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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) 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/3w7e3zmd)
See [Pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) 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-70m-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 | 0.1715|± | 0.0110|
| | |none | 0|acc_norm | 0.2082|± | 0.0119|
|arc_easy | 1|none | 0|acc | 0.3384|± | 0.0097|
| | |none | 0|acc_norm | 0.3262|± | 0.0096|
|boolq | 2|none | 0|acc | 0.4239|± | 0.0086|
|hellaswag | 1|none | 0|acc | 0.2629|± | 0.0044|
| | |none | 0|acc_norm | 0.2691|± | 0.0044|
|lambada_openai| 1|none | 0|perplexity |5937.7964|± |424.7555|
| | |none | 0|acc | 0.0328|± | 0.0025|
|openbookqa | 1|none | 0|acc | 0.1580|± | 0.0163|
| | |none | 0|acc_norm | 0.2520|± | 0.0194|
|piqa | 1|none | 0|acc | 0.5593|± | 0.0116|
| | |none | 0|acc_norm | 0.5392|± | 0.0116|
|sciq | 1|none | 0|acc | 0.3710|± | 0.0153|
| | |none | 0|acc_norm | 0.4990|± | 0.0158|
|wikitext | 2|none | 0|word_perplexity| 550.5954|± |N/A |
| | |none | 0|byte_perplexity| 3.2550|± |N/A |
| | |none | 0|bits_per_byte | 1.7027|± |N/A |
|winogrande | 1|none | 0|acc | 0.4878|± | 0.0140|
hf (pretrained=lomahony/pythia-70m-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 | 0.1869|± | 0.0114|
| | |none | 5|acc_norm | 0.2210|± | 0.0121|
|arc_easy | 1|none | 5|acc | 0.3207|± | 0.0096|
| | |none | 5|acc_norm | 0.3245|± | 0.0096|
|boolq | 2|none | 5|acc | 0.4159|± | 0.0086|
|hellaswag | 1|none | 5|acc | 0.2633|± | 0.0044|
| | |none