django

joined 1 year ago
[–] [email protected] 1 points 1 year ago

There's a difference between iterating on your code patterns until you get something working and iterating on the solution as requirements evolve. You're only two months in so you still have loads to learn, but eventually you'll come to know all the "best practices" so that when you're presented with a problem, you can immediately know what the ideal solution should look like. The caveat there is that the ideal solution is subject to change as the situation changes.

[–] [email protected] 11 points 1 year ago

This is why it's important to have API access available (for a reasonable price, let alone free). The people who want Twitter data are now loading tons of completely unnecessary and heavy multimedia content just to parse it manually for the comparatively light text data they care about. It would literally save Twitter money to give the data away.

[–] [email protected] 4 points 1 year ago (2 children)

As an experienced programmer, I can say there's nothing inherently wrong with hardcoding data. If you're not presented with clear requirements up front (i.e. all the different use cases your code should handle) then it makes more sense to get something working and refactor as you go. In your case you said the data was subject to change, which doesn't necessarily mean you could abstract it out from the start (unless you were told exactly how it would change). In general, however, software development is all about iteratively refining your assumptions about how things should work while simultaneously juggling the changing demands of stakeholders. One of the most useful rules of thumb is the DRY approach (don't repeat yourself). This is complemented by WET (write exactly twice). Together this means that if you find yourself repeating logic three times, that's enough for you to refactor into a single, generalized function. Repeating twice is generally fine because your third use case might be sufficiently different that a premature refactor is a waste of effort. As you gain more experience programming, you'll find it's less about technical proficiency and more about working efficiently, creating fewer headaches, etc.

[–] [email protected] 6 points 1 year ago (1 children)

Same publisher, different devs. Great game though.

 

Hey, I'm working on some local LLM applications and my goal is to run the smallest model possible without crippling performance. I'm already using 4 bit GPTQ but I want something smaller. These models have been trained on such a massive amount of data but my specific use case only touches a very very small fraction of that, so I would imagine it's possible to cut away large chunks of the model that I don't care about. I'm wondering if there has been any work on runtime pruning of LLMs (not just static pruning based on model weights) based on "real world" data. Something like: you run the model a bunch of times with your actual data and monitor the neuron activations to inform some kind of pruning process. Does anyone here know about something like that?

[–] [email protected] 0 points 1 year ago (1 children)
 

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