Several interesting releases today/recently.
Multi-modal llama: llama3.2. Tons of model infra providers announced availability day one. We seem to be getting into a bit of a rhythm here. It’s also convenient for Meta who doesn’t need to scale the infra (though they of all companies would probably be capable) – providers do it for them.
AllenAI’s Olmo: another interesting, open source multi-modal model.
Open source is catching up in multi-modal. I’m looking forward to experimenting with both of these.I finally found some time to run a more comprehensive evals of Connections with one guess at a time and using Python code to validate the guesses and give feedback. I ran about 100 puzzles with gpt-4o-mini, gp-4o, and claude-3-5-sonnet, but it became clear that Sonnet was going to perform the best, so I decide to only complete the 466 puzzles released as of today with Sonnet. This wasn’t cheap but it was interesting to see the results.Some interesting commentary on the behaviors of founders, managers and leaders written by Rands.
Race’s article on using Jupyter notebooks with Hugo was a helpful intro to the landscape.There have been a number of small-in-scope, but tough problems that I’ve run into that models haven’t been able crack as l’ve presented them via prompting. Usually, these are problems with a few separate areas of complexity, like a recursive parser plus a weird templating language to do it in. o1 is the first model that I can recall that took my high level approach and suggested a simplifying change to the input (tree -F to tree -J -F) that meaningfuly simplified the problem’s complexity (the parser is no longer needed if the input is JSON).I’m making another, more thorough pass of course.fast.ai, including all notebooks and videos and this time I am going to focus more on the projects. I’ll also be logging a lot more notes as doing so is by far the most effective way that I learn things.
The course materials are very detailed but I’ve still run into some rough edges. The image search for bird vs. forest image classifier didn’t quite work without some modifications to make the search work.A nice writeup by Eugene on building a simple data viewer webapp with a few different framworks. I am going to need to try out including llm-ctx.txt next time I write FastHTML to see if it helps make the language model better at writing it.I was going to write a quick guide on how to get up and running using Google’s Gemini model via API, since I found it quite straightforward and Twitter is currently dunking on Google for how hard this is. When I tried to retrace my steps, the CSS for the documentation was failing to load with a 503, so I guess this will have to wait until another day.I am continuing to see a lot of buzz about ColPali and Qwen2-VL. I’d like to try these out but haven’t put together enough of the pieces to make sense of it yet. I am also seeing a lot of conversation about how traditional OCR to LLM pipelines will be superseded by these approaches. Based on my experience with VLMs, this seems directionally correct. The overall amount of noise makes it tough to figure out what is worth focusing on and what is real vs.Played around a bit with baml for extraction structured data with a VLM. It’s an interesting approach and has better ergonomics and tooling from most things I’ve tried so far. I like how you can declare test cases in the same place as the object schemas and that there is a built-in playground. I need to see how to handle multi-step pipelines.
I experimented with doing data extraction from pictures of menus.Benchmarking >80 LLMs shows: The best model is not necessarily the best for your programming language š±
- Best overall: Anthropicās Sonnet 3.5
- Best for Go: Metaās Llama 3.1 405B
- Best for Java: OpenAIās GPT-4 Turbo
- Best for Ruby: OpenAIās GPT-4o
Good models for oneā¦ pic.twitter.com/EYUphEI5rH
— Markus Zimmermann (@zimmskal) September 2, 2024 Great to see more concrete results published on how different models are “the best” at writing different programming languages.