82 lines
4.5 KiB
Markdown
82 lines
4.5 KiB
Markdown
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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-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) 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/ydaj2ks8)
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See [Pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) 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-1.4b-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 | 0.2679|± |0.0129|
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| | |none | 0|acc_norm | 0.2978|± |0.0134|
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|arc_easy | 1|none | 0|acc | 0.6120|± |0.0100|
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| | |none | 0|acc_norm | 0.5282|± |0.0102|
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|boolq | 2|none | 0|acc | 0.6260|± |0.0085|
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|hellaswag | 1|none | 0|acc | 0.4097|± |0.0049|
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| | |none | 0|acc_norm | 0.5212|± |0.0050|
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|lambada_openai| 1|none | 0|perplexity | 6.4836|± |0.1838|
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| | |none | 0|acc | 0.5789|± |0.0069|
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|openbookqa | 1|none | 0|acc | 0.2120|± |0.0183|
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| | |none | 0|acc_norm | 0.3340|± |0.0211|
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|piqa | 1|none | 0|acc | 0.7100|± |0.0106|
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| | |none | 0|acc_norm | 0.7144|± |0.0105|
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|sciq | 1|none | 0|acc | 0.8540|± |0.0112|
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| | |none | 0|acc_norm | 0.7830|± |0.0130|
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|wikitext | 2|none | 0|word_perplexity|15.8394|± |N/A |
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| | |none | 0|byte_perplexity| 1.6763|± |N/A |
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| | |none | 0|bits_per_byte | 0.7453|± |N/A |
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|winogrande | 1|none | 0|acc | 0.5872|± |0.0138|
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hf (pretrained=lomahony/pythia-1.4b-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 | 0.2892|± |0.0133|
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| | |none | 5|acc_norm | 0.3097|± |0.0135|
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|arc_easy | 1|none | 5|acc | 0.6444|± |0.0098|
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| | |none | 5|acc_norm | 0.6309|± |0.0099|
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|boolq | 2|none | 5|acc | 0.6333|± |0.0084|
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|hellaswag | 1|none | 5|acc | 0.4065|± |0.0049|
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| | |none | 5|acc_norm | 0.5215|± |0.0050|
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|lambada_openai| 1|none | 5|perplexity | 9.7040|± |0.2887|
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| | |none | 5|acc | 0.4951|± |0.0070|
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|openbookqa | 1|none | 5|acc | 0.2220|± |0.0186|
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| | |none | 5|acc_norm | 0.3100|± |0.0207|
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|piqa | 1|none | 5|acc | 0.7029|± |0.0107|
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| | |none | 5|acc_norm | 0.7127|± |0.0106|
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|sciq | 1|none | 5|acc | 0.9170|± |0.0087|
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| | |none | 5|acc_norm | 0.9160|± |0.0088|
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|wikitext | 2|none | 5|word_perplexity|15.8394|± |N/A |
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| | |none | 5|byte_perplexity| 1.6763|± |N/A |
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| | |none | 5|bits_per_byte | 0.7453|± |N/A |
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|winogrande | 1|none | 5|acc | 0.5699|± |0.0139|
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