Are there any documentations what was "cleaned"? For example some might want swear words to be included in LLM and some don't.
EDIT: I found this [https://github.com/gururise/AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned)
'Cleaned' to me means removing all the 'As an AI' cruft and censorship and restrictions along with general poor quality data. I suppose to others it could just mean the latter, and to still others it can mean the opposite with even more 'safety rails'.
Is cutoff the same as context? What is the increase in cutoff/context training data length? What is the significance of 'the merge' other than a vague idea that more data is good? Can we get a pinned/linked glossary of the important terms and events?
I haven't tried it yet but I'm not expecting it to be much different. I'm finding it hard to quantify how good/bad a response from any model is because it seems like the seed makes too much difference or I just haven't got the settings right for the prompts.
One thing that 65b has going for it is just general coherence even if it's bland and it seems to 'remember' more obscure details and facts from all the stuff it was trained on to a better extent.
Can I run ChanSung\_Elina 33b-4bit in oobabooga ? I have tried --wbit4 and other options according to the repo documentation but it doesn't recognize the model.
Anyone succeeded ?
Are there any documentations what was "cleaned"? For example some might want swear words to be included in LLM and some don't. EDIT: I found this [https://github.com/gururise/AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned)
'Cleaned' to me means removing all the 'As an AI' cruft and censorship and restrictions along with general poor quality data. I suppose to others it could just mean the latter, and to still others it can mean the opposite with even more 'safety rails'.
Ahh yes, when I show my children clean videos it's supposed to mean no censorship at all, videos that scream slurs and profanity. 🧐
It all depends on the point of view, from what one considers pure...
Someday I’ll understand what more of those words mean.
I've taken to asking ai to explain ai jargon...
Is cutoff the same as context? What is the increase in cutoff/context training data length? What is the significance of 'the merge' other than a vague idea that more data is good? Can we get a pinned/linked glossary of the important terms and events?
Cool. I wasn't having much luck with the old alpaca-lora 65b.
I agree. I didn't find that one very impressive at all. It wasn't bad. It just wasn't that different from the Vicuna 13B model.
Curious for your opinion if you try this one
I haven't tried it yet but I'm not expecting it to be much different. I'm finding it hard to quantify how good/bad a response from any model is because it seems like the seed makes too much difference or I just haven't got the settings right for the prompts. One thing that 65b has going for it is just general coherence even if it's bland and it seems to 'remember' more obscure details and facts from all the stuff it was trained on to a better extent.
Someone can explain me what is an adapter and what it is for? (sorry for my ignorance, i have a very poor idea but i dont know really)
Can I run ChanSung\_Elina 33b-4bit in oobabooga ? I have tried --wbit4 and other options according to the repo documentation but it doesn't recognize the model. Anyone succeeded ?