---
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|