a multi-step logic puzzle. The model writes out its reasoning and answers, running only on your computer. ### For newcomers and experts If you are new, download the files with the two commands above, open the GGUF file in a local runner and ask a simple question first. Add harder reasoning questions once that works. If you know the field, the card describes a hybrid reasoning mixture-of-experts design. Only 1.3B of 7.9B parameters run per token, which lowers compute per token while the full model still needs memory for all of its weights. Download the GGUF file → Open it in a local runner → Your prompt goes in → Only 1.3B parameters activate per token → The model writes its reply ### Good to know The card is short and points to the original Ling-3.0-tiny model card for the full details. It does not list which quantization levels the files use. - License: MIT - Cost to run: none named, you use your own hardware - Data it reaches: the files you download from Hugging Face and the prompts you type locally - Not for: full-precision use, since this is a GGUF copy of the original model ## README ````markdown # Ling-3.0-tiny-GGUF GGUF copy of inclusionAI's Ling-3.0-tiny, a small mixture-of-experts reasoning model with 7.9B total and 1.3B active parameters, for running locally at low cost. Made by inclusionAI 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 a GGUF copy of Ling-3.0-tiny, a small reasoning model from inclusionAI. It is a mixture-of-experts model with 7.9B parameters in total and only 1.3B active for each token. The aim is reasoning and agent-style work at a low running cost, so it can run on local or limited hardware. The GGUF format is the one that local runners such as llama.cpp read. Who it suits: People who want a compact reasoning model on their own machine, such as developers building local assistants and hobbyists who run models at home. ```table Industry | Who uses it | How they use it | Example Software | Application developers | Add a small local reasoning model to a tool | An offline assistant inside a desktop app Research | Model tinkerers | Test how a mixture-of-experts model behaves on modest hardware | Comparing speed with a larger dense model Education | Students and teachers | Practice working with open models locally | A classroom demo with no cloud account Hobbyists | Local AI users | Run a 4-bit copy at home | Chatting with it on a spare laptop ``` You can skip it if you want the original full-precision weights, or a hosted chat service with nothing to download. Where to use it: It is for local runners that load GGUF files, on any computer that can run them. - GGUF-capable runners such as llama.cpp - Windows, macOS and Linux with Python for the download step - Desktops and laptops with modest memory ```table Use case | Who | What they get back Local reasoning chat | Hobbyists | Step-by-step answers from a model on their own PC Agent experiments | Developers | A low-cost model to try in agent-style workflows Resource-limited deployment | Engineers | A model with only 1.3B parameters active per token ``` How to set it up: You need Python and the Hugging Face tools to fetch the files, then a runner that opens GGUF files. The weights are about 3.9 GB at 4-bit, an estimate from Hugging Face. - Python with pip - A GGUF-capable runner - About 3.9 GB of disk for the 4-bit copy 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Open the GGUF file in your runner. ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny-GGUF ``` How to use it: Load the downloaded GGUF file in your runner and chat with it as you would with any local model. The card gives no run command and sends you to the original Ling-3.0-tiny model card for details. Example: You load the file and ask it to work through a multi-step logic puzzle. The model writes out its reasoning and answers, running only on your computer. For newcomers and experts: If you are new, download the files with the two commands above, open the GGUF file in a local runner and ask a simple question first. Add harder reasoning questions once that works. If you know the field, the card describes a hybrid reasoning mixture-of-experts design. Only 1.3B of 7.9B parameters run per token, which lowers compute per token while the full model still needs memory for all of its weights. ```flow Download the GGUF file Open it in a local runner Your prompt goes in Only 1.3B parameters activate per token The model writes its reply ``` Good to know: The card is short and points to the original Ling-3.0-tiny model card for the full details. It does not list which quantization levels the files use. - License: MIT - Cost to run: none named, you use your own hardware - Data it reaches: the files you download from Hugging Face and the prompts you type locally - Not for: full-precision use, since this is a GGUF copy of the original model ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 7.9B - Weights: about 3.9 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 11,961 - Likes: 55 - License: mit - Fine-tunes: 1 - Tags: gguf, text-generation, conversational ## Links - Model: https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF - Files: https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny-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 [inclusionAI/Ling-3.0-tiny-GGUF on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.0-tiny-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 | | --- | --- | --- | --- | --- | --- | | Ling-3.0-tiny-GGUF (this page) | 11,961 | 55 | 7.9B | mit | 2026-08-31 | | [Ornith-1.5-9B-GGUF](https://mindket.com/p/5dd265f6-9a3e-450b-ad2a-b49a78e2700a) | 5,772,425 | 673 | 9.2B | mit | 2026-08-24 | | [Ornith-1.5-35B-A3B-GGUF](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | | [Ornith-1.0-9B-GGUF](https://mindket.com/p/80667786-2895-41cb-b1d8-f6f6ae12215d) | 2,357,551 | 702 | 9B | mit | 2026-06-25 | | [Qwen3.8-35B-A3B-Distill-GGUF](https://mindket.com/p/d707b0c8-89b8-4c4f-8d49-2ff67de613c7) | 1,337,097 | 277 | 35.5B | apache-2.0 | 2026-09-16 | 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: 2026-08-31 - First published: 2026-08-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ling-3.0-tiny-GGUF on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/74814513-c3e9-449b-a9d4-ce6e631d6c99) - [Its page on Mindket](https://mindket.com/p/74814513-c3e9-449b-a9d4-ce6e631d6c99)