--- title: "Llama-3.2-3B-Instruct-GGUF by unsloth" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ab971aa3-d8d6-4252-8d06-8a7963b1ad29" updated: "2026-11-13" --- # Llama-3.2-3B-Instruct-GGUF by unsloth > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats, summarizes and answers questions with Meta's compact Llama 3.2 3B instruct model, in GGUF files by Unsloth from 2-bit up to 16-bit. Small enough for ordinary laptops. Llama-3.2-3B-Instruct-GGUF by unsloth 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 This repository holds GGUF versions of Llama-3.2-3B-Instruct, a 3.2B parameter text model from Meta tuned to follow instructions. Unsloth made the files, and they come in sizes from 2-bit up to 16-bit. At 4-bit the weights are estimated at about 1.6 GB. ### Who it suits People who want a small local assistant for chat, summaries and retrieval help on a laptop or desktop. Developers who plan to fine-tune a model will also like the free notebooks the card points to. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a small local chat model to an app | Draft short replies inside a tool | | Education | Students | Chat with a model on a modest laptop | Ask for a plain explanation of a topic | | Business | Small teams | Summarize notes and look things up in their own texts | Shorten a long meeting memo | | Research | AI researchers | Fine-tune a compact model on their own data | Adapt it to a narrow task | People who need top answer quality on hard tasks can skip it, because this is a compact model. ### Where to use it - Tools that load GGUF files, such as llama.cpp - Unsloth's free notebooks, when you want to fine-tune the model - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Chat | Anyone | Written replies in a back and forth conversation | | Summarizing | Students and teams | Shorter versions of longer texts | | Agentic retrieval | Developers | Help finding and using information in tools | | Fine-tuning | ML engineers | A model adapted to your own data | ### How to set it up - Python, for the Hugging Face hub tools - A runner that loads GGUF files - Disk space for the file you choose, with the 4-bit weights estimated at about 1.6 GB 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. Load a file in your runner. ```sh pip install -U huggingface_hub hf download unsloth/Llama-3.2-3B-Instruct-GGUF ``` ### How to use it The card shows no run command in the part given. Load the GGUF file in your runner and chat with it. The model was tuned for dialogue in several languages, so you can ask questions, request summaries or try retrieval style tasks. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | GGUF runner | Loads a file and lets you chat with the model | | Unsloth notebooks | Free notebooks for fine-tuning, with export to GGUF | ### Example You download the 4-bit file, load it in your runner and paste in a long note. You ask for a short summary, and the model writes a few sentences back. ### For newcomers and experts Start with the 4-bit file, which is the smallest practical size for most laptops. If answers feel weak, try a larger file. If you want a model for your own data, use a notebook from Unsloth's collection. The model is an auto-regressive language model with an optimized transformer design. Meta tuned it with supervised fine-tuning and reinforcement learning from human feedback, aiming for helpfulness and safety. The card says the instruction-tuned versions beat many open and closed chat models on common benchmarks. Download a GGUF file → Load it in your runner → Send a prompt as a chat message → Read the reply ### Good to know - Languages: Eight are officially supported, among them English, German, French, Spanish and Thai. More can be added by fine-tuning, within Meta's terms. - Sizes: The files range from 2-bit to 16-bit, so you can trade quality for memory. - Speed: The card says Unsloth notebooks fine-tune the Llama 3.2 3B model 2.4x faster with 58% less memory. - License: The Llama 3.2 Community License, plus Meta's Acceptable Use Policy. - Cost to run: none named. The notebooks run on free Google Colab, according to the card. - Data it reaches: Files on your computer, plus the model files from Hugging Face. - Not for: Uses that break Meta's Acceptable Use Policy. ## README ````markdown # Llama-3.2-3B-Instruct-GGUF by unsloth Chats, summarizes and answers questions with Meta's compact Llama 3.2 3B instruct model, in GGUF files by Unsloth from 2-bit up to 16-bit. Small enough for ordinary laptops. 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 repository holds GGUF versions of Llama-3.2-3B-Instruct, a 3.2B parameter text model from Meta tuned to follow instructions. Unsloth made the files, and they come in sizes from 2-bit up to 16-bit. At 4-bit the weights are estimated at about 1.6 GB. Who it suits: People who want a small local assistant for chat, summaries and retrieval help on a laptop or desktop. Developers who plan to fine-tune a model will also like the free notebooks the card points to. