Granite 4.1 3B SVG Pelican Gallery

<p><strong><a href="https://simonw.github.io/granite-4.1-3b-gguf-pelicans/">Granite 4.1 3B SVG Pelican Gallery</a></strong></p> IBM released their <a href="https://research.ibm.com/blog/granite-4-1-ai-foundation-models">Granite 4.1 family</a> of LLMs a few days ago. They're Apache 2.0 licensed and come in 3B, 8B and 30B sizes.</p> <p><a href="https://huggingface.co/blog/ibm-granite/granite-4-1">Granite 4.1 LLMs: How They’re Built</a> by Granite team member Yousaf Shah describes the training process in detail.</p> <p>Unsloth released the <a href="https://huggingface.co/unsloth/granite-4.1-3b-GGUF">unsloth/granite-4.1-3b-GGUF</a> collection of GGUF encoded quantized variants of the 3B model - 21 different model files ranging in size from 1.2GB to 6.34GB.</p> <p>All 21 of those Unsloth files add up to 51.3GB, which inspired me to finally try an experiment I've been wanting to run for ages: prompting "Generate an SVG of a pelican riding a bicycle" against different sized quantized variants of the same model to see what the results would look like.</p> <p>Honestly, <a href="https://simonw.github.io/granite-4.1-3b-gguf-pelicans/">the results</a> are less interesting than I expected. There's no distinguishable pattern relating quality to size - they're all pretty terrible!</p> <p><img alt="Six different SVG images from models ranging in size from 1.67GB to 1.2GB. They are almost all an abstract collection of shapes - weirdly the smallest model had the best version of a bicycle, while the largest one had something that looked a tiny bit like a pelican." src="https://static.simonwillison.net/static/2026/granite-3B-pelicans.jpg" /></p> <p>I'll likely try this again in the future with a model that's better at drawing pelicans. <p>Tags: <a href="https://simonwillison.net/tags/ibm">ibm</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a