--- title: "Llama-3.2-3B-Instruct-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a5ec8e51-6ef0-456c-97bc-9c7fb27a06d6" updated: "2026-11-13" --- # Llama-3.2-3B-Instruct-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF quantizations of Meta's Llama-3.2-3B-Instruct, made by bartowski with an importance matrix, from about 1.8 GB to 6.4 GB. For running a small chat model locally with llama.cpp or LM Studio. Llama-3.2-3B-Instruct-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 bartowski ## About ### What it is This repository holds GGUF quantizations of Meta's Llama-3.2-3B-Instruct, a 3.2B-parameter chat model. Bartowski made the files with llama.cpp and an importance matrix. Many quant levels are offered, so you can match the file to your memory. ### Who it suits People who want a small chat model on a laptop or desktop with no cloud account. The page lists English, German, French, Italian, Portuguese, Hindi, Spanish and Thai. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists | Chat with a small model on their own computer | Load a mid-size quant in LM Studio and ask questions | | Software | Developers | Run a light instruction model inside a local app | Serve it with llama.cpp for a prototype | | Mobile and ARM | Edge developers | Use quants made for ARM chips | Pick the ARM-tuned file for a board that supports it | | Education | Students | Study how quantization changes size and quality | Compare Q8\_0 and Q4\_K\_M on the same prompt | You can skip it if you need long reasoning or a large model. A 3B model has limits that a larger one does not. ### Where to use it - LM Studio, which the card names for running the files - llama.cpp and other tools that read GGUF - Windows, macOS and Linux with Python, for downloading | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Hobbyists | Text replies from a 3B model on your machine | | Low-memory use | People with little RAM | A small quant that still runs | | ARM devices | Edge developers | A quant tuned for ARM inference | ### How to set it up Download one file, not the whole branch, as the card advises. - Python and the Hugging Face tool, or the download button in LM Studio - A program that reads GGUF, such as LM Studio or llama.cpp - Memory for the quant you choose, from about 1.8 GB to 6.4 GB 1. Choose a quant from the file list below. 2. Download that one file. 3. Open it in LM Studio or llama.cpp. 4. Use the prompt format shown for the model. ```bash pip install -U huggingface_hub hf download bartowski/Llama-3.2-3B-Instruct-GGUF ``` ### How to use it Load a file in LM Studio and chat, or run it in llama.cpp with the prompt format below. The command above fetches the whole repository, so narrow it to one file. ```text <|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 26 Jul 2024 {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` | Quant | Size and role | | --- | --- | | f16 | 6.43 GB, the full F16 weights | | Q8\_0 | 3.42 GB, the largest quant, rarely needed | | Q6\_K\_L | 2.74 GB, Q8\_0 for embedding and output weights, near-perfect quality | | Q6\_K | 2.64 GB, near-perfect quality and recommended | | Q5\_K\_M | 2.32 GB, high quality and recommended | | Q4\_K\_L | 2.11 GB, Q8\_0 for embedding and output weights, good quality | | Q4\_K\_M | 2.02 GB, the default size for most uses | | Q4\_K\_S | 1.93 GB, a little lower quality with more savings | | Q4\_0\_4\_4 | 1.92 GB, tuned for ARM chips and the safe ARM pick | | Q3\_K\_XL | 1.91 GB, lower quality but usable on low RAM | | IQ4\_XS | 1.83 GB, decent quality and smaller than Q4\_K\_S | ### Example You download the Q4\_K\_M file, open it in LM Studio and ask for three tips for a first job interview. The model replies with a short list of tips in plain text. ### For newcomers and experts A quant is a smaller copy of a model with its numbers stored in fewer bits. If you are unsure, take Q4\_K\_M, which the card calls the default for most uses. Choose Q6\_K if you have memory to spare. Quants with L in the name keep the embedding and output weights at Q8\_0. The ARM-specific Q4\_0 variants need features such as sve or i8mm, and Q4\_0\_4\_4 is the pick when you are unsure about your chip. The card calls plain Q4\_0 a legacy format. All quants used an importance matrix built from a dataset the card links. Pick a quant that fits your memory → Download that one file → Open it in LM Studio or llama.cpp → Chat using the prompt format File size in GB | Label | Value | | --- | --- | | f16 | 6.43 GB | | Q8\_0 | 3.42 GB | | Q6\_K | 2.64 GB | | Q5\_K\_M | 2.32 GB | | Q4\_K\_M | 2.02 GB | | IQ4\_XS | 1.83 GB | ### Good to know - The file list shown is cut off, so more quants may exist. - The prompt format sets a knowledge cutoff and a date inside the system block. - The model supports eight languages, listed above. - License: llama3.2, as listed on the page. Read its terms before use. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: the card names no limits. ## README ````markdown # Llama-3.2-3B-Instruct-GGUF GGUF quantizations of Meta's Llama-3.2-3B-Instruct, made by bartowski with an importance matrix, from about 1.8 GB to 6.4 GB. For running a small chat model locally with llama.cpp or LM Studio. Made by bartowski 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 quantizations of Meta's Llama-3.2-3B-Instruct, a 3.2B-parameter chat model. Bartowski made the files with llama.cpp and an importance matrix. Many quant levels are offered, so you can match the file to your memory. Who it suits: People who want a small chat model on a laptop or desktop with no cloud account. The page lists English, German, French, Italian, Portuguese, Hindi, Spanish and Thai. