61–70 of 174 posts
Finally! I’ve been getting the shakes waiting for next OpenAI release. 16k context with 3.5-turbo is huge. It’ll make all those dime a dozen document driven assistants a lot more useful. I’m curious to see if people will figure out ways to hack functions to get more reliable structured JSON data out out of GPT without tons of examples, giving lots more context room to play with
Anyone know how they pick who to invite off the waitlist for GPT 4? I've been on there for a while. My project is open source and I wonder if that is getting me deprioritized.
I hate seeing these guys succeed because everyone of their successes is a new day that AI becomes less accessible to the average person and more locked behind their APIs.
I see this statement a lot and have no idea how people come to this conclusion. I have a beefy 16k$ workstation with 2 4090s and I could barely run the LLAMA 65B model at a very slow pace. Let us say we do have the model weights to GPT-4 and GPT3.5, me as the average consumer I don't know how this helps me in any way. I need to shell at least 25k (possibly much more for GPT-4) before I can run these models for even i…
Anyone know how they pick who to invite off the waitlist for GPT 4? I've been on there for a while. My project is open source and I wonder if that is getting me deprioritized.
Although from the blog post it looks like they're planning to open up to everyone soon, so that may happen before you get through the evals backlog.
I hate seeing these guys succeed because everyone of their successes is a new day that AI becomes less accessible to the average person and more locked behind their APIs.
I see this statement a lot and have no idea how people come to this conclusion. I have a beefy 16k$ workstation with 2 4090s and I could barely run the LLAMA 65B model at a very slow pace. Let us say we do have the model weights to GPT-4 and GPT3.5, me as the average consumer I don't know how this helps me in any way. I need to shell at least 25k (possibly much more for GPT-4) before I can run these models for even i…
Give it a decade and you might be able to, but without the model you'll never have the option.
Earlier quoted context omitted.
An "average" person is not someone who knows how to call an API. Perhaps only on HN
If they don't know how to call an API, they won't know how to run local models (at the moment it's quite a pain to set-up all the dependencies)
I hate seeing these guys succeed because everyone of their successes is a new day that AI becomes less accessible to the average person and more locked behind their APIs.
- The API is extremely cheap - There are plenty of open source tools built on top if it (example list: https://github.com/heartly/awesome-writing-tools ) While I wish this work was open, they are both the best and cheapest option out there... by a mile.
So far, gpt-4 has been significantly better at editing code than gpt-3.5-turbo. This is for two reasons (which I previously discussed here [1]):
1. GPT-4's bigger context window lets it understand and edit larger codebases. The new 16k window for 3.5 might solve this problem.
2. GPT-4 is much better at following instructions about how to format code edits into a diff-like output format. Perhaps the improved instruction following will help 3.5 succeed here.
Essentially, 3.5 isn't capable of outputting any sort of diff-based edit format. The only thing it can reliably do is send back *all* of the code (say an entire source file) with the changes included. On the other hand, GPT-4 is capable of reliably outputting (some) diff-like formats.
So I'm very curious to see if the new 3.5 model solves both of these problems. I'll be running some benchmarks to find out!
[1] https://github.com/paul-gauthier/aider#gpt-4-vs-gpt-35
EDIT: On point (2) above... early experiments with gpt-3.5-turbo-16k seem to indicate that it is NOT able to follow system prompt instructions to use a diff-like output format. Still need to try the new functions capability.
LLMs orchestrating, pulling data from, and coordinating between existing systems in this manner seems very powerful. I feel like we haven't even really seen many of the possibilities there.
Earlier quoted context omitted.
You don't. You take the model response and you call your API.
Ah, I missed this part. ChatGPT response after you tell it a function call exists, it can decide whether or not to call it. { "id": "chatcmpl-123", ... "choices": [{ "index": 0, "message": { "role": "assistant", "content": null, "function_call": { "name": "get_current_weather", "arguments": "{ \"location\": \"Boston, MA\"}" } }, "finish_reason": "function_call" }] } Gotcha. This makes it so that instead of English, g…
What is returned from openai should be treated like any other user input.