Simon Willison’s Weblog

Ars Live: Our first encounter with manipulative AI (via) I'm participating in a live conversation with Benj Edwards on 19th November reminiscing over that incredible time back in February last year when Bing went feral.

# 11:58 pm / bing, generative-ai, arstechnica, ai, speaking, llms, benj-edwards

django-plugin-django-debug-toolbar (via) Tom Viner built a plugin for my DJP Django plugin system that configures the excellent django-debug-toolbar debugging tool.

You can see everything it sets up for you in this Python code: it configures installed apps, URL patterns and middleware and sets the INTERNAL_IPS and DEBUG settings.

Here are Tom's running notes as he created the plugin.

# 1:14 am / djp, django, plugins

Ollama: Llama 3.2 Vision. Ollama released version 0.4 last week with support for Meta's first Llama vision model, Llama 3.2.

If you have Ollama installed you can fetch the 11B model (7.9 GB) like this:

ollama pull llama3.2-vision

Or the larger 90B model (55GB download, likely needs ~88GB of RAM) like this:

ollama pull llama3.2-vision:90b

I was delighted to learn that Sukhbinder Singh had already contributed support for LLM attachments to Sergey Alexandrov's llm-ollama plugin, which means the following works once you've pulled the models:

llm install --upgrade llm-ollama
llm -m llama3.2-vision:latest 'describe' \
  -a https://static.simonwillison.net/static/2024/pelican.jpg

This image features a brown pelican standing on rocks, facing the camera and positioned to the left of center. The bird's long beak is a light brown color with a darker tip, while its white neck is adorned with gray feathers that continue down to its body. Its legs are also gray.

In the background, out-of-focus boats and water are visible, providing context for the pelican's environment.

That's not a bad description of this image, especially for a 7.9GB model that runs happily on my MacBook Pro.

# 1:55 am / vision-llms, llm, llama, ai, edge-llms, llms, meta, ollama, generative-ai

Releasing the largest multilingual open pretraining dataset (via) Common Corpus is a new "open and permissible licensed text dataset, comprising over 2 trillion tokens (2,003,039,184,047 tokens)" released by French AI Lab PleIAs.

This appears to be the largest available corpus of openly licensed training data:

• 926,541,096,243 tokens of public domain books, newspapers, and Wikisource content
• 387,965,738,992 tokens of government financial and legal documents
• 334,658,896,533 tokens of open source code from GitHub
• 221,798,136,564 tokens of academic content from open science repositories
• 132,075,315,715 tokens from Wikipedia, YouTube Commons, StackExchange and other permissively licensed web sources

It's majority English but has significant portions in French and German, and some representation for Latin, Dutch, Italian, Polish, Greek and Portuguese.

I can't wait to try some LLMs trained exclusively on this data. Maybe we will finally get a GPT-4 class model that isn't trained on unlicensed copyrighted data.

# 5:44 am / ethics, generative-ai, training-data, ai, llms