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists | Chat with a small model on their own computer | Load a mid-size quant in LM Studio and ask questions Software | Developers | Run a light instruction model inside a local app | Serve it with llama.cpp for a prototype Mobile and ARM | Edge developers | Use quants made for ARM chips | Pick the ARM-tuned file for a board that supports it Education | Students | Study how quantization changes size and quality | Compare Q8_0 and Q4_K_M on the same prompt ``` You can skip it if you need long reasoning or a large model. A 3B model has limits that a larger one does not. Where to use it: - LM Studio, which the card names for running the files - llama.cpp and other tools that read GGUF - Windows, macOS and Linux with Python, for downloading ```table Use case | Who | What they get back Local chat | Hobbyists | Text replies from a 3B model on your machine Low-memory use | People with little RAM | A small quant that still runs ARM devices | Edge developers | A quant tuned for ARM inference ``` How to set it up: Download one file, not the whole branch, as the card advises. - Python and the Hugging Face tool, or the download button in LM Studio - A program that reads GGUF, such as LM Studio or llama.cpp - Memory for the quant you choose, from about 1.8 GB to 6.4 GB 1. Choose a quant from the file list below. 2. Download that one file. 3. Open it in LM Studio or llama.cpp. 4. Use the prompt format shown for the model. ```bash pip install -U huggingface_hub hf download bartowski/Llama-3.2-3B-Instruct-GGUF ``` How to use it: Load a file in LM Studio and chat, or run it in llama.cpp with the prompt format below. The command above fetches the whole repository, so narrow it to one file. ```text <|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 26 Jul 2024 {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ```table Quant | Size and role f16 | 6.43 GB, the full F16 weights Q8_0 | 3.42 GB, the largest quant, rarely needed Q6_K_L | 2.74 GB, Q8_0 for embedding and output weights, near-perfect quality Q6_K | 2.64 GB, near-perfect quality and recommended Q5_K_M | 2.32 GB, high quality and recommended Q4_K_L | 2.11 GB, Q8_0 for embedding and output weights, good quality Q4_K_M | 2.02 GB, the default size for most uses Q4_K_S | 1.93 GB, a little lower quality with more savings Q4_0_4_4 | 1.92 GB, tuned for ARM chips and the safe ARM pick Q3_K_XL | 1.91 GB, lower quality but usable on low RAM IQ4_XS | 1.83 GB, decent quality and smaller than Q4_K_S ``` Example: You download the Q4_K_M file, open it in LM Studio and ask for three tips for a first job interview. The model replies with a short list of tips in plain text. For newcomers and experts: A quant is a smaller copy of a model with its numbers stored in fewer bits. If you are unsure, take Q4_K_M, which the card calls the default for most uses. Choose Q6_K if you have memory to spare. Quants with L in the name keep the embedding and output weights at Q8_0. The ARM-specific Q4_0 variants need features such as sve or i8mm, and Q4_0_4_4 is the pick when you are unsure about your chip. The card calls plain Q4_0 a legacy format. All quants used an importance matrix built from a dataset the card links. ```flow Pick a quant that fits your memory Download that one file Open it in LM Studio or llama.cpp Chat using the prompt format ``` ```chart Title: File size in GB f16: 6.43 GB Q8_0: 3.42 GB Q6_K: 2.64 GB Q5_K_M: 2.32 GB Q4_K_M: 2.02 GB IQ4_XS: 1.83 GB ``` Good to know: - The file list shown is cut off, so more quants may exist. - The prompt format sets a knowledge cutoff and a date inside the system block. - The model supports eight languages, listed above. - License: llama3.2, as listed on the page. Read its terms before use. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: the card names no limits. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 3.2B - Weights: about 1.6 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 190,537 - Likes: 261 - License: llama3.2 - Languages: en, de, fr, it, pt, hi, es, th - Based on: meta-llama/Llama-3.2-3B-Instruct - Quantized versions: 4 - Fine-tunes: 1 - Tags: gguf, facebook, meta, llama, llama-3, text-generation, conversational ## Links - Model: https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF - Files: https://huggingface.co/bartowski/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 bartowski/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, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [bartowski/Llama-3.2-3B-Instruct-GGUF on Hugging Face](https://huggingface.co/bartowski/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 (this page) | 190,537 | 261 | 3.2B | llama3.2 | 2024-10-08 | | [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 by unsloth](https://mindket.com/p/ab971aa3-d8d6-4252-8d06-8a7963b1ad29) | 243,788 | 93 | 3.2B | llama3.2 | 2025-11-08 | | [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 | | [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-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: 2024-10-08 - First published: 2024-09-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open bartowski/Llama-3.2-3B-Instruct-GGUF on Hugging Face](https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a5ec8e51-6ef0-456c-97bc-9c7fb27a06d6) - [Its page on Mindket](https://mindket.com/p/a5ec8e51-6ef0-456c-97bc-9c7fb27a06d6)