Sunday, 18th February 2024
datasette-studio. I’ve been thinking for a while that it might be interesting to have a version of Datasette that comes bundled with a set of useful plugins, aimed at expanding Datasette’s default functionality to cover things like importing data and editing schemas.
This morning I built the very first experimental preview of what that could look like. Install it using pipx:
pipx install datasette-studio
I recommend pipx because it will ensure datasette-studio gets its own isolated environment, independent of any other Datasette installations you might have.
Now running “datasette-studio” instead of “datasette” will get you the version with the bundled plugins.
The implementation of this is fun—it’s a single pyproject.toml file defining the dependencies and setting up the datasette-studio CLI hook, which is enough to provide the full set of functionality.
Is this a good idea? I don’t know yet, but it’s certainly an interesting initial experiment. # 8:38 pm
Datasette 1.0a10. The only changes in this alpha release concern the way Datasette handles database transactions. The database.execute_write_fn() internal method used to leave functions to implement transactions on their own—it now defaults to wrapping them in a transaction unless they opt out with the new transaction=False parameter.
In implementing this I found several places inside Datasette—in particular parts of the JSON write API—which had not been handling transactions correctly. Those are all now fixed. # 5:10 am
Representation Engineering: Mistral-7B on Acid (via) Theia Vogel provides a delightfully clear explanation (and worked examples) of control vectors—a relatively recent technique for influencing the behaviour of an LLM by applying vectors to the hidden states that are evaluated during model inference.
These vectors are surprisingly easy to both create and apply. Build a small set of contrasting prompt pairs—“Act extremely happy” v.s. “Act extremely sad” for example (with a tiny bit of additional boilerplate), then run a bunch of those prompts and collect the hidden layer states. Then use “single-component PCA” on those states to get a control vector representing the difference.
The examples Theia provides, using control vectors to make Mistral 7B more or less honest, trippy, lazy, creative and more, are very convincing. # 3:49 am
wddbfs – Mount a sqlite database as a filesystem. Ingenious hack from Adam Obeng. Install this Python tool and run it against a SQLite database:
wddbfs --anonymous --db-path path/to/content.db
Then tell the macOS Finder to connect to Go -> Connect to Server -> http://127.0.0.1:8080/ (connect as guest)—connecting via WebDAV.
/Volumes/127.0.0.1/content.db will now be a folder full of CSV, TSV, JSON and JSONL files—one of each format for every table.
This means you can open data from SQLite directly in any application that supports that format, and you can even run CLI commands such as grep, ripgrep or jq directly against the data!
Adam used WebDAV because “Despite how clunky it is, this seems to be the best way to implement a filesystem given that getting FUSE support is not straightforward”. What a neat trick. # 3:31 am