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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-70m](https://huggingface.co/EleutherAI/pythia-70m) 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/3w7e3zmd) |
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See [Pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) 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-70m-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 | 0.1715|± | 0.0110| |
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| | |none | 0|acc_norm | 0.2082|± | 0.0119| |
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|arc_easy | 1|none | 0|acc | 0.3384|± | 0.0097| |
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| | |none | 0|acc_norm | 0.3262|± | 0.0096| |
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|boolq | 2|none | 0|acc | 0.4239|± | 0.0086| |
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|hellaswag | 1|none | 0|acc | 0.2629|± | 0.0044| |
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| | |none | 0|acc_norm | 0.2691|± | 0.0044| |
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|lambada_openai| 1|none | 0|perplexity |5937.7964|± |424.7555| |
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| | |none | 0|acc | 0.0328|± | 0.0025| |
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|openbookqa | 1|none | 0|acc | 0.1580|± | 0.0163| |
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| | |none | 0|acc_norm | 0.2520|± | 0.0194| |
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|piqa | 1|none | 0|acc | 0.5593|± | 0.0116| |
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| | |none | 0|acc_norm | 0.5392|± | 0.0116| |
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|sciq | 1|none | 0|acc | 0.3710|± | 0.0153| |
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| | |none | 0|acc_norm | 0.4990|± | 0.0158| |
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|wikitext | 2|none | 0|word_perplexity| 550.5954|± |N/A | |
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| | |none | 0|byte_perplexity| 3.2550|± |N/A | |
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| | |none | 0|bits_per_byte | 1.7027|± |N/A | |
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|winogrande | 1|none | 0|acc | 0.4878|± | 0.0140| |
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hf (pretrained=lomahony/pythia-70m-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 | 0.1869|± | 0.0114| |
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| | |none | 5|acc_norm | 0.2210|± | 0.0121| |
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|arc_easy | 1|none | 5|acc | 0.3207|± | 0.0096| |
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| | |none | 5|acc_norm | 0.3245|± | 0.0096| |
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|boolq | 2|none | 5|acc | 0.4159|± | 0.0086| |
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|hellaswag | 1|none | 5|acc | 0.2633|± | 0.0044| |
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| | |none | 5|acc_norm | 0.2596|± | 0.0044| |
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|lambada_openai| 1|none | 5|perplexity |19968.0749|± |1423.3001| |
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| | |none | 5|acc | 0.0202|± | 0.0020| |
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|openbookqa | 1|none | 5|acc | 0.1440|± | 0.0157| |
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| | |none | 5|acc_norm | 0.2420|± | 0.0192| |
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|piqa | 1|none | 5|acc | 0.5359|± | 0.0116| |
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| | |none | 5|acc_norm | 0.5229|± | 0.0117| |
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|sciq | 1|none | 5|acc | 0.3240|± | 0.0148| |
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| | |none | 5|acc_norm | 0.4310|± | 0.0157| |
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|wikitext | 2|none | 5|word_perplexity| 550.5954|± |N/A | |
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| | |none | 5|byte_perplexity| 3.2550|± |N/A | |
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| | |none | 5|bits_per_byte | 1.7027|± |N/A | |
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|winogrande | 1|none | 5|acc | 0.5154|± | 0.0140| |
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