51–60 of 174 posts
I don't understand it. You tell the gpt-4 session a function exists and supply the signature, but I don't see any URLs being defined on where the APIs live? > 1. Call the model with functions and the user’s input > 2. Use the model response to call your API I don't get this part. Where do you tell it where the API lives?
Hm, still don’t see the 32k context in the Playground
Also try
curl -X GET https://api.openai.com/v1/models \ -H "Authorization: Bearer your-api-key"
It is listed there for me.
Is there any open model with a similar context length? [I'm not talking about the dubious for long context fine-tuned LLaMA variants, I mean the real thing.]
Nice! Real life use case of these updates for my autonomous web scraping product: - Bigger context window means less slicing and less calls for generating the web scrapers on the fly - The functions will help to reliably build our data transformation steps (e.g. mapping different sources into the same structure) - Way better unit economics
I would be a happy paying customer if so.
This seems like a direct result of Plugins not hitting PMF -- rather than give API developers access to Plugins, give them the underpinnings to build Plugin-like experiences. Love it!
Seems like I was right to wait for price drop on GPT3.5 a few months ago, was hoping for mostly a drastic drop for GPT4 but I guess 25%(for input tokens, effectively 12.5% on average) on 3.5 works as well
Despite sharing a common prefix, GPT3.5 and GPT4 are completely different models. So, if you were hoping for a price drop on GPT4, the 3.5 drop might not be of any use to you
1. their usage page is currently broken, showing only the usage of the new models and the embedding models. usage for the deprecated models are not included now.
2. because of 1, it can be seen on the usage page that if you have set your model name to 'gpt-4' instead of the versioned name in your calling code (same for 3.5), you have already been using the new models for the past two days!
This seems great, but what does this mean for fine-tuning? Will we be able to fine-tune models prompted with function calls? Should we fine-tune models prompted with function calls? Depending on the complexity of the function-call/text-query pairs I think we may still want to...
GPT3.5 has been undergoing constant improvements, this price decrease (and context length increase) is great news! The main problem I see with people using GPT3.5 is they try and ask it to "write a short story about aliens" and then they get back a crap boring response that sounds like it was written by an AI that was asleep at the wheel. Good creative prompts are long and detailed, and to get the best results you re…
Would you mind sharing what are then good prompt structures? Seems you have a grasp
1. Give lots of examples, you can see in my shared prompt that I include plenty of different examples of things that can happen.
2. The system prompt is important, choose a style you want things written in and provide some context about what the writing will be used for
3. Restrictions create art! My prompt forces GPT to summarize almost every paragraph, which means the things that get written are things that can be summarized with a few emojis.
4. Keep playing with it, use the GPT playground to experiment with different settings.
5. Settings that allow the AI more leeway also result in prompt instructions being ignored, you need to decide where on the scale you are comfortable operating. At one point GPT3.5 was generating (good!) dialogue, which sadly wasn't what I wanted, but I could have chosen to embrace that and go with it.
6. Once you feel a good trend, keep on generating! Occasionally GPT pops out a really good story, maybe 4 or 5 out of the hundreds of stories I've seen have been truly memorable! Ideally I'd be able to prompt engineer to get more of those, but sadly the genre I am writing for (medieval fantasy drama) is right at the edge of ChatGPT's censorship rules.
At one point I actually asked GPT 4 to rewrite my GPT3.5 prompt, and the prompt it came back with resulted in much lower levels of creativity, all the generated text was of the form "A does B, resulting in C", the sentence structure just got really simplified.
Even when asking for summaries, be specific! My summary prompt (not yet pushed to GH sadly) is something like:
"After these instructions I will send you a story. Write a clickbait summary full of drama, limit the summary to 1 sentence and do not spoil the ending."
Compare that to just "summarize the following story."
An example of what output from the crafted prompt may look like:
"When the king of Arcadia fell ill, his children fought to the death to rule the kingdom."
vs the naive prompt:
"King Henry became sick and died. His two sons, John and Tim, fought over who would rule. In the end Tim killed John and became the new king."
This seems great, but what does this mean for fine-tuning? Will we be able to fine-tune models prompted with function calls? Should we fine-tune models prompted with function calls? Depending on the complexity of the function-call/text-query pairs I think we may still want to...