Simon Willison’s Weblog

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Entries tagged llms

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LLM now provides tools for working with embeddings

LLM is my Python library and command-line tool for working with language models. I just released LLM 0.9 with a new set of features that extend LLM to provide tools for working with embeddings.

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Making Large Language Models work for you

I gave an invited keynote at WordCamp 2023 in National Harbor, Maryland on Friday.

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How I make annotated presentations

Giving a talk is a lot of work. I go by a rule of thumb I learned from Damian Conway: a minimum of ten hours of preparation for every one hour spent on stage.

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Catching up on the weird world of LLMs

I gave a talk on Sunday at North Bay Python where I attempted to summarize the last few years of development in the space of LLMs—Large Language Models, the technology behind tools like ChatGPT, Google Bard and Llama 2.

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Run Llama 2 on your own Mac using LLM and Homebrew

Llama 2 is the latest commercially usable openly licensed Large Language Model, released by Meta AI a few weeks ago. I just released a new plugin for my LLM utility that adds support for Llama 2 and many other llama-cpp compatible models.

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Accessing Llama 2 from the command-line with the llm-replicate plugin

The big news today is Llama 2, the new openly licensed Large Language Model from Meta AI. It’s a really big deal:

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Weeknotes: Self-hosted language models with LLM plugins, a new Datasette tutorial, a dozen package releases, a dozen TILs

A lot of stuff to cover from the past two and a half weeks.

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My LLM CLI tool now supports self-hosted language models via plugins

LLM is my command-line utility and Python library for working with large language models such as GPT-4. I just released version 0.5 with a huge new feature: you can now install plugins that add support for additional models to the tool, including models that can run on your own hardware.

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Weeknotes: symbex, LLM prompt templates, a bit of a break

I had a holiday to the UK for a family wedding anniversary and mostly took the time off... except for building symbex, which became one of those projects that kept on inspiring new features.

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Symbex: search Python code for functions and classes, then pipe them into a LLM

I just released a new Python CLI tool called Symbex. It’s a search tool, loosely inspired by ripgrep, which lets you search Python code for functions and classes by name or wildcard, then see just the source code of those matching entities.

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Understanding GPT tokenizers

Large language models such as GPT-3/4, LLaMA and PaLM work in terms of tokens. They take text, convert it into tokens (integers), then predict which tokens should come next.

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Weeknotes: Parquet in Datasette Lite, various talks, more LLM hacking

I’ve fallen a bit behind on my weeknotes. Here’s a catchup for the last few weeks.

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It’s infuriatingly hard to understand how closed models train on their input

One of the most common concerns I see about large language models regards their training data. People are worried that anything they say to ChatGPT could be memorized by it and spat out to other users. People are concerned that anything they store in a private repository on GitHub might be used as training data for future versions of Copilot.

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ChatGPT should include inline tips

In OpenAI isn’t doing enough to make ChatGPT’s limitations clear James Vincent argues that OpenAI’s existing warnings about ChatGPT’s confounding ability to convincingly make stuff up are not effective.

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Lawyer cites fake cases invented by ChatGPT, judge is not amused

Legal Twitter is having tremendous fun right now reviewing the latest documents from the case Mata v. Avianca, Inc. (1:22-cv-01461). Here’s a neat summary:

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llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs

I’ve been building out a small suite of command-line tools for working with ChatGPT, GPT-4 and potentially other language models in the future.

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Delimiters won’t save you from prompt injection

Prompt injection remains an unsolved problem. The best we can do at the moment, disappointingly, is to raise awareness of the issue. As I pointed out last week, “if you don’t understand it, you are doomed to implement it.”

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Leaked Google document: “We Have No Moat, And Neither Does OpenAI”

SemiAnalysis published something of a bombshell leaked document this morning: Google “We Have No Moat, And Neither Does OpenAI”.

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Prompt injection explained, with video, slides, and a transcript

I participated in a webinar this morning about prompt injection, organized by LangChain and hosted by Harrison Chase, with Willem Pienaar, Kojin Oshiba (Robust Intelligence), and Jonathan Cohen and Christopher Parisien (Nvidia Research).

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Let’s be bear or bunny

The Machine Learning Compilation group (MLC) are my favourite team of AI researchers at the moment.

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Enriching data with GPT3.5 and SQLite SQL functions

I shipped openai-to-sqlite 0.3 yesterday with a fun new feature: you can now use the command-line tool to enrich data in a SQLite database by running values through an OpenAI model and saving the results, all in a single SQL query.

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The Dual LLM pattern for building AI assistants that can resist prompt injection

I really want an AI assistant: a Large Language Model powered chatbot that can answer questions and perform actions for me based on access to my private data and tools.

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What’s in the RedPajama-Data-1T LLM training set

RedPajama is “a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens”. It’s a collaboration between Together, Ontocord.ai, ETH DS3Lab, Stanford CRFM, Hazy Research, and MILA Québec AI Institute.

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Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive

A month ago I asked Could you train a ChatGPT-beating model for $85,000 and run it in a browser?. $85,000 was a hypothetical training cost for LLaMA 7B plus Stanford Alpaca. “Run it in a browser” was based on the fact that Web Stable Diffusion runs a 1.9GB Stable Diffusion model in a browser, so maybe it’s not such a big leap to run a small Large Language Model there as well.

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Prompt injection: What’s the worst that can happen?

Activity around building sophisticated applications on top of LLMs (Large Language Models) such as GPT-3/4/ChatGPT/etc is growing like wildfire right now.

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Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha

Today I wanted to understand the performance difference between two Python implementations of a mechanism to detect changes to a SQLite database schema. I rendered the difference between the two as this chart:

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Thoughts on AI safety in this era of increasingly powerful open source LLMs

This morning, VentureBeat published a story by Sharon Goldman: With a wave of new LLMs, open source AI is having a moment — and a red-hot debate. It covers the explosion in activity around openly available Large Language Models such as LLaMA—a trend I’ve been tracking in my own series LLMs on personal devices—and talks about their implications with respect to AI safety.

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The Changelog podcast: LLMs break the internet

I’m the guest on the latest episode of The Changelog podcast: LLMs break the internet. It’s a follow-up to the episode we recorded six months ago about Stable Diffusion.

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We need to tell people ChatGPT will lie to them, not debate linguistics

ChatGPT lies to people. This is a serious bug that has so far resisted all attempts at a fix. We need to prioritize helping people understand this, not debating the most precise terminology to use to describe it.

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Weeknotes: A new llm CLI tool, plus automating my weeknotes and newsletter

I started publishing weeknotes in 2019 partly as a way to hold myself accountable but mainly as a way to encourage myself to write more.

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