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a small local chat model to an app | Draft short replies inside a tool Education | Students | Chat with a model on a modest laptop | Ask for a plain explanation of a topic Business | Small teams | Summarize notes and look things up in their own texts | Shorten a long meeting memo Research | AI researchers | Fine-tune a compact model on their own data | Adapt it to a narrow task ``` People who need top answer quality on hard tasks can skip it, because this is a compact model. Where to use it: - Tools that load GGUF files, such as llama.cpp - Unsloth's free notebooks, when you want to fine-tune the model - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Chat | Anyone | Written replies in a back and forth conversation Summarizing | Students and teams | Shorter versions of longer texts Agentic retrieval | Developers | Help finding and using information in tools Fine-tuning | ML engineers | A model adapted to your own data ``` How to set it up: - Python, for the Hugging Face hub tools - A runner that loads GGUF files - Disk space for the file you choose, with the 4-bit weights estimated at about 1.6 GB 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. Load a file in your runner. ```sh pip install -U huggingface_hub hf download unsloth/Llama-3.2-3B-Instruct-GGUF ``` How to use it: The card shows no run command in the part given. Load the GGUF file in your runner and chat with it. The model was tuned for dialogue in several languages, so you can ask questions, request summaries or try retrieval style tasks. ```table Tool | What it does hf download | Fetches the model files to your computer GGUF runner | Loads a file and lets you chat with the model Unsloth notebooks | Free notebooks for fine-tuning, with export to GGUF ``` Example: You download the 4-bit file, load it in your runner and paste in a long note. You ask for a short summary, and the model writes a few sentences back. For newcomers and experts: Start with the 4-bit file, which is the smallest practical size for most laptops. If answers feel weak, try a larger file. If you want a model for your own data, use a notebook from Unsloth's collection. The model is an auto-regressive language model with an optimized transformer design. Meta tuned it with supervised fine-tuning and reinforcement learning from human feedback, aiming for helpfulness and safety. The card says the instruction-tuned versions beat many open and closed chat models on common benchmarks. ```flow Download a GGUF file Load it in your runner Send a prompt as a chat message Read the reply ``` Good to know: - Languages: Eight are officially supported, among them English, German, French, Spanish and Thai. More can be added by fine-tuning, within Meta's terms. - Sizes: The files range from 2-bit to 16-bit, so you can trade quality for memory. - Speed: The card says Unsloth notebooks fine-tune the Llama 3.2 3B model 2.4x faster with 58% less memory. - License: The Llama 3.2 Community License, plus Meta's Acceptable Use Policy. - Cost to run: none named. The notebooks run on free Google Colab, according to the card. - Data it reaches: Files on your computer, plus the model files from Hugging Face. - Not for: Uses that break Meta's Acceptable Use Policy. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 3.2B - Weights: about 1.6 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 243,788 - Likes: 93 - License: llama3.2 - Languages: en - Based on: meta-llama/Llama-3.2-3B-Instruct - Quantized versions: 1 - Fine-tunes: 1 - Tags: transformers, gguf, llama, text-generation, llama-3, meta, facebook, unsloth, conversational ## Links - Model: https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF - Files: https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/Llama-3.2-3B-Instruct-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/Llama-3.2-3B-Instruct-GGUF on Hugging Face](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-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 | | --- | --- | --- | --- | --- | --- | | Llama-3.2-3B-Instruct-GGUF by unsloth (this page) | 243,788 | 93 | 3.2B | llama3.2 | 2025-11-08 | | [Llama-3.2-1B-Instruct-GGUF](https://mindket.com/p/bbc280ad-bc5e-44ab-bbe3-8d5a6cab1a9b) | 53,598 | 85 | 1.2B | llama3.2 | 2025-05-09 | | [Meta-Llama-3.1-8B-Instruct-GGUF](https://mindket.com/p/51184fc4-b11d-4c65-93f0-2107bafe9b0e) | 322,920 | 433 | 8B | llama3.1 | 2024-12-01 | | [Llama-3.2-3B-Instruct-GGUF](https://mindket.com/p/a5ec8e51-6ef0-456c-97bc-9c7fb27a06d6) | 190,537 | 261 | 3.2B | llama3.2 | 2024-10-08 | | [Llama-3.2-1B-Instruct GGUF](https://mindket.com/p/81d4259b-7ebb-48c2-80ef-cd6cc65c611f) | 138,010 | 188 | 1.2B | llama3.2 | 2024-10-08 | | [Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://mindket.com/p/ccfe63e6-9cb6-47ca-98b8-084a038b9851) | 319,739 | 96 | 8B | llama3.1 | 2024-08-07 | Numbers as Mindket last read them, from 2026-11-13 to 2026-11-14. ## 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: 2025-11-08 - First published: 2024-09-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/Llama-3.2-3B-Instruct-GGUF on Hugging Face](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ab971aa3-d8d6-4252-8d06-8a7963b1ad29) - [Its page on Mindket](https://mindket.com/p/ab971aa3-d8d6-4252-8d06-8a7963b1ad29)