It'll give you a really good introduction to house electrical systems, things like "any panel that is connected directly to the utility is the main panel, any panel that is connected to a panel with a shutoff is a sub panel".
Having spent quite a bit of time playing around with llama.cpp, alpaca.cpp, loras, and the many other llama-based weights lately, here is my impression:
The biggest deal with this isn't the published lora adapter (which seems limited to llama 7b), but the cleaned training data, which is likely better than the previous data sets used to train the alpaca-inspired loras that have been publicly released so far. [0]
If you're really limited to running "just" llama 7b, this is great for you. But the biggest value will be when people inevitably release lora adapters for the 13b, 30b, and 65b, based on this training data (assuming it really is better than the previously released adapters).
[0] admittedly, this is based off anecdotes and github issues, and not real measurements. but smarter people than I have claimed the currently most popular loras were trained on messy data, and have started an effort to clean that data and retrain. So if the training data in this repo is high quality like the authors claim, it will benefit models of all sizes.
Historically, the master server of YTsaurus was a single RSM (replicated state machine) that contained all the meta-information about the cluster. This included the tree of the distributed filesystem, transactions, information about users and tables, placement of chunks, and much more.
However, this approach proved to be non-scalable as the memory amount and throughput of the master server soon became insufficient. To address this issue, we implemented Multicell technology. With Multicell, there are multiple RSMs called secondary masters that store information about chunks of the tables and their placement. The primary master still stores information about the distributed filesystem and transactions but is now single and non-sharded.
After a few years, the masters became overloaded again, and we implemented Portals. With Portals, one can select a subtree of Cypress and place it in one of the secondary masters. This technology is used nowadays, and home directories of some active users are hosted on secondary masters.
However, we anticipate that this approach will also become insufficient in a few years. Therefore, we are currently working on a new technology called Sequoia, which stores information about the Cypress tree shape in horizontally scalable dynamic tables.
It is hard to describe all aspects of master server internals in one comment. Therefore, feel free to join our chat at t.me/ytsaurus for further discussion!
My daughter is in 3rd grade and is bored with her math lessons, so I started to do my own nightly math lessons. Long division was a big hit, but what I really want is to find some accessable number theory material, preferably about interesting patterns. Any recommendations?
Probably something in the 10-15 age range, or 8-10 and originally in Russian
It doesn't need to be.
You can just use a physical interlock and toggle between utility breaker and (any input you want) breaker.
You can do this on an integrated meter panel.
This is a dead-simple configuration that you can comprehend - and verify - with your own eyes.
The lock-out switch is NEC compliant, utility approved, etc.:
https://www.amazon.com/Generator-Interlock-Compatible-Homeli...
Yes, you do lose all power for a second or two but ... so much simpler and comprehensible than an ATX solution.