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SantaCoder: A new 1.1B code model for generation and infilling

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Re: SantaCoder: A new 1.1B code model for generation and infilling

#71

Post by notwokeno »

Uptrenda wrote: post #69

As a software engineer what is the use-case for these kind of 'code generation' tools? Are they good enough to generate different scripts for OS tasks? Can they automate CRUD APIs? What level of detail is required to use them? Like, do I basically have to describe an algorithm in English or can I go up to a higher level and talk about features and what the software ought to do? Are these tools good enough to improve…

I've been using chatgpt with my side projects. Its ability to generate boilerplate for APIs just from what would normally be a google search prompt means you can often go straight from idea to the part where you're adding the interesting features for your app.

Its ability to generate what are essentially highly specialized tutorials that match exactly your use cases is also a really big deal.

Overall it's really extended what I'm capable of doing. Not because I couldn't do the things before but because I can skip over the boring part in the beginning and save my emotional energy for the part that actually matters.

>do I basically have to describe an algorithm in English or can I go up to a higher level and talk about features and what the software ought to do?

It understands any "well known" algorithm, api, paradigm or pattern that was written about before 2022. Even pretty obscure stuff. One thing I tried was copy and pasting some of my code into it and having it generate unit tests.

Re: SantaCoder: A new 1.1B code model for generation and infilling

#72

Post by hooande »

aunch wrote: post #12
ilaksh wrote: post #11

Is anyone else here building AI programming services based on models like this? I see a lot of comments saying the models can't do much programming. But I just suspect there must be a silent contingent that is also working on services like that. And maybe less likely to promote the abilities of these models because it encourages competition.

We are at Codeium (codeium.com)! Not the SantaCoder model specifically, but the same types of LLM architectures. We've started with AI-based code autocomplete, but we think there is a lot more we can do. We wrote up some of our learnings so far in @swyx's blog recently: https://lspace.swyx.io/p/what-building-copilot-for-x-really

my unsolicited advice: pick an X. what is the one best use case for this other than code? law? finance? focus on that vertical. if you have no idea what that could be or if that market is too small, you're already in trouble.

I don't use anticomplete at all. What I would like is something that can take my current, bad code and style transfer it into proper, modern code. best case, take code as I write it naturally and confirm it to the style guide of my organization.

Re: SantaCoder: A new 1.1B code model for generation and infilling

#74

Post by PurpleRamen »

Quite unimpressive, so far.

Only works somewhat well for very simple tasks and well known tasks. Any mildly more complex and it fails. It also seems to have no understanding of imports. It's barfing out a dozen of oneline-functions for common tasks, which all are just a call to some lib-function, of which half are not even in the standard-python-library.

Also kinda strange that at some point it drifts away from the demanded task, or just ends on unfinished code if the token-number is too small. For example, I asked some code relating xml-parsing and handling, and after some xml-functions, it's moving to json and yaml.

I guess with some optimization and integration, there might be some benefit for this, to replace the common stack overflow-copy n'paste. But I don't see this yet at adding significant value to actual work.

Re: SantaCoder: A new 1.1B code model for generation and infilling

#75

Post by Tepix »

Jap2-0 wrote: post #51
Jap2-0 wrote: post #50

Earlier quoted context omitted.

Mine never defines is_prime(): def all_prime_elements(sequence): """Returns every prime element of the sequence.""" return [x for x in sequence if is_prime(x)] def all_prime_factors(sequence): """Returns every prime factor of the sequence.""" return [x for x in sequence if is_prime(x) and x!= 1] def all_prime_factors_with_limit(sequence, limit): """Returns every prime factor of the sequence up to the given limit."""…

If I prompt it, it actually comes up with a decent function: def is_prime(element): """Returns whether a number is prime.""" if element Of course, if you ask it to keep generating past that there's the usual slow descent into madness.

I got something similar:

    def is_prime(n):
      """ Use sieve of erasthotenes to check if n is prime. """
      if n

Re: SantaCoder: A new 1.1B code model for generation and infilling

#76

Post by stevenhuang »

wokwokwok wrote: post #68
stevenhuang wrote: post #52

Earlier quoted context omitted.

> so generally speaking 'generate an entirely application' one-step models currently don't exist. This already exists. People have created full apps in ChatGPT by doing this. Many examples online.

No, they haven’t. ChatGPT has the same prompt history limitations as other models, and cannot generate entire end to end applications. What you’ve seen is applications running on top of chatGPT and using it iteratively to generate multiple code segments.

But the context window size of ChatGPT is sufficient.

And again it does generate a full application, just not all of it at once.

Just see https://news.ycombinator.com/item?id=33854638

Sure you can't prompt it once and download a zip, but you still get a consistent full app at the end that can be used as a base from this prompting.

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