gdb

It's really how it works.

One detail you may have missed — "system" is only special when it comes right after a special token. So it's not a special token itself, but you cannot inject a valid-looking system message from user text.

In more detail, the current format is:

<|im_start|>HEADER BODY<|im_end|>

We are actually going to swap over to this shortly:

<|start|>HEADER<|sep|>BODY<|end|>

So basically getting rid of the newline separator and replacing with a special token. Shouldn't change anything fundamentally, but does help with some whitespace tokenization-related issues.

BTW, format of HEADER is going to be really interesting, there's all sorts of metadata one might want to add in there — and making sure that its extensible and not injectable will be an ongoing part of the design work!

Instant Message :). We will drop that prefix in future releases though.

Will all make more sense with upcoming releases, we have a lot of extensions in the works :).

This document is a preview of the underlying format consumed by ChatGPT models. As an API user, today you use our higher-level API (https://platform.openai.com/docs/guides/chat). We'll be opening up direct access to this format in the future, and want to give people visibility into what's going on under the hood in the meanwhile!

Here's how I got started: https://blog.gregbrockman.com/how-i-became-a-machine-learnin...

Thanks for the report — these are not actually messages from other users, but instead the model generating something ~random due to hitting a bug on our backend where, rather than submitting your question, we submitted an empty query to the model.

That's why you see just the answers and no question upon refresh — the question has been effectively dropped for this request. Team is fixing the issue so this doesn't happen in the future!

Comment · 4 years ago

There are many courses available these days; I recommend picking a simple project to start (for me, it was trying to make inference work for GPT-1), learn what you need to in order to get started, and iterate from there.

It’s time to become an ML engineer blog.gregbrockman.com

4 gdb 4 years ago 4 comments

Comment · 5 years ago

We've been ramping up our invites from the waitlist — our Slack community has over 18,000 members — but we still are only a small fraction of way through. We've been really overwhelmed with the demand and have been scaling our team and processes to be able to meet it.

We can also often accelerate invites for people who do have a specific application they'd like to build. Please feel free to email me ([email protected]) and I may be able to help. (As a caveat, I get about a hundred emails a week, so I can't reply to all of them — but know that I will do my best.)

> especially as someone that didn't get a response for my requests for GPT-3 beta access

We are still working our way through the beta list — we've received tens of thousands of applications and we're trying to grow responsibly. We will definitely get to you (and everyone else who applies), but it may take some time.

We are generally prioritizing people with a specific application they'd like to build, if you email me directly ([email protected]) I may be able to accelerate an invite to you.

waves

Given the amount of demand, we're trying to prioritize folks who want to build a concrete application or integrate with a product.

Please feel free to email me ([email protected]) and let me know what you'd like to build — I can't guarantee I'll be able to accelerate an invite, but at the very least I'll make sure we're tracking your use-case internally.

There's just about infinite surface area with the API — we're trying to build a dead-simple API that developers can plug into any product in order to add intelligence features that would be otherwise impossible.

This requires a lot of traditional software work — API design, writing and maintaining a growing amount of business logic, providing great tools and interfaces to help our users work with the API, excellent documentation and tutorials, scaling and operating backend systems, etc — and machine learning systems work — building serving infrastructure for a great variety of giant neural networks while making the most efficient use of our hardware, allowing our users to interact with these neural networks in increasingly sophisticated ways, etc.

While we're just getting started and have a small team, we are already supporting customers across a wide variety of industries (see https://beta.openai.com/ for a sample) and serving millions of requests per day. We are busy trying to invite folks off a very long waitlist while building out the API to support everyone.

Would love more help :).

I am finishing up our fine-tuning API this weekend :).

If anyone on HN would like to try out the fine-tuning API (or want to build something on top of the base API), send me an email ([email protected]) with your use-case and I can try to accelerate you in our invite queue.

PS: We're hiring — if you enjoy building APIs with Python/Go/Kubernetes/Kafka or building front-end interfaces in React, then please get in touch — [email protected].