--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- [Pythia-2.8b](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. 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/pythia-sft/runs/3b0ltx73) See [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) 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-2.8b-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 | 0.2901|± |0.0133| | | |none | 0|acc_norm | 0.3404|± |0.0138| |arc_easy | 1|none | 0|acc | 0.6469|± |0.0098| | | |none | 0|acc_norm | 0.5766|± |0.0101| |boolq | 2|none | 0|acc | 0.6361|± |0.0084| |hellaswag | 1|none | 0|acc | 0.4557|± |0.0050| | | |none | 0|acc_norm | 0.5984|± |0.0049| |lambada_openai| 1|none | 0|perplexity | 5.2226|± |0.1377| | | |none | 0|acc | 0.6210|± |0.0068| |openbookqa | 1|none | 0|acc | 0.2640|± |0.0197| | | |none | 0|acc_norm | 0.3760|± |0.0217| |piqa | 1|none | 0|acc | 0.7481|± |0.0101| | | |none | 0|acc_norm | 0.7481|± |0.0101| |sciq | 1|none | 0|acc | 0.8800|± |0.0103| | | |none | 0|acc_norm | 0.8180|± |0.0122| |wikitext | 2|none | 0|word_perplexity|13.4928|± |N/A | | | |none | 0|byte_perplexity| 1.6268|± |N/A | | | |none | 0|bits_per_byte | 0.7020|± |N/A | |winogrande | 1|none | 0|acc | 0.6125|± |0.0137| hf (pretrained=lomahony/pythia-2.8b-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 | 0.3285|± |0.0137| | | |none | 5|acc_norm | 0.3677|± |0.0141| |arc_easy | 1|none | 5|acc | 0.6873|± |0.0095| | | |none | 5|acc_norm | 0.6835|± |0.0095| |boolq | 2|none | 5|acc | 0.6670|± |0.0082| |hellaswag | 1|none | 5|acc | 0.4542|± |0.0050| | | |none | 5|acc_norm | 0.5963|± |0.0049| |lambada_openai| 1|none | 5|perplexity | 7.4076|± |0.2095| | | |none | 5|acc | 0.5486|± |0.0069| |openbookqa | 1|none | 5|acc | 0.2680|± |0.0198| | | |none | 5|acc_norm | 0.3620|± |0.0215| |piqa | 1|none | 5|acc | 0.7568|± |0.0100| | | |none | 5|acc_norm | 0.7486|± |0.0101| |sciq | 1|none | 5|acc | 0.9380|± |0.0076| | | |none | 5|acc_norm | 0.9330|± |0.0079| |wikitext | 2|none | 5|word_perplexity|13.4928|± |N/A | | | |none | 5|byte_perplexity| 1.6268|± |N/A | | | |none | 5|bits_per_byte | 0.7020|± |N/A | |winogrande | 1|none | 5|acc | 0.5935|± |0.0138|