8 items tagged “o1”
OpenAI’s o1 family of models.
2024
Database Remote-Copy Tool For SQLite (draft)
(via)
Neat new SQLite utilities often show up in branches of the SQLite repository. Here's a new one from last month: sqlite3-rsync, providing tools for efficiently creating and updating copies of WAL-mode SQLite databases on either the same machine or across remote machines via SSH.
The way it works is neat, inspired by rsync (hence the tool's name):
The protocol is for the replica to send a cryptographic hash of each of its pages over to the origin side, then the origin sends back the complete content of any page for which the hash does not match.
SQLite's default page size is 4096 bytes and a hash is 20 bytes, so if nothing has changed then the client will transmit 0.5% of the database size in hashes and get nothing back in return.
The tool takes full advantage of SQLite's WAL mode - when you run it you'll get an exact snapshot of the database state as it existed at the moment the copy was initiated, even if the source database continues to apply changes.
I wrote up a TIL on how to compile it - short version:
cd /tmp
git clone https://github.com/sqlite/sqlite.git
cd sqlite
git checkout sqlite3-rsync
./configure
make sqlite3.c
cd tool
gcc -o sqlite3-rsync sqlite3-rsync.c ../sqlite3.c -DSQLITE_ENABLE_DBPAGE_VTAB
./sqlite3-rsync --help
Update: It turns out you can now just run ./configure && make sqlite-rsync in the root checkout.
Something I’ve worried about in the past is that if I want to make a snapshot backup of a SQLite database I need enough additional free disk space to entirely duplicate the current database first (using the backup mechanism or VACUUM INTO). This tool fixes that - I don’t need any extra disk space at all, since the pages that have been updated will be transmitted directly over the wire in 4096 byte chunks.
I tried feeding the 1800 lines of C through OpenAI’s o1-preview with the prompt “Explain the protocol over SSH part of this” and got a pretty great high level explanation - markdown copy here.
Solving a bug with o1-preview, files-to-prompt and LLM.
I added a new feature to DJP this morning: you can now have plugins specify their middleware in terms of how it should be positioned relative to other middleware - inserted directly before or directly after django.middleware.common.CommonMiddleware for example.
At one point I got stuck with a weird test failure, and after ten minutes of head scratching I decided to pipe the entire thing into OpenAI's o1-preview to see if it could spot the problem. I used files-to-prompt to gather the code and LLM to run the prompt:
files-to-prompt **/*.py -c | llm -m o1-preview "
The middleware test is failing showing all of these - why is MiddlewareAfter repeated so many times?
['MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware2', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware4', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware2', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware', 'MiddlewareBefore']"The model whirled away for a few seconds and spat out an explanation of the problem - one of my middleware classes was accidentally calling self.get_response(request) in two different places.
I did enjoy how o1 attempted to reference the relevant Django documentation and then half-repeated, half-hallucinated a quote from it:
This took 2,538 input tokens and 4,354 output tokens - by my calculations at $15/million input and $60/million output that prompt cost just under 30 cents.
o1 prompting is alien to me. Its thinking, gloriously effective at times, is also dreamlike and unamenable to advice.
Just say what you want and pray. Any notes on “how” will be followed with the diligence of a brilliant intern on ketamine.
[… OpenAI’s o1] could work its way to a correct (and well-written) solution if provided a lot of hints and prodding, but did not generate the key conceptual ideas on its own, and did make some non-trivial mistakes. The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, graduate student. However, this was an improvement over previous models, whose capability was closer to an actually incompetent graduate student.
Believe it or not, the name Strawberry does not come from the “How many r’s are in strawberry” meme. We just chose a random word. As far as we know it was a complete coincidence.
— Noam Brown, OpenAI
o1-mini is the most surprising research result I've seen in the past year
Obviously I cannot spill the secret, but a small model getting >60% on AIME math competition is so good that it's hard to believe
— Jason Wei, OpenAI
LLM 0.16.
New release of LLM adding support for the o1-preview and o1-mini OpenAI models that were released today.
Notes on OpenAI’s new o1 chain-of-thought models
OpenAI released two major new preview models today: o1-preview and o1-mini (that mini one is not a preview)—previously rumored as having the codename “strawberry”. There’s a lot to understand about these models—they’re not as simple as the next step up from GPT-4o, instead introducing some major trade-offs in terms of cost and performance in exchange for improved “reasoning” capabilities.