Thursday, 15th February 2024
uv: Python packaging in Rust (via) “uv is an extremely fast Python package installer and resolver, written in Rust, and designed as a drop-in replacement for pip and pip-tools workflows.”
From Charlie Marsh and Astral, the team behind Ruff, who describe it as a milestone in their pursuit of a “Cargo for Python”.
Also in this announcement: Astral are taking over stewardship of Armin Ronacher’s Rye packaging tool, another Rust project.
uv is reported to be 8-10x faster than regular pip, increasing to 80-115x faster with a warm global module cache thanks to copy-on-write and hard links on supported filesystems—which saves on disk space too.
It also has a --resolution=lowest option for installing the lowest available version of dependencies—extremely useful for testing, I’ve been wanting this for my own projects for a while.
Also included: “uv venv”—a fast tool for creating new virtual environments with no dependency on Python itself. # 7:57 pm
Val Town Newsletter 15 (via) I really like how Val Town founder Steve Krouse now accompanies their “what’s new” newsletter with a video tour of the new features. I’m seriously considering imitating this for my own projects. # 4:26 pm
Our next-generation model: Gemini 1.5 (via) The big news here is about context length: Gemini 1.5 (a Mixture-of-Experts model) will do 128,000 tokens in general release, available in limited preview with a 1 million token context and has shown promising research results with 10 million tokens!
1 million tokens is 700,000 words or around 7 novels—also described in the blog post as an hour of video or 11 hours of audio. # 4:17 pm
Adaptive Retrieval with Matryoshka Embeddings (via) Nomic Embed v1 only came out two weeks ago, but the same team just released Nomic Embed v1.5 trained using a new technique called Matryoshka Representation.
This means that unlike v1 the v1.5 embeddings are resizable—instead of a fixed 768 dimension embedding vector you can trade size for quality and drop that size all the way down to 64, while still maintaining strong semantically relevant results.
Joshua Lochner build this interactive demo on top of Transformers.js which illustrates quite how well this works: it lets you embed a query, embed a series of potentially matching text sentences and then adjust the number of dimensions and see what impact it has on the results. # 4:19 am