228 items tagged “open-source”
2024
The Open Source Sustainability Crisis (via) Chad Whitacre: “What is Open Source sustainability? Why do I say it is in crisis? My answers are that sustainability is when people are getting paid without jumping through hoops, and we’re in a crisis because people aren’t and they’re burning out.”
I really like Chad’s focus on “jumping through hoops” in this piece. It’s possible to build a financially sustainable project today, but it requires picking one or more activities that aren’t directly aligned with working on the core project: raising VC and starting a company, building a hosted SaaS platform and becoming a sysadmin, publishing books and courses and becoming a content author.
The dream is that open source maintainers can invest all of their effort in their projects and make a good living from that work.
We estimate the supply-side value of widely-used OSS is $4.15 billion, but that the demand-side value is much larger at $8.8 trillion. We find that firms would need to spend 3.5 times more on software than they currently do if OSS did not exist. [...] Further, 96% of the demand-side value is created by only 5% of OSS developers.
— The Value of Open Source Software, Harvard Business School Strategy Unit
DSF calls for applicants for a Django Fellow. The Django Software Foundation employs contractors to manage code reviews and releases, responsibly handle security issues, coach new contributors, triage tickets and more.
This is the Django Fellows program, which is now ten years old and has proven enormously impactful.
Mariusz Felisiak is moving on after five years and the DSF are calling for new applicants, open to anywhere in the world.
Talking about Open Source LLMs on Oxide and Friends
I recorded an episode of the Oxide and Friends podcast on Monday, talking with Bryan Cantrill and Adam Leventhal about Open Source LLMs.
[... 1,995 words]Open Source LLMs with Simon Willison. I was invited to the Oxide and Friends weekly audio show (previously on Twitter Spaces, now using broadcast using Discord) to talk about open source LLMs, and to respond to a very poorly considered op-ed calling for them to be regulated as “uniquely dangerous”. It was a really fun conversation, now available to listen to as a podcast or YouTube audio-only video.
Marimo (via) This is a really interesting new twist on Python notebooks.
The most powerful feature is that these notebooks are reactive: if you change the value or code in a cell (or change the value in an input widget) every other cell that depends on that value will update automatically. It’s the same pattern implemented by Observable JavaScript notebooks, but now it works for Python.
There are a bunch of other nice touches too. The notebook file format is a regular Python file, and those files can be run as “applications” in addition to being edited in the notebook interface. The interface is very nicely built, especially for such a young project—they even have GitHub Copilot integration for their CodeMirror cell editors.
Microsoft Research relicense Phi-2 as MIT (via) Phi-2 was already an interesting model—really strong results for its size—made available under a non-commercial research license. It just got significantly more interesting: Microsoft relicensed it as MIT open source.
NPM: modele-social (via) This is a fascinating open source package: it’s an NPM module containing an implementation of the rules for calculating social security contributions in France, maintained by a team at Urssaf, the not-quite-government organization in France that manages the collection of social security contributions there.
The rules themselves can be found in the associated GitHub repository, encoded in a YAML-like declarative language called Publicodes that was developed by the French government for this and similar purposes.
2023
tldraw/draw-a-ui (via) Absolutely spectacular GPT-4 Vision API demo. Sketch out a rough UI prototype using the open source tldraw drawing app, then select a set of components and click “Make Real” (after giving it an OpenAI API key). It generates a PNG snapshot of your selection and sends that to GPT-4 with instructions to turn it into a Tailwind HTML+JavaScript prototype, then adds the result as an iframe next to your mockup.
You can then make changes to your mockup, select it and the previous mockup and click “Make Real” again to ask for an updated version that takes your new changes into account.
This is such a great example of innovation at the UI layer, and everything is open source. Check app/lib/getHtmlFromOpenAI.ts for the system prompt that makes it work.
Financial sustainability for open source projects at GitHub Universe
I presented a ten minute segment at GitHub Universe on Wednesday, ambitiously titled Financial sustainability for open source projects.
[... 2,485 words]YouTube: OpenAssistant is Completed—by Yannic Kilcher (via) The OpenAssistant project was an attempt to crowdsource the creation of an alternative to ChatGPT, using human volunteers to build a Reinforcement Learning from Human Feedback (RLHF) dataset suitable for training this kind of model.
The project started in January. In this video from 24th October project founder Yannic Kilcher announces that the project is now shutting down.
They’ve declared victory in that the dataset they collected has been used by other teams as part of their training efforts, but admit that the overhead of running the infrastructure and moderation teams necessary for their project is more than they can continue to justify.
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.
[... 3,466 words]I like to make sure almost every line of code I write is under a commercially friendly OS license (usually Apache 2) for genuinely selfish reasons: I never want to have to solve that problem ever again, so OS licensing my code now ensures I can use it for the rest of my life no matter who I happen to be working for in the future
— Me
Overnight, tens of thousands of businesses, ranging from one-person shops to the Fortune 500, woke up to a new reality where the underpinnings of their infrastructure suddenly became a potential legal risk. The BUSL and the additional use grant written by the HashiCorp team are vague, and now every company, vendor, and developer using Terraform has to wonder whether what they are doing could be construed as competitive with HashiCorp's offerings.
Databricks Signs Definitive Agreement to Acquire MosaicML, a Leading Generative AI Platform. MosaicML are the team behind MPT-7B and MPT-30B, two of the most impressive openly licensed LLMs. They just got acquired by Databricks for $1.3 billion dollars.
abacaj/mpt-30B-inference. MPT-30B, released last week, is an extremely capable Apache 2 licensed open source language model. This repo shows how it can be run on a CPU, using the ctransformers Python library based on GGML. Following the instructions in the README got me a working MPT-30B model on my M2 MacBook Pro. The model is a 19GB download and it takes a few seconds to start spitting out tokens, but it works as advertised.
