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--- |
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language: |
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- en |
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tags: |
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- pytorch |
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- causal-lm |
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- pythia |
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license: apache-2.0 |
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datasets: |
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- Anthropic/hh-rlhf |
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--- |
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[Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) DPO finetuned using original DPO code with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch. |
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Checkpoints are also uploaded. |
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Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/direct-preference-optimization/tree/main) |
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[wandb log](https://wandb.ai/lauraomahony999/pythia-dpo/runs/0mhjakjz) |
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See [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) for model details [(paper)](https://arxiv.org/abs/2101.00027). |
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See further details of these models in the paper [Attributing Mode Collapse in the Fine-Tuning of Large Language Models](https://openreview.net/pdf?id=3pDMYjpOxk). |
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You can cite these models if they are helpful as follows: |
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<pre> |
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@inproceedings{o2024attributing, |
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title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models}, |
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author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella}, |
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booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop}, |
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year={2024} |
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} |
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</pre> |
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hf (pretrained=lomahony/pythia-1b-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16 |
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| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |
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|--------------|------:|------|-----:|---------------|------:|---|------| |
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|arc_challenge | 1|none | 0|acc | 0.2602|± |0.0128| |
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| | |none | 0|acc_norm | 0.2867|± |0.0132| |
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|arc_easy | 1|none | 0|acc | 0.5859|± |0.0101| |
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| | |none | 0|acc_norm | 0.5008|± |0.0103| |
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|boolq | 2|none | 0|acc | 0.6205|± |0.0085| |
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|hellaswag | 1|none | 0|acc | 0.3895|± |0.0049| |
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| | |none | 0|acc_norm | 0.4872|± |0.0050| |
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|lambada_openai| 1|none | 0|perplexity | 6.9417|± |0.2019| |
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| | |none | 0|acc | 0.5550|± |0.0069| |
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|openbookqa | 1|none | 0|acc | 0.2140|± |0.0184| |
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| | |none | 0|acc_norm | 0.3220|± |0.0209| |
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|piqa | 1|none | 0|acc | 0.7193|± |0.0105| |
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| | |none | 0|acc_norm | 0.7008|± |0.0107| |
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|sciq | 1|none | 0|acc | 0.8450|± |0.0115| |
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| | |none | 0|acc_norm | 0.7600|± |0.0135| |
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|wikitext | 2|none | 0|word_perplexity|17.2316|± |N/A | |
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| | |none | 0|byte_perplexity| 1.7029|± |N/A | |
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| | |none | 0|bits_per_byte | 0.7680|± |N/A | |
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|winogrande | 1|none | 0|acc | 0.5367|± |0.0140| |
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hf (pretrained=lomahony/pythia-1b-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16 |
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| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |
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|--------------|------:|------|-----:|---------------|------:|---|------| |
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|arc_challenge | 1|none | 5|acc | 0.2662|± |0.0129| |
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| | |none | 5|acc_norm | 0.3003|± |0.0134| |
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|arc_easy | 1|none | 5|acc | 0.6103|± |0.0100| |
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| | |none | 5|acc_norm | 0.5892|± |0.0101| |
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|boolq | 2|none | 5|acc | 0.6284|± |0.0085| |
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|hellaswag | 1|none | 5|acc | 0.3841|± |0.0049| |
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| | |none | 5|acc_norm | 0.4845|± |0.0050| |
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|lambada_openai| 1|none | 5|perplexity | 9.6301|± |0.2809| |
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| | |none | 5|acc | 0.4865|± |0.0070| |
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|openbookqa | 1|none | 5|acc | 0.2020|± |0.0180| |
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| | |none | 5|acc_norm | 0.3300|± |0.0210| |
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|piqa | 1|none | 5|acc | 0.7122|± |0.0106| |
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| | |none | 5|acc_norm | 0.7046|± |0.0106| |
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|sciq | 1|none | 5|acc | 0.9030|± |0.0094| |
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| | |none | 5|acc_norm | 0.8980|± |0.0096| |
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|wikitext | 2|none | 5|word_perplexity|17.2316|± |N/A | |
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| | |none | 5|byte_perplexity| 1.7029|± |N/A | |
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| | |none | 5|bits_per_byte | 0.7680|± |N/A | |
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|winogrande | 1|none | 5|acc | 0.5296|± |0.0140| |
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