Simon Willison’s Weblog


11 items tagged “jeremy-howard”


Absolutely any time I try to explore something even slightly against commonly accepted beliefs, LLMs always just rehash the commonly accepted beliefs.

As a researcher, I find this behaviour worse than unhelpful. It gives the mistaken impression that there's nothing to explore.

Jeremy Howard

# 29th June 2024, 10:52 pm / ai, llms, jeremy-howard

fastlite (via) New Python library from Jeremy Howard that adds some neat utility functions and syntactic sugar to my sqlite-utils Python library, specifically for interactive use in Jupyter notebooks.

The autocomplete support through newly exposed dynamic properties is particularly neat, as is the diagram(db.tables) utility for rendering a graphviz diagram showing foreign key relationships between all of the tables.

# 27th May 2024, 9:14 pm / python, sqlite, jupyter, sqlite-utils, jeremy-howard

The creator of a model can not ensure that a model is never used to do something harmful – any more so that the developer of a web browser, calculator, or word processor could. Placing liability on the creators of general purpose tools like these mean that, in practice, such tools can not be created at all, except by big businesses with well funded legal teams.

[...] Instead of regulating the development of AI models, the focus should be on regulating their applications, particularly those that pose high risks to public safety and security. Regulate the use of AI in high-risk areas such as healthcare, criminal justice, and critical infrastructure, where the potential for harm is greatest, would ensure accountability for harmful use, whilst allowing for the continued advancement of AI technology.

Jeremy Howard

# 29th April 2024, 4:04 pm / ethics, ai, generative-ai, llms, jeremy-howard

You can now train a 70b language model at home (via) Jeremy Howard and team: “Today, we’re releasing Answer.AI’s first project: a fully open source system that, for the first time, can efficiently train a 70b large language model on a regular desktop computer with two or more standard gaming GPUs (RTX 3090 or 4090).”

This is about fine-tuning an existing model, not necessarily training one from scratch.

There are two tricks at play here. The first is QLoRA, which can be used to train quantized models despite the reduced precision usually preventing gradient descent from working correctly.

QLoRA can bring the memory requirements for a 70b model down to 35GB, but gaming GPUs aren’t quite that big. The second trick is Meta’s Fully Sharded Data Parallel or FSDP library, which can shard a model across GPUs. Two consumer 24GB GPUs can then handle the 70b training run.

# 8th March 2024, 10:47 am / ai, generative-ai, llms, jeremy-howard

Getting Started With CUDA for Python Programmers (via) if, like me, you’ve avoided CUDA programming (writing efficient code that runs on NVIGIA GPUs) in the past, Jeremy Howard has a new 1hr17m video tutorial that demystifies the basics. The code is all run using PyTorch in notebooks running on Google Colab, and it starts with a very clear demonstration of how to convert a RGB image to black and white.

# 29th January 2024, 9:23 pm / python, ai, pytorch, jeremy-howard


So something everybody I think pretty much agrees on, including Sam Altman, including Yann LeCun, is LLMs aren't going to make it. The current LLMs are not a path to ASI. They're getting more and more expensive, they're getting more and more slow, and the more we use them, the more we realize their limitations.

We're also getting better at taking advantage of them, and they're super cool and helpful, but they appear to be behaving as extremely flexible, fuzzy, compressed search engines, which when you have enough data that's kind of compressed into the weights, turns out to be an amazingly powerful operation to have at your disposal.

[...] And the thing you can really see missing here is this planning piece, right? So if you try to get an LLM to solve fairly simple graph coloring problems or fairly simple stacking problems, things that require backtracking and trying things and stuff, unless it's something pretty similar in its training, they just fail terribly.

[...] So that's the theory about what something like Q* might be, or just in general, how do we get past this current constraint that we have?

Jeremy Howard

# 1st December 2023, 2:49 am / ai, generative-ai, llms, jeremy-howard

A Hackers’ Guide to Language Models. Jeremy Howard’s new 1.5 hour YouTube introduction to language models looks like a really useful place to catch up if you’re an experienced Python programmer looking to start experimenting with LLMs. He covers what they are and how they work, then shows how to build against the OpenAI API, build a Code Interpreter clone using OpenAI functions, run models from Hugging Face on your own machine (with NVIDIA cards or on a Mac) and finishes with a demo of fine-tuning a Llama 2 model to perform text-to-SQL using an open dataset.

# 25th September 2023, 12:24 am / python, ai, openai, generative-ai, llama, llms, jeremy-howard, fine-tuning, nvidia

Mojo may be the biggest programming advance in decades (via) Jeremy Howard makes a very convincing argument for why the new programming language Mojo is a big deal.

Mojo is a superset of Python designed by a team lead by Chris Lattner, who previously created LLVM, Clang and and Swift.

Existing Python code should work unmodified, but it also adds features that enable performant low-level programming—like “fn” for creating typed, compiled functions and “struct” for memory-optimized alternatives to classes.

It’s worth watching Jeremy’s video where he uses these features to get more than a 2000x speed up implementing matrix multiplication, while still keeping the code readable and easy to follow.

Mojo isn’t available yet outside of a playground preview environment, but it does look like an intriguing new project.

# 4th May 2023, 4:41 am / programming-languages, python, ai, mojo, jeremy-howard

From Deep Learning Foundations to Stable Diffusion. Brand new free online video course from Jeremy Howard: 30 hours of content, covering everything you need to know to implement the Stable Diffusion image generation algorithm from scratch. I previewed parts of this course back in December and it was fascinating: this field is moving so fast that some of the lectures covered papers that had been released just a few days before.

# 5th April 2023, 1:13 am / ai, fastai, stable-diffusion, generative-ai, jeremy-howard


Your own hosted blog, the easy, free, open way (even if you’re not a computer expert) (via) Jeremy Howard and the team have released fast_template—a GitHub repository designed to be used as a template to create new repositories with a complete Jekyll blog configured for use with GitHub pages. GitHub’s official document recommends you install Ruby on your machine to do this, but Jeremy points out that with the right repository setup you can run a blog entirely by editing files through the GitHub web interface.

# 17th January 2020, 1:12 am / blogging, github, jeremy-howard


Automatically playing science communication games with transfer learning and fastai

This weekend was the 9th annual Science Hack Day San Francisco, which was also the 100th Science Hack Day held worldwide.

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