Thunderbird Is Thriving: Our 2022 Financial Report (via) Astonishing numbers: in 2022 the Thunderbird project received $6,442,704 in donations from 300,000 users. These donations are now supporting 24 staff members. Part of their success is credited to an “in-app donations appeal” that they launched at the end of 2022.
GitHub code search is generally available. I’ve been a beta user of GitHub’s new code search for a year and a half now and I wouldn’t want to be without it. It’s spectacularly useful: it provides fast, regular-expression-capable search across every public line of code hosted by GitHub—plus code in private repos you have access to.
I mainly use it to compensate for libraries with poor documentation—I can usually find an example of exactly what I want to do somewhere on GitHub.
It’s also great for researching how people are using libraries that I’ve released myself—to figure out how much pain deprecating a method would cause, for example.
Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs (via) There’s a lot to absorb about this one. Mosaic trained this model from scratch on 1 trillion tokens, at a cost of $200,000 taking 9.5 days. It’s Apache-2.0 licensed and the model weights are available today.
They’re accompanying the base model with an instruction-tuned model called MPT-7B-Instruct (licensed for commercial use) and a non-commercially licensed MPT-7B-Chat trained using OpenAI data. They also announced MPT-7B-StoryWriter-65k+—“a model designed to read and write stories with super long context lengths”—with a previously unheard of 65,000 token context length.
They’re releasing these models mainly to demonstrate how inexpensive and powerful their custom model training service is. It’s a very convincing demo!
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”.
[... 1,073 words]Introducing PyPI Organizations. Launched at PyCon US today: Organizations allow packages on the Python Package Index to be owned by a group, not an individual user account. “We’re making organizations available to community projects for free, forever, and to corporate projects for a small fee.”—this is the first revenue generating PyPI feature.
RedPajama, a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens. With the amount of projects that have used LLaMA as a foundation model since its release two months ago—despite its non-commercial license—it’s clear that there is a strong desire for a fully openly licensed alternative.
RedPajama is a collaboration between Together, Ontocord.ai, ETH DS3Lab, Stanford CRFM, Hazy Research, and MILA Québec AI Institute aiming to build exactly that.
Step one is gathering the training data: the LLaMA paper described a 1.2 trillion token training set gathered from sources that included Wikipedia, Common Crawl, GitHub, arXiv, Stack Exchange and more.
RedPajama-Data-1T is an attempt at recreating that training set. It’s now available to download, as 2,084 separate multi-GB jsonl files—2.67TB total.
Even without a trained model, this is a hugely influential contribution to the world of open source LLMs. Any team looking to build their own LLaMA from scratch can now jump straight to the next stage, training the model.
GitHub Accelerator: our first cohort. I’m participating in the first cohort of GitHub’s new open source accelerator program, with Datasette (and related projects). It’s a 10 week program with 20 projects working together “with an end goal of building durable streams of funding for their work”.
Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM (via) Databricks released a large language model called Dolly a few weeks ago. They just released Dolly 2.0 and it is MUCH more interesting—it’s an instruction tuned 12B parameter upgrade of EleutherAI’s Pythia model. Unlike other recent instruction tuned models Databricks didn’t use a training set derived from GPT-3—instead, they recruited 5,000 employees to help put together 15,000 human-generated request/response pairs, which they have released under a Creative Commons Attribution-ShareAlike license. The model itself is a 24GB download from Hugging Face—I’ve run it slowly on a small GPU-enabled Paperspace instance, but hopefully optimized ways to run it will emerge in short order.
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.
[... 781 words]Working in public
I participated in a panel discussion this week for path to Citus Con, a series of Discord audio events that are happening in the run up to the Citus Con 2023 later this month.
[... 546 words]gpt4all. Similar to Alpaca, here’s a project which takes the LLaMA base model and fine-tunes it on instruction examples generated by GPT-3—in this case, it’s 800,000 examples generated using the ChatGPT GPT 3.5 turbo model (Alpaca used 52,000 generated by regular GPT-3). This is currently the easiest way to get a LLaMA derived chatbot running on your own computer: the repo includes compiled binaries for running on M1/M2, Intel Mac, Windows and Linux and provides a link to download the 3.9GB 4-bit quantized model.
Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models (via) The latest example of an open source large language model you can run your own hardware. This one is particularly interesting because the entire thing is under the Apache 2 license. Cerebras are an AI hardware company offering a product with 850,000 cores—this release was trained on their hardware, presumably to demonstrate its capabilities. The model comes in seven sizes from 111 million to 13 billion parameters, and the smaller sizes can be tried directly on Hugging Face.
bloomz.cpp (via) Nouamane Tazi Adapted the llama.cpp project to run against the BLOOM family of language models, which were released in July 2022 and trained in France on 45 natural languages and 12 programming languages using the Jean Zay Public Supercomputer, provided by the French government and powered using mostly nuclear energy.
It’s under the RAIL license which allows (limited) commercial use, unlike LLaMA.
Nouamane reports getting 16 tokens/second from BLOOMZ-7B1 running on an M1 Pro laptop.
Stanford Alpaca, and the acceleration of on-device large language model development
On Saturday 11th March I wrote about how Large language models are having their Stable Diffusion moment. Today is Monday. Let’s look at what’s happened in the past three days.
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