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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) DPO finetuned using original DPO code 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/direct-preference-optimization/tree/main)
[wandb log](https://wandb.ai/lauraomahony999/pythia-dpo/runs/blurtl4v)
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:
<pre>
@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}
}
</pre>
hf (pretrained=lomahony/pythia-2.8b-helpful-dpo), 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 | 0.3157|± |0.0136|
| | |none | 0|acc_norm | 0.3447|± |0.0139|
|arc_easy | 1|none | 0|acc | 0.6591|± |0.0097|
| | |none | 0|acc_norm | 0.6002|± |0.0101|
|boolq | 2|none | 0|acc | 0.6239|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.4671|± |0.0050|
| | |none | 0|acc_norm | 0.6107|± |0.0049|
|lambada_openai| 1|none | 0|perplexity | 4.8811|± |0.1354|
| | |none | 0|acc | 0.6264|± |0.0067|
|openbookqa | 1|none | 0|acc | 0.2820|± |0.0201|
| | |none | 0|acc_norm | 0.4040|± |0.0220|
|piqa | 1|none | 0|acc | 0.7568|± |0.0100|
| | |none | 0|acc_norm | 0.7557|± |0.0100|
|sciq | 1|none | 0|acc | 0.8900|± |0.0099|
| | |none | 0|acc_norm | 0.8340|± |0.0118|
|wikitext | 2|none | 0|word_perplexity|13.9186|± |N/A |
| | |none | 0|byte_perplexity| 1.6363|± |N/A |
| | |none | 0|bits_per_byte | 0.7104|± |N/A |
|winogrande | 1|none | 0|acc | 0.6046|± |0.0137|
hf (pretrained=lomahony/pythia-2.8b-helpful-dpo), 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 | 0.3498|± |0.0139|
| | |none | 5|acc_norm | 0.3823|± |0.0142|
|arc_easy | 1|none | 5|acc | 0.6940|± |0.0095|
| | |none | 5|acc_norm | 0.6940|± |0.0095|
|boolq | 2|none | 5|acc | 0.6440|± |0.0084|
|hellaswag | 1|none | 5|acc | 0.4596|± |0.0050|
| | |none | 5|acc_norm | 0.6096|± |0.0049|
|lambada_openai| 1|none | 5|perplexity | 6.9027|± |0.2030|
| | |none | 5|acc | 0.5614|± |0.0069|
|openbookqa | 1|none | 5|acc | 0.2920|± |0.0204|
| | |none | 5|acc_norm | 0.3820|± |0.0218|
|piqa | 1|none | 5|acc | 0.7601|± |0.0100|
| | |none | 5|acc_norm | 0.7563|± |0.0100|
|sciq | 1|none | 5|acc | 0.9380|± |0.0076|
| | |none | 5|acc_norm | 0.9290|± |0.0081|
|wikitext | 2|none | 5|word_perplexity|13.9186|± |N/A |
| | |none | 5|byte_perplexity| 1.6363|± |N/A |
| | |none | 5|bits_per_byte | 0.7104|± |N/A |
|winogrande | 1|none | 5|acc | 0.6006|± |0.0138|
|