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Pythia-1b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch (sft-model), before DPO (paper) with same dataset for 1 epoch.

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See Pythia-1b for model details (paper).

Benchmark raw results:

Results for the base model are taken from the Pythia paper.

Zero shot

Task 1B_base 1B_sft 1B_dpo
Lambada (OpenAI) 0.562 ± 0.007 0.563 ± 0.007 0.5575 ± 0.0069
PIQA 0.707 ± 0.011 0.711 ± 0.011 0.7122 ± 0.0106
WinoGrande 0.537 ± 0.014 0.534 ± 0.014 0.5525 ± 0.0140
WSC 0.365 ± 0.047 0.365 ± 0.047 0.3654 ± 0.0474
ARC - Easy 0.569 ± 0.010 0.583 ± 0.010 0.5901 ± 0.0101
ARC - Challenge 0.244 ± 0.013 0.248 ± 0.013 0.2611 ± 0.0128
SciQ 0.840 ± 0.012 0.847 ± 0.011 0.8530 ± 0.0112
LogiQA 0.223 ± 0.016 N/A N/A

Five shot

Task 1B_base 1B_sft 1B_dpo
Lambada (OpenAI) 0.507 ± 0.007 0.4722 ± 0.007 0.4669 ± 0.0070
PIQA 0.705 ± 0.011 0.7165 ± 0.0105 0.7138 ± 0.0105
WinoGrande 0.532 ± 0.014 0.5343 ± 0.014 0.5525 ± 0.0140
WSC 0.365 ± 0.047 0.5000 ± 0.0493 0.5577 ± 0.0489
ARC - Easy 0.594 ± 0.010 0.6010 ± 0.010 0.6170 ± 0.0100
ARC - Challenge 0.259 ± 0.013 0.2679 ± 0.0129 0.2833 ± 0.0132
SciQ 0.920 ± 0.009 0.9100 ± 0.0091 0.9020 ± 0.0094
LogiQA 0.227 ± 0.016 N/A N/A
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Dataset used to train Leogrin/eleuther-pythia1b-hh-dpo