The first thing I looked for was the number of training tokens. I think yi34 got a lot of benefit from 3 trillion, so this model having 3 trillion bodes well.
The first thing I looked for was the number of training tokens. I think yi34 got a lot of benefit from 3 trillion, so this model having 3 trillion bodes well.
I agree. I have these for yichat34 --top-k 0 --min-p 0.05 --top-p 1.0 --color -t 5 --temp 3 --repeat_penalty 1 -c 4096 -i -n -1
I think the --min-p I have is a bit low, so maybe you have the min-p back to front? Lower is more precise I think.
Orca still memeing strong.
Does it have min-p sampling?
I’m not sure where this chart is from, but I remember it was made before qlora even existed.
I still have this feeling in my gut that closedai have been doing this for a while. It seems like a free lunch.
Seems amazingly good. I might get a real use out of a raspberry pi after all.
Fully open source?
I had nous capy34 get it right a couple of days ago with these settings
Which is interesting. I want to test this new yi chat because apparently it can do decent ASCII?! Need a gguf though.
Somebody wake up Hicks Thebloke
It’s only Wednesday. CZ from Binance will probably be CEO by Friday.
Maybe one day they’ll release the original, genius-tier Orca.
Remember when Orca 1 was supposedly this amazing thing that nobody would ever catch up to? Ambient_temp_xeno remembers.
It seems quite interesting and useful for story writing. The way it works positively invites you to jump in and steer it, if not during generation then afterwards.
Just pick dead celebrities.
EXL2 5.0bpw was surprisingly doing much worse than GGUF Q2_K
Risitas.mov https://www.youtube.com/watch?v=QT13kk8HDDo
To be fair, it’s pretty clear that openai update their models with every kind of test people throw at them as well.
I did the gguf-py/scripts/gguf-set-metadata.py some-yi-model.gguf tokenizer.ggml.bos_token_id 144
and it’s changed the outputs a lot from yesterday.
Apparently the chat version has about 64 for humaneval.