Comment by bearjaws
2 years ago
I've been using Mixtral and Bard ever since the end of the year. I am pleased with their performance overall for a mixture of content generation and coding.
It seems to me GPT4 has become short in its outputs, you have to do a lot more COT type prompting to get it to actually output a good result. Which is excruciating given how slow it is to produce content.
Mixtral on together AI is crazy to see ~70-100token/s, and the quality works for my use case as well.
OpenAi it's an unreliable provider. Even if their model don't change as they say, there's a current issue where they blanked added a guardian tool to enforce content policies that are obscure and the tool is over eager, causing quite a stir across startups where this manifests on the surface like an outage.
It will get better as they fix it and tune it, but their entire release pipeline is absolutely bonkers, like no forewarning, no test environment, no opt out. It's scary amateurish for a billion dollar company.
$80bn dollar company *
If we're talking about the API, it seems like it's short because it is shorter. The latest version of GPT-4 (1106) might have a significantly larger input window, but its maximum output token size is limited to 4096 tokens.
It's likely that ChatGPT uses the 1106 model underneath the covers or some variant, so it probably suffers from the same restricted output window.
If you like that speed, you would love Mixtral running at >500 tokens/s @ Groq https://www.youtube.com/watch?v=5fJyOVtOk4Y
In full disclosure, I have worked on getting this up @ Groq.
PS: Experience the speed for yourself, LLama2-70B, at https://chat.groq.com/
Can you give an example of a query where you find GPT4 is short with outputs? I’ve use custom instructions so that may have shielded me from this change.
At least for me making tests has been very frustrating, full of many "test conditions here" and "continue with the rest of the tests".
It _hates_ making assumptions about things it doesn't know for sure, I suspect because of "anti-hallucination" nonsense. Instead it has to be shoved to even try making any assumptions, even reasonable ones.
I know it's capable of making reasonable assumptions for class structures/behaviour, etc where I can just tweak it as needed to work. It just refuses too. I've even seen comments like "We'll put the rest of the code in later"
Yep, with code generation I have definitely encountered this issue as well. It will write out a function description as a comment and move on instead of actually writing out the function a lot for me. It also does this when it has properly written code but you ask it to rewrite to tweak something in my experience.
Given this JSON: <JSON examples> And this Table schema: <Table Schema in SQL>
Create JavaScript to insert the the JSON into the SQL using knex('table_name')
Below is part of its output:
values });
It's missing several columns it could populate with the data it knows from the prompt, primarily created_at, updated_at, account_id, user_id, lesson number... and instead I get a comment telling me to do it.
Theres a lot of people complaining about this, primarily on Reddit, but usually the ChatGPT fan boys jump in to defend OAI.
Try this custom instruction:
3 replies →
Here is the mixtral output (truncated):
knex('course_module')