I have fond memories of cs224d [1] taught by richardsocher. It’s a bit dated at this point as it was created in the pre-transformer era, but it was a very cool introduction to applying deep learning to nlp at the time.
Similar thoughts here. That was when I realized the potential of the Internet: I didn't have to be a grad student at a tier 1 research university to learn about the frontier.
Those suggestions they make for a B200 start at $4.99 an hour.
Is that really required, for starting out?
I've been tinkering with my own from-scratch LLM, but in the early phases I don't need anything more than a 4090 on Vast.ai
TA here. Definitely not! In fact we explicitly added sections in the first assignment to allow for scaling down to even local compute (M-series GPUs). For assignment 2 there are a few regions that require Triton support for your GPU, but everything can be adapted for much cheaper GPUs.
We were lucky enough to get Blackwell GPUs for Stanford students this year, which is why the writeups are written mostly around them.
You're right to be sceptical. I have trained reasonably good SLMs for the TinyStories dataset on my 4060Ti (16GB) with no problems. You'll only encounter problems if you want to try if your ideas scale up to models any bigger than "arguably tiny".
Two schools of thought - people are paying 100K per year, we should provide everything. Second is - they are paying 100K per year, do you think they will care for couple of hundred more.
I brought a group together to do this class using the YouTube videos and course materials available online. It is challenging but rewarding. We tackled it one lecture video per week. Started with over 30 learners and by last session we were down to 8.
TA here. Biggest changes are in the second assignment (distributed) where we added a bunch of memory, profiling and distributed tasks, as well as in the fifth assignment (alignment), where most of the RL tasks are fresh this year. Assignment 3 (scaling laws) was also completely updated, but in a way that might be difficult to run without substantial resources. I'm working on a way for external students to be able to run simulated experiments for free!
Assignment 1 (basics) has the most hours of preparation invested in it, and only minor modernization/bug fixes were necessary this year.
i recently started reading "build reasoning model from scratch" then i realized that i am not really interested in building part and just want to understand theory and practice behind it.
A want like a casual lesswrong style from ground up explanation.
Gives you the basics on LLM internals in about 90 minutes and includes an already built model in JavaScript that you can step through in browser devtools to get as detailed as you want.
[1] https://cs224d.stanford.edu
Those suggestions they make for a B200 start at $4.99 an hour.
Is that really required, for starting out? I've been tinkering with my own from-scratch LLM, but in the early phases I don't need anything more than a 4090 on Vast.ai
We were lucky enough to get Blackwell GPUs for Stanford students this year, which is why the writeups are written mostly around them.
Would be great to have a community to discuss the material - even if folks can't commit to the full course.
AI Agent Guidelines for CS336 at Stanford https://github.com/stanford-cs336/assignment1-basics/blob/ma... (https://news.ycombinator.com/item?id=48359232)
Assignment 1 (basics) has the most hours of preparation invested in it, and only minor modernization/bug fixes were necessary this year.
A want like a casual lesswrong style from ground up explanation.
Gives you the basics on LLM internals in about 90 minutes and includes an already built model in JavaScript that you can step through in browser devtools to get as detailed as you want.