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lomahony/pythia-410m-helpful-dpo
pythia-410m-helpful-dpo is a text generation model from lomahony. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Pythia-410m DPO finetuned using original DPO code with the helpful subset of Anthropic-hh-rlhf dataset for 1 epoch.
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From the Hugging Face model README
Pythia-410m DPO finetuned using original DPO code with the helpful subset of Anthropic-hh-rlhf dataset for 1 epoch.
Checkpoints are also uploaded.
Fully reproducible finetuning code is available on GitHub
See Pythia-410m for model details (paper).
See further details of these models in the paper Attributing Mode Collapse in the Fine-Tuning of Large Language Models.
You can cite these models if they are helpful as follows:
<pre> @inproceedings{o2024attributing, title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models}, author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella}, booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop}, year={2024} } </pre>hf (pretrained=lomahony/pythia-410m-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| arc_challenge | 1 | none | 0 | acc | 0.2338 | ± | 0.0124 |
| none | 0 | acc_norm | 0.2602 | ± | 0.0128 | ||
| arc_easy | 1 | none | 0 | acc | 0.5185 | ± | 0.0103 |
| none | 0 | acc_norm | 0.4609 | ± | 0.0102 | ||
| boolq | 2 | none | 0 | acc | 0.6214 | ± | 0.0085 |
| hellaswag | 1 | none | 0 | acc | 0.3447 | ± | 0.0047 |
| none | 0 | acc_norm | 0.4074 | ± | 0.0049 | ||
| lambada_openai | 1 | none | 0 | perplexity | 19.0431 | ± | 0.7027 |
| none | 0 | acc | 0.3978 | ± | 0.0068 | ||
| openbookqa | 1 | none | 0 | acc | 0.2000 | ± | 0.0179 |
| none | 0 | acc_norm | 0.3100 | ± | 0.0207 | ||
| piqa | 1 | none | 0 | acc | 0.6779 | ± | 0.0109 |
| none | 0 | acc_norm | 0.6757 | ± | 0.0109 | ||
| sciq | 1 | none | 0 | acc | 0.7760 | ± | 0.0132 |
| none | 0 | acc_norm | 0.6690 | ± | 0.0149 | ||
| wikitext | 2 | none | 0 | word_perplexity | 24.3807 | ± | N/A |
| none | 0 | byte_perplexity | 1.8171 | ± | N/A | ||
| none | 0 | bits_per_byte | 0.8617 | ± | N/A | ||
| winogrande | 1 | none | 0 | acc | 0.5343 | ± | 0.0140 |
hf (pretrained=lomahony/pythia-410m-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| arc_challenge | 1 | none | 5 | acc | 0.2346 | ± | 0.0124 |
| none | 5 | acc_norm | 0.2747 | ± | 0.0130 | ||
| arc_easy | 1 | none | 5 | acc | 0.5509 | ± | 0.0102 |
| none | 5 | acc_norm | 0.5198 | ± | 0.0103 | ||
| boolq | 2 | none | 5 | acc | 0.5982 | ± | 0.0086 |
| hellaswag | 1 | none | 5 | acc | 0.3437 | ± | 0.0047 |
| none | 5 | acc_norm | 0.4059 | ± | 0.0049 | ||
| lambada_openai | 1 | none | 5 | perplexity | 34.3002 | ± | 1.3044 |
| none | 5 | acc | 0.3148 | ± | 0.0065 | ||
| openbookqa | 1 | none | 5 | acc | 0.1740 | ± | 0.0170 |
| none | 5 | acc_norm | 0.2880 | ± | 0.0203 | ||
| piqa | 1 | none | 5 | acc | 0.6741 | ± | 0.0109 |
| none | 5 | acc_norm | 0.6670 | ± | 0.0110 | ||
| sciq | 1 | none | 5 | acc | 0.8520 | ± | 0.0112 |
| none | 5 | acc_norm | 0.8350 | ± | 0.0117 | ||
| wikitext | 2 | none | 5 | word_perplexity | 24.3807 | ± | N/A |
| none | 5 | byte_perplexity | 1.8171 | ± | N/A | ||
| none | 5 | bits_per_byte | 0.8617 | ± | N/A | ||
| winogrande | 1 | none | 5 | acc | 0.5162 | ± | 0.0140 |