GerbilLab/GerbilBlender-A-32m

Model Name	Parameters	Class	Ratio	Tokens	Batch Size (Tokens)	Training Loss ↓
GerbilLab/GerbilBlender-A-32m	32m	A-Class	20	640M	262K	4.127

"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models 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|>'