bartowski/granite-4.2-3b-GGUF
Llamacpp imatrix Quantizations of granite-4.2-3b by ibm-granite
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10603">b10603</a> for quantization.
Original model: https://huggingface.co/ibm-granite/granite-4.2-3b
Model details:
- Parameter count: 4B
- Input support: text
- Speculative decoding: no
- imatrix: yes - details
Prompt format
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>Don't know which to choose? Grab Q4_K_M (2.32GB) - usually a good mix of size and performance. Download instructions available here
Available files:
Download a specific file:
hf download bartowski/granite-4.2-3b-GGUF --include "granite-4.2-3b-Q4_K_M.gguf" --local-dir ./Downloading using the Hugging Face CLI
<details> <summary>Click to view download instructions</summary>
First, make sure you have the Hugging Face CLI installed:
pip install -U "huggingface_hub[cli]"Download a specific file:
hf download bartowski/granite-4.2-3b-GGUF --include "granite-4.2-3b-Q4_K_M.gguf" --local-dir ./</details>
How to run
These quants run with llama.cpp - installable in one line via llama.app:
curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/granite-4.2-3b-GGUF:Q4_K_Mllama-server includes a built-in chat web UI, served at http://localhost:8080 by default.
These quants were made with llama.cpp release b10603 - if this model's architecture is newly supported, you'll need that release or newer to run them.
They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat
imatrix
All quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs plain prose with tool-calling and reasoning conversations (corpus source data), encoded exactly as this model sees them at inference and processed with --parse-special, so chat-format special tokens contribute to the importance matrix. The corpus rendered for this model is included in this repo: granite-4.2-3b-calibration-v6.txt. The imatrix is available here: granite-4.2-3b-imatrix.gguf.
<details> <summary>Calibration render details</summary>
{
"generator": "auto_quant_v2 calibration renderer",
"recipe": "calibration-v6",
"model": "granite-4.2-3b",
"encoder": "chat_template",
"chunk_size": 512,
"prose_chunks": 238,
"tool_chunks": 335,
"total_chunks": 573,
"tool_chunk_fraction": 0.585,
"n_conversations": 137,
"extension_convs_used": 0,
"conversation_token_lengths": [
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],
"warnings": []
}</details>
Embed/output weights
Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
ARM/AVX information
llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4044/48/88 files; those are long gone. Online repacking now covers Q40, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.
Which file should I choose?
<details> <summary>Click here for details</summary>
An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QXKX', like Q5KM.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQXX, like IQ3M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski