---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---

[Pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) 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/cn14yuod)

See [Pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) 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-1.4b-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            | 0.2816|±  |0.0131|
|              |       |none  |     0|acc_norm       | 0.3123|±  |0.0135|
|arc_easy      |      1|none  |     0|acc            | 0.6229|±  |0.0099|
|              |       |none  |     0|acc_norm       | 0.5459|±  |0.0102|
|boolq         |      2|none  |     0|acc            | 0.6229|±  |0.0085|
|hellaswag     |      1|none  |     0|acc            | 0.4191|±  |0.0049|
|              |       |none  |     0|acc_norm       | 0.5383|±  |0.0050|
|lambada_openai|      1|none  |     0|perplexity     | 6.4790|±  |0.1947|
|              |       |none  |     0|acc            | 0.5674|±  |0.0069|
|openbookqa    |      1|none  |     0|acc            | 0.2280|±  |0.0188|
|              |       |none  |     0|acc_norm       | 0.3360|±  |0.0211|
|piqa          |      1|none  |     0|acc            | 0.7122|±  |0.0106|
|              |       |none  |     0|acc_norm       | 0.7214|±  |0.0105|
|sciq          |      1|none  |     0|acc            | 0.8480|±  |0.0114|
|              |       |none  |     0|acc_norm       | 0.7840|±  |0.0130|
|wikitext      |      2|none  |     0|word_perplexity|16.4022|±  |N/A   |
|              |       |none  |     0|byte_perplexity| 1.6873|±  |N/A   |
|              |       |none  |     0|bits_per_byte  | 0.7547|±  |N/A   |
|winogrande    |      1|none  |     0|acc            | 0.5959|±  |0.0138|

hf (pretrained=lomahony/pythia-1.4b-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            | 0.3089|±  |0.0135|
|              |       |none  |     5|acc_norm       | 0.3353|±  |0.0138|
|arc_easy      |      1|none  |     5|acc            | 0.6423|±  |0.0098|
|              |       |none  |     5|acc_norm       | 0.6334|±  |0.0099|
|boolq         |      2|none  |     5|acc            | 0.6291|±  |0.0084|
|hellaswag     |      1|none  |     5|acc            | 0.4124|±  |0.0049|
|              |       |none  |     5|acc_norm       | 0.5347|±  |0.0050|
|lambada_openai|      1|none  |     5|perplexity     | 9.7688|±  |0.3083|
|              |       |none  |     5|acc            | 0.4904|±  |0.0070|
|openbookqa    |      1|none  |     5|acc            | 0.2260|±  |0.0187|
|              |       |none  |     5|acc_norm       | 0.3240|±  |0.0210|
|piqa          |      1|none  |     5|acc            | 0.7095|±  |0.0106|
|              |       |none  |     5|acc_norm       | 0.7165|±  |0.0105|
|sciq          |      1|none  |     5|acc            | 0.9140|±  |0.0089|
|              |       |none  |     5|acc_norm       | 0.9050|±  |0.0093|
|wikitext      |      2|none  |     5|word_perplexity|16.4022|±  |N/A   |
|              |       |none  |     5|byte_perplexity| 1.6873|±  |N/A   |
|              |       |none  |     5|bits_per_byte  | 0.7547|±  |N/A   |
|winogrande    |      1|none  |     5|acc            | 0.5612|±  |0.0139|