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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

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11–20 of 96 posts

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#11
post #4

From all the experimentation I've done, 7B parameter models just don't seem to be able to produce useful output reliably enough for my use cases. What use cases do people have for these smaller LLM's?

Bert had lots of use cases and this one is supposedly stronger model.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#12
post #7
post #4

From all the experimentation I've done, 7B parameter models just don't seem to be able to produce useful output reliably enough for my use cases. What use cases do people have for these smaller LLM's?

The main use case is that it's probably the only size consumers can run on their personal devices. If you don't want your data going into an external platform like OpenAI it's the only solution even if it's not very usuable.

These are constraints but not a use case.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#14
post #4

From all the experimentation I've done, 7B parameter models just don't seem to be able to produce useful output reliably enough for my use cases. What use cases do people have for these smaller LLM's?

Bert had lots of use cases and this one is supposedly stronger model.

I've used BERT in a number of production apps, it feels like a very apples to oranges comparison given how the AI landscape has changed since BERT's release.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#15
post #14

Earlier quoted context omitted.

Bert had lots of use cases and this one is supposedly stronger model.

I've used BERT in a number of production apps, it feels like a very apples to oranges comparison given how the AI landscape has changed since BERT's release.

Then looks like you know already where smaller and weaker models are useful.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#16
post #7

Earlier quoted context omitted.

The main use case is that it's probably the only size consumers can run on their personal devices. If you don't want your data going into an external platform like OpenAI it's the only solution even if it's not very usuable.

These are constraints but not a use case.

Isn't it obvious they're referring to the use case of using a model given those constraints?

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#17
post #4

From all the experimentation I've done, 7B parameter models just don't seem to be able to produce useful output reliably enough for my use cases. What use cases do people have for these smaller LLM's?

They're only modestly worse than text-davinci-003 in my experience, and you can finetune them cheaply to do simple tasks e.g. triage human input and decide where to send it. But yeah, if you can afford to run a larger model or pay OpenAI for gpt4 calls, that's gonna work a lot better.

If you're relying on prompting for the 7B models IMO you're gonna have a bad time — they're mostly toys at that: interesting output but not consistently useful. But finetuning gets better results, and it's cheap to finetune.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#18
post #5

I have no idea what any of these words mean, but I'd like to. Can someone point me in the direction of an "AI for Dipshits"?

Why not ask the AIs themselves? They are pretty good at explaining this type of thing.

Sounds like a joke but it's true.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#19

Earlier quoted context omitted.

These are constraints but not a use case.

Isn't it obvious they're referring to the use case of using a model given those constraints?

Yes, but I think the responder is wondering if there are useable use cases for that - like, what can you actually Do with that model. I’m in the same boat - I don’t want to ship my data to openai, I do want to run local, so I’d love to hear what other folks are Doing with models of that size.

Re: XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens

#20

Earlier quoted context omitted.

These are constraints but not a use case.

Isn't it obvious they're referring to the use case of using a model given those constraints?

"Use case of using" :)
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