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| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss ↓ | |
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| --- | --- | --- | --- | --- | --- | --- | |
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| [GerbilLab/GerbilBlender-A-32m](https://hf.co/GerbilLab/GerbilBlender-A-32m) | 32m | A-Class | 20 | 640M | 262K | 4.127 | |
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"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: |
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``` |
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'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>' |
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# Example fill in the middle |
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'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>' |
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# Example causal language modelling |
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'<causal> this is an example text for causal language modelling <|endoftext|>' |
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# Example masked language modelling |
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'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>' |
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``` |