Llama-3.2-1B-Instruct GGUF

Llama-3.2-1B-Instruct GGUF

GGUF quantizations of Llama-3.2-1B-Instruct, a 1.2B text model in eight languages, with the 4-bit file near 0.8 GB. For people who run a tiny local model.

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 Llama-3.2-1B-Instruct, a small instruction-tuned text model with about 1.2B parameters. A third party made the files with llama.cpp and an importance matrix, so the model runs in local tools such as LM Studio. The 4-bit weights are about 618 MB.

Who it suits: It suits people who want a tiny chat model on an ordinary laptop, an older PC or a small device. The eight languages Hugging Face lists are English, German, French, Italian, Portuguese, Hindi, Spanish and Thai.

Industry | Who uses it | How they use it | Example
Hobby AI | Enthusiasts | Chat with a tiny model offline in LM Studio | Asking quick everyday questions
Software | Developers | Test prompts and app plumbing on a light model | Checking a system prompt in seconds
Education | Students | See how a local language model behaves | Comparing two quant sizes
Low-memory setups | People with little RAM | Run a file under 1 GB | Using Q4_K_M on an old laptop

If you need strong answers on hard tasks, you can skip a 1B model.

Where to use it: You load one file in an app that reads GGUF.

• LM Studio, which the card names
• Other llama.cpp based tools, since the files were made with llama.cpp
• Windows, macOS or Linux with Python, to download the files

Use case | Who | What they get back
Local chat | Hobbyists | Replies from a tiny model on your own computer
Prompt testing | Developers | Quick answers from a light model
Quant comparison | Learners | A feel for size against quality

How to set it up: You need an app that loads GGUF files. Every file here is small, so memory is rarely the limit. Download one file rather than the whole repository.

• LM Studio or another GGUF-capable app
• A file that fits your RAM, with Q4_K_M as the card's default size
• Python, only for the Hugging Face download command

1. Pick a file from the table, for example Q4_K_M.
2. Download it from the repository.
3. Load it in your app and use the prompt format below.

Title: Some files in the card
Quant | Size | Use
f16 | 2.48 GB | Full weights
Q8_0 | 1.32 GB | Top quality, more than most people need
Q6_K | 1.02 GB | Near-perfect quality, recommended
Q5_K_M | 0.91 GB | High quality, recommended
Q4_K_M | 0.81 GB | Good quality, the card's default pick
IQ4_XS | 0.74 GB | Decent quality in a smaller file

pip install -U huggingface_hub
hf download bartowski/Llama-3.2-1B-Instruct-GGUF

How to use it: Load the file and use the chat format below, where you fill in the system prompt and your message. Most apps apply this layout for you. The card also lists three ARM-tuned Q4_0 files for phones and similar chips.

<|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|>

Example: You load the Q4_K_M file in LM Studio, write a short system prompt that asks for brief answers, and ask for three tips to keep a desk tidy. The model replies in the chat window with a short list.

For newcomers and experts: A quantized file stores a model's numbers more coarsely, so it is smaller and a little less accurate. Start with Q4_K_M, and move up or down in size if answers or memory use disappoint.

For experts, the quants use an importance matrix and were built with llama.cpp release b3821. Q6_K_L, Q5_K_L, Q4_K_L and Q3_K_XL keep embedding and output weights at Q8_0. Three Q4_0 variants target ARM CPUs.

The original model is converted to GGUF
An importance matrix guides the quantization
Many sizes are written, from f16 down
You download one file that fits your RAM
You load it in LM Studio or llama.cpp

Good to know: The Q3 file is usable but lower in quality, and the Q4_0 file is a legacy format the card does not rate highly. Hugging Face lists the Q4_K_M file at 0.81 GB.

Title: File size by quant type
f16: 2.48 GB
Q8_0: 1.32 GB
Q6_K: 1.02 GB
Q5_K_M: 0.91 GB
Q4_K_M: 0.81 GB
IQ4_XS: 0.74 GB

• License: listed as llama3.2 on Hugging Face
• Cost to run: none named
• Data it reaches: your prompts, run locally, and the files you download
• Not for: hard reasoning tasks that need a large model, since this one has about 1.2B parameters

On Hugging Face, 13 November 2026

• Task: text generation
• Parameters: 1.2B
• Weights: about 618 MB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 138,010
• Likes: 188
• License: llama3.2
• Languages: en, de, fr, it, pt, hi, es, th
• Based on: meta-llama/Llama-3.2-1B-Instruct
• Quantized versions: 3
• Tags: gguf, facebook, meta, llama, llama-3, text-generation, conversational

Links

• Model: https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
• Files: https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download bartowski/Llama-3.2-1B-Instruct-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.