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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-160m](https://huggingface.co/EleutherAI/pythia-160m) supervised finetuned using TRLx library 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/trlx-pythia/tree/main) |
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[wandb log](https://wandb.ai/lauraomahony999/pythia-sft/runs/9507tygf) |
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See [Pythia-160m](https://huggingface.co/EleutherAI/pythia-410m) 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-160m-helpful-sft), 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 | 0.1894|± | 0.0115| |
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| | |none | 0|acc_norm | 0.2235|± | 0.0122| |
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|arc_easy | 1|none | 0|acc | 0.3889|± | 0.0100| |
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| | |none | 0|acc_norm | 0.3737|± | 0.0099| |
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|boolq | 2|none | 0|acc | 0.5346|± | 0.0087| |
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|hellaswag | 1|none | 0|acc | 0.2801|± | 0.0045| |
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| | |none | 0|acc_norm | 0.2949|± | 0.0046| |
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|lambada_openai| 1|none | 0|perplexity |439.3682|± |23.5771| |
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| | |none | 0|acc | 0.0984|± | 0.0041| |
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|openbookqa | 1|none | 0|acc | 0.1580|± | 0.0163| |
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| | |none | 0|acc_norm | 0.2260|± | 0.0187| |
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|piqa | 1|none | 0|acc | 0.5936|± | 0.0115| |
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| | |none | 0|acc_norm | 0.5865|± | 0.0115| |
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|sciq | 1|none | 0|acc | 0.5710|± | 0.0157| |
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| | |none | 0|acc_norm | 0.6290|± | 0.0153| |
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|wikitext | 2|none | 0|word_perplexity| 87.3261|± |N/A | |
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| | |none | 0|byte_perplexity| 2.3068|± |N/A | |
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| | |none | 0|bits_per_byte | 1.2059|± |N/A | |
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|winogrande | 1|none | 0|acc | 0.4878|± | 0.0140| |
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hf (pretrained=lomahony/pythia-160m-helpful-sft), 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 | 0.2022|± | 0.0117| |
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| | |none | 5|acc_norm | 0.2270|± | 0.0122| |
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|arc_easy | 1|none | 5|acc | 0.3733|± | 0.0099| |
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| | |none | 5|acc_norm | 0.3746|± | 0.0099| |
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|boolq | 2|none | 5|acc | 0.5413|± | 0.0087| |
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|hellaswag | 1|none | 5|acc | 0.2770|± | 0.0045| |
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| | |none | 5|acc_norm | 0.2853|± | 0.0045| |
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|lambada_openai| 1|none | 5|perplexity |1644.8526|± |87.8870| |
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| | |none | 5|acc | 0.0491|± | 0.0030| |
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|openbookqa | 1|none | 5|acc | 0.1400|± | 0.0155| |
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| | |none | 5|acc_norm | 0.2200|± | 0.0185| |
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|piqa | 1|none | 5|acc | 0.5892|± | 0.0115| |
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| | |none | 5|acc_norm | 0.5854|± | 0.0115| |
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|sciq | 1|none | 5|acc | 0.5100|± | 0.0158| |
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| | |none | 5|acc_norm | 0.6020|± | 0.0155| |
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|wikitext | 2|none | 5|word_perplexity| 87.3261|± |N/A | |
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| | |none | 5|byte_perplexity| 2.3068|± |N/A | |
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| | |none | 5|bits_per_byte | 1.2059|± |N/A | |
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|winogrande | 1|none | 5|acc | 0.5178|± | 0.0140| |
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