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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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/0mhjakjz)
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:
<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-1b-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 | 0.2602|± |0.0128|
| | |none | 0|acc_norm | 0.2867|± |0.0132|
|arc_easy | 1|none | 0|acc | 0.5859|± |0.0101|
| | |none | 0|acc_norm | 0.5008|± |0.0103|
|boolq | 2|none | 0|acc | 0.6205|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.3895|± |0.0049|
| | |none | 0|acc_norm | 0.4872|± |0.0050|
|lambada_openai| 1|none | 0|perplexity | 6.9417|± |0.2019|
| | |none | 0|acc | 0.5550|± |0.0069|
|openbookqa | 1|none | 0|acc | 0.2140|± |0.0184|
| | |none | 0|acc_norm | 0.3220|± |0.0209|
|piqa | 1|none | 0|acc | 0.7193|± |0.0105|
| | |none | 0|acc_norm | 0.7008|± |0.0107|
|sciq | 1|none | 0|acc | 0.8450|± |0.0115|
| | |none | 0|acc_norm | 0.7600|± |0.0135|
|wikitext | 2|none | 0|word_perplexity|17.2316|± |N/A |
| | |none | 0|byte_perplexity| 1.7029|± |N/A |
| | |none | 0|bits_per_byte | 0.7680|± |N/A |
|winogrande | 1|none | 0|acc | 0.5367|± |0.0140|
hf (pretrained=lomahony/pythia-1b-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 | 0.2662|± |0.0129|
| | |none | 5|acc_norm | 0.3003|± |0.0134|
|arc_easy | 1|none | 5|acc | 0.6103|± |0.0100|
| | |none | 5|acc_norm | 0.5892|± |0.0101|
|boolq | 2|none | 5|acc | 0.6284|± |0.0085|
|hellaswag | 1|none | 5|acc | 0.3841|± |0.0049|
| | |none | 5|acc_norm | 0.4845|± |0.0050|
|lambada_openai| 1|none | 5|perplexity | 9.6301|± |0.2809|
| | |none | 5|acc | 0.4865|± |0.0070|
|openbookqa | 1|none | 5|acc | 0.2020|± |0.0180|
| | |none | 5|acc_norm | 0.3300|± |0.0210|
|piqa | 1|none | 5|acc | 0.7122|± |0.0106|
| | |none | 5|acc_norm | 0.7046|± |0.0106|
|sciq | 1|none | 5|acc | 0.9030|± |0.0094|
| | |none | 5|acc_norm | 0.8980|± |0.0096|
|wikitext | 2|none | 5|word_perplexity|17.2316|± |N/A |
| | |none | 5|byte_perplexity| 1.7029|± |N/A |
| | |none | 5|bits_per_byte | 0.7680|± |N/A |
|winogrande | 1|none | 5|acc | 0.5296|± |0.0140|
|