granite-4.2-3b-GGUF

granite-4.2-3b-GGUF

Quantized GGUF copies of IBM's granite-4.2-3b text model, in many sizes from full precision down to small 3-bit files. For people who run a small chat model on their own computer.

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 is a set of GGUF files made from IBM's granite-4.2-3b text generation model by bartowski. Each file shrinks the original weights to a different degree, so you can trade file size against answer quality to match your memory.

The files were produced with llama.cpp and an importance matrix. The model takes text in and gives text out. Hugging Face lists about 3.7 billion parameters.

Who it suits: People who want a small language model on a laptop or desktop and are happy to choose a file size themselves. Hugging Face lists English, German, Spanish, French, Japanese, Portuguese, Arabic and Czech.

Industry | Who uses it | How they use it | Example
Software | Developers | Run a small local model inside a tool they are building | Testing a chat feature without sending text to a server
Education | Students and teachers | Try a language model on an ordinary computer | Asking short questions while offline
Research | Hobbyists and tinkerers | Compare quant sizes against speed and memory | Picking the smallest file that still answers well
Support | Small teams | Draft replies with a model kept on their own machine | Writing first drafts of customer replies

Anyone who needs image or audio input can skip it, since the card lists text input only.

Where to use it:

• llama.cpp and apps built on it, since the files were made with that tool.
• Windows, macOS or Linux with Python, for downloading the files with the Hugging Face tool.
• Machines with little memory, using the smaller 3-bit files.

Use case | Who | What they get back
Local chat | Anyone with a spare computer | Text replies generated on their own machine
Size testing | Tinkerers | A clear view of how file size affects quality
Offline drafting | Writers and developers | Draft text without an internet connection

How to set it up: You need Python and the Hugging Face tool, plus a program that can load GGUF files.

• Python on Windows, macOS or Linux
• The huggingface_hub package
• A GGUF-capable runner such as llama.cpp
• Free disk space for the file you pick, from about 1.8 GB up to 7.32 GB

1. Install the Hugging Face tool.
2. Download the repository, or pick a single file from the list.
3. Load the file in your GGUF-capable runner.

pip install -U huggingface_hub
hf download bartowski/granite-4.2-3b-GGUF

How to use it: Load a file in your runner, then send prompts in the chat format the card gives. Fill in your own system text and question where the placeholders sit.

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

The card points beginners to the Q4_K_M file, a middle choice between size and quality. Here is how some of the files compare.

Quant | Size | What the card says
bf16 | 7.32GB | Full precision weights
Q8_0 | 3.89GB | Highest quality of the quantized files, rarely needed
Q6_K | 3.12GB | Near-perfect quality, recommended
Q5_K_M | 2.67GB | High quality, recommended
Q4_K_M | 2.32GB | The usual pick for most people
IQ4_XS | 2.08GB | Smaller than Q4_K_S at similar speed
Q3_K_L | 1.99GB | Lower quality, suits low memory
IQ3_M | 1.79GB | Medium-low quality, close to Q3_K_M

Example: Put a question such as "Explain the difference between RAM and disk storage" in the user slot of the format above, using the Q4_K_M file. The model writes a text reply. The format ends with an opening think tag, which suggests it may reason before it answers.

For newcomers and experts: If you are new, a GGUF file is one self-contained copy of a model that a local program can open. Start with the Q4_K_M file, send one short question, and see how fast and how good the reply is. Move up or down in size from there.

For experts, the files are llama.cpp quantizations made with an importance matrix. Several L variants keep the embedding and output weights at Q8_0, which costs a little size for quality. The Q4_1 and Q4_0 types are older formats, and the card lists speculative decoding as unsupported.

IBM publishes granite-4.2-3b
bartowski quantizes it with llama.cpp
You download the file that fits your memory
A local runner loads the GGUF file
You send prompts in the chat format

Good to know: The points below cover limits and costs.

• Size trade-off: smaller files use less memory but give lower quality, and the lowest ones are described as low quality.
• License: Apache 2.0.
• Cost to run: no API key or paid service is named. You need enough RAM and disk for your chosen file.
• Data it reaches: it reads the text you send. Downloading the files needs a connection to Hugging Face.
• Not for: image or other non-text input, and speculative decoding.

On Hugging Face, 13 November 2026

• Task: text generation
• Parameters: 3.7B
• Weights: about 1.8 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 6,928
• Likes: 9
• License: apache-2.0
• Languages: en, de, es, fr, ja, pt, ar, cs
• Based on: ibm-granite/granite-4.2-3b
• Tags: gguf, granite, granite-4.2, reasoning, thinking, tool-calling, text-generation, conversational

Links

• Model: https://huggingface.co/bartowski/granite-4.2-3b-GGUF
• Files: https://huggingface.co/bartowski/granite-4.2-3b-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download bartowski/granite-4.2-3b-GGUF

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