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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-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) 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/blurtl4v) |
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See [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) 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-2.8b-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 | 0.3157|± |0.0136| |
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| | |none | 0|acc_norm | 0.3447|± |0.0139| |
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|arc_easy | 1|none | 0|acc | 0.6591|± |0.0097| |
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| | |none | 0|acc_norm | 0.6002|± |0.0101| |
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|boolq | 2|none | 0|acc | 0.6239|± |0.0085| |
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|hellaswag | 1|none | 0|acc | 0.4671|± |0.0050| |
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| | |none | 0|acc_norm | 0.6107|± |0.0049| |
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|lambada_openai| 1|none | 0|perplexity | 4.8811|± |0.1354| |
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| | |none | 0|acc | 0.6264|± |0.0067| |
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|openbookqa | 1|none | 0|acc | 0.2820|± |0.0201| |
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| | |none | 0|acc_norm | 0.4040|± |0.0220| |
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|piqa | 1|none | 0|acc | 0.7568|± |0.0100| |
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| | |none | 0|acc_norm | 0.7557|± |0.0100| |
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|sciq | 1|none | 0|acc | 0.8900|± |0.0099| |
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| | |none | 0|acc_norm | 0.8340|± |0.0118| |
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|wikitext | 2|none | 0|word_perplexity|13.9186|± |N/A | |
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| | |none | 0|byte_perplexity| 1.6363|± |N/A | |
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| | |none | 0|bits_per_byte | 0.7104|± |N/A | |
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|winogrande | 1|none | 0|acc | 0.6046|± |0.0137| |
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hf (pretrained=lomahony/pythia-2.8b-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 | 0.3498|± |0.0139| |
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| | |none | 5|acc_norm | 0.3823|± |0.0142| |
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|arc_easy | 1|none | 5|acc | 0.6940|± |0.0095| |
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| | |none | 5|acc_norm | 0.6940|± |0.0095| |
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|boolq | 2|none | 5|acc | 0.6440|± |0.0084| |
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|hellaswag | 1|none | 5|acc | 0.4596|± |0.0050| |
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| | |none | 5|acc_norm | 0.6096|± |0.0049| |
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|lambada_openai| 1|none | 5|perplexity | 6.9027|± |0.2030| |
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| | |none | 5|acc | 0.5614|± |0.0069| |
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|openbookqa | 1|none | 5|acc | 0.2920|± |0.0204| |
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| | |none | 5|acc_norm | 0.3820|± |0.0218| |
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|piqa | 1|none | 5|acc | 0.7601|± |0.0100| |
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| | |none | 5|acc_norm | 0.7563|± |0.0100| |
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|sciq | 1|none | 5|acc | 0.9380|± |0.0076| |
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| | |none | 5|acc_norm | 0.9290|± |0.0081| |
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|wikitext | 2|none | 5|word_perplexity|13.9186|± |N/A | |
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| | |none | 5|byte_perplexity| 1.6363|± |N/A | |
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| | |none | 5|bits_per_byte | 0.7104|± |N/A | |
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|winogrande | 1|none | 5|acc | 0.6006|± |0.0138| |
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