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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-410m](https://huggingface.co/EleutherAI/pythia-410m) 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/quq2097z) |
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See [Pythia-410m](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-410m-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 | 0.2355|± |0.0124| |
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| | |none | 0|acc_norm | 0.2594|± |0.0128| |
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|arc_easy | 1|none | 0|acc | 0.5051|± |0.0103| |
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| | |none | 0|acc_norm | 0.4478|± |0.0102| |
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|boolq | 2|none | 0|acc | 0.6113|± |0.0085| |
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|hellaswag | 1|none | 0|acc | 0.3372|± |0.0047| |
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| | |none | 0|acc_norm | 0.4001|± |0.0049| |
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|lambada_openai| 1|none | 0|perplexity |21.8172|± |0.7736| |
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| | |none | 0|acc | 0.3755|± |0.0067| |
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|openbookqa | 1|none | 0|acc | 0.1940|± |0.0177| |
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| | |none | 0|acc_norm | 0.2960|± |0.0204| |
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|piqa | 1|none | 0|acc | 0.6719|± |0.0110| |
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| | |none | 0|acc_norm | 0.6687|± |0.0110| |
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|sciq | 1|none | 0|acc | 0.7700|± |0.0133| |
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| | |none | 0|acc_norm | 0.6540|± |0.0151| |
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|wikitext | 2|none | 0|word_perplexity|23.8136|± |N/A | |
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| | |none | 0|byte_perplexity| 1.8091|± |N/A | |
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| | |none | 0|bits_per_byte | 0.8553|± |N/A | |
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|winogrande | 1|none | 0|acc | 0.5320|± |0.0140| |
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hf (pretrained=lomahony/pythia-410m-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 | 0.2355|± |0.0124| |
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| | |none | 5|acc_norm | 0.2790|± |0.0131| |
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|arc_easy | 1|none | 5|acc | 0.5274|± |0.0102| |
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| | |none | 5|acc_norm | 0.5072|± |0.0103| |
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|boolq | 2|none | 5|acc | 0.5226|± |0.0087| |
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|hellaswag | 1|none | 5|acc | 0.3367|± |0.0047| |
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| | |none | 5|acc_norm | 0.3991|± |0.0049| |
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|lambada_openai| 1|none | 5|perplexity |37.4791|± |1.3737| |
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| | |none | 5|acc | 0.3049|± |0.0064| |
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|openbookqa | 1|none | 5|acc | 0.1620|± |0.0165| |
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| | |none | 5|acc_norm | 0.2900|± |0.0203| |
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|piqa | 1|none | 5|acc | 0.6708|± |0.0110| |
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| | |none | 5|acc_norm | 0.6676|± |0.0110| |
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|sciq | 1|none | 5|acc | 0.8630|± |0.0109| |
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| | |none | 5|acc_norm | 0.8430|± |0.0115| |
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|wikitext | 2|none | 5|word_perplexity|23.8136|± |N/A | |
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| | |none | 5|byte_perplexity| 1.8091|± |N/A | |
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| | |none | 5|bits_per_byte | 0.8553|± |N/A | |
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|winogrande | 1|none | 5|acc | 0.5272|± |0.0140| |
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