--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) supervised finetuned using TRLx library with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch. Checkpoints are also uploaded. Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/trlx-pythia/tree/main) [wandb log](https://wandb.ai/lauraomahony999/sft-pythia/runs/azscanfe) See [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) for model details [(paper)](https://arxiv.org/abs/2101.00027). 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). You can cite these models if they are helpful as follows:
@inproceedings{o2024attributing,
  title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
  author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
  booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
  year={2024}
}
hf (pretrained=lomahony/pythia-1b-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16 | Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |--------------|------:|------|-----:|---------------|------:|---|------| |arc_challenge | 1|none | 0|acc | 0.2543|± |0.0127| | | |none | 0|acc_norm | 0.2739|± |0.0130| |arc_easy | 1|none | 0|acc | 0.5724|± |0.0102| | | |none | 0|acc_norm | 0.4941|± |0.0103| |boolq | 2|none | 0|acc | 0.6199|± |0.0085| |hellaswag | 1|none | 0|acc | 0.3819|± |0.0048| | | |none | 0|acc_norm | 0.4736|± |0.0050| |lambada_openai| 1|none | 0|perplexity | 7.1374|± |0.2014| | | |none | 0|acc | 0.5626|± |0.0069| |openbookqa | 1|none | 0|acc | 0.2040|± |0.0180| | | |none | 0|acc_norm | 0.3140|± |0.0208| |piqa | 1|none | 0|acc | 0.7138|± |0.0105| | | |none | 0|acc_norm | 0.6997|± |0.0107| |sciq | 1|none | 0|acc | 0.8400|± |0.0116| | | |none | 0|acc_norm | 0.7620|± |0.0135| |wikitext | 2|none | 0|word_perplexity|16.9719|± |N/A | | | |none | 0|byte_perplexity| 1.6981|± |N/A | | | |none | 0|bits_per_byte | 0.7639|± |N/A | |winogrande | 1|none | 0|acc | 0.5343|± |0.0140| hf (pretrained=lomahony/pythia-1b-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16 | Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |--------------|------:|------|-----:|---------------|------:|---|------| |arc_challenge | 1|none | 5|acc | 0.2628|± |0.0129| | | |none | 5|acc_norm | 0.2918|± |0.0133| |arc_easy | 1|none | 5|acc | 0.6040|± |0.0100| | | |none | 5|acc_norm | 0.5816|± |0.0101| |boolq | 2|none | 5|acc | 0.5963|± |0.0086| |hellaswag | 1|none | 5|acc | 0.3780|± |0.0048| | | |none | 5|acc_norm | 0.4719|± |0.0050| |lambada_openai| 1|none | 5|perplexity |10.2584|± |0.2936| | | |none | 5|acc | 0.4832|± |0.0070| |openbookqa | 1|none | 5|acc | 0.1980|± |0.0178| | | |none | 5|acc_norm | 0.3220|± |0.0209| |piqa | 1|none | 5|acc | 0.7057|± |0.0106| | | |none | 5|acc_norm | 0.7095|± |0.0106| |sciq | 1|none | 5|acc | 0.8980|± |0.0096| | | |none | 5|acc_norm | 0.9000|± |0.0095| |wikitext | 2|none | 5|word_perplexity|16.9719|± |N/A | | | |none | 5|byte_perplexity| 1.6981|± |N/A | | | |none | 5|bits_per_byte | 0.7639|± |N/A | |winogrande | 1|none | 5|acc | 0.5446|± |0.0140|