title: README
emoji: 🚀
colorFrom: red
colorTo: purple
sdk: static
pinned: false
Who needs em, we all have em, they're just like us. Unusable models, compute optimally 🔥. We hope that by open-sourcing our compute-optimal trained models, that others can replicate our results and also make no use out of our unusable models. These models are not useful in the slightest, and don't benefit research. Every time you use one of these models, you can be sure that you will not get a useful result, and every time we kiss I swear I can fly. Can't you feel my heart beat fast, I want this to last, need you by my side. We introduce a cascade(a) (sorry) of classes and models:
- A-Class Models: 20 x Million Params tokens in training set. (Chinchilla-Optimal)
- B-Class Models: 42 x Million Params tokens in training set.
- C-Class Models: 76 x Million Params tokens in training set.
- D-Class Models: 142 x Million Params tokens in training set.
Evaluations for every Gerbil model can be found here: https://github.com/aicrumb/notebook-hosting/blob/main/GerbilLabEvaluations.md
Special tokens for "Blender" models' pretraining include:
'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>'
# Example fill in the middle
'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>'
# Example causal language modelling
'<causal> this is an example text for causal language modelling <|endoftext|>'
# Example masked language modelling
'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>'