We've granted some exceptions to the team responsible for determining how to stand up something internal. Lots of shooting in the dark going on here, so I figured we would need some divulgence of our IP against public tools to gain traction.
One developer opening a folder in VSCode with Copilot enabled aaaaand it’s gone. You never know what part of the folder left your building.
You use Windows, VSCode, etc, all of this has access to your code.
And Windows and VS Code don't upload your data to Microsoft unless you choose to do so.
There is way more context to our business than can fit in 4/8/32k tokens. Even if we could fit the 32k token budget, it would be very expensive to run like this 24/7. Fine-tuning a base model is the only practical/affordable path for us.
> ChatGPT is now available in Azure OpenAI Service
https://azure.microsoft.com/en-us/blog/chatgpt-is-now-availa...
That's all I use ChatGPT for. I don't need it to be able to write poetry.
./main -m ./models/7B/ggml-model-q4_0.bin -t 4 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --repeat_penalty 1.17647 -n 1024 -p $'Here is a handy short form regex to validate an email address: '
Here is a handy short form regex to validate an email address: ^([a-zA-Z0-9_.+-]+@[a-zA-Z0-9.-]+\.[a-z\.]{2,6})+$ The first character can be either uppercase or lower case. The second group must contain at least one letter and the third group may contain any number of characters (up to 5). The last part ensures that it ends with @ followed by two more letters separated by dots. If you want to make sure that your input string contains only valid characters for emails then use this regex instead: \A[\w.]@[\w.]\.\w{1,4}\z
And the second one is confusing me. It seems to be matching a single character only for the initial portion?
Neither of them seem good, and especially the last one.
And the way it describes both seems off as well. I would have to say it brings more harm than good based on that.
What it emitted accepts a large number of invalid addresses (due to things like not checking dot placement, and the inexplicable (…)+ wrapping around the entire thing), and doesn’t accept a large number of valid addresses (some comparatively esoteric, like local parts containing any of !#$%&'*/=?^`{|}~ or IP addresses for the domain name, and some very reasonable, like TLDs of more than six characters, or internationalised TLDs even in Punycode form).
The description it emits does not match the regular expression at all well, either.
The second regex it emits is even worse than the first, unnecessarily uses PCRE-specific syntax, and is given with a nonsensical description. (Note: the asterisks got turned into italics, backslash-escape them here on HN. With this fixed, the regex was \A[\w.]*@[\w.]*\.\w{1,4}\z.)
> on the very surface scan seems OK
And there’s the danger of this stuff. As a subject-matter expert on regex and email, I glanced at the regular expression and was immediately appalled (… quite apart from the whole “here we go again, this is certain to be terrible” cringe on the prompt). But it looks plausible enough if you aren’t.
email_pattern = r"^(?=.{1,256})(?=.{1,64}@.{1,255}$)(?=\S)(?:(?!@)[\w&'+._%-]+(?:(?<!\\)[,;])?)(?<=\S)@((?=\S)(?!-)[A-Za-z0-9-]{1,63}(?<!-)\.?)+[A-Za-z]{2,19}(?<=\S)$"
Also completely spitballing, I expect that a big chunk of OpenAI's 'secret sauce' is simple processing layers above and beyond the model. If you input gibberish to llama does it give you an output? If OpenAI is artificially tokenizing inputs (as opposed to just sending inputs straight to the software), it would both dramatically limit the input domain, thus improving output tuning, as well as give "it" the ability to say when it doesn't know something. I put "it" in quotes since that response would not becoming from the LLM, but from the preprocess tokenization system returning an error code in natural language.
I think there's some weak indirect evidence for this in the service itself, since incoherent inputs are instantly rejected, whereas even simple queries take dramatically longer to output even the first word. It's like the input is not even being sent to the LLM software for processing.
I've been debating the idea of building tiers or layers of models to accomplish the same.
It very well could be that this go/no-go pre-processor is simply another ML model trained on a binary classification task. Stack a few of these and you can wind up with some interesting programming models.
The problem is the verification of this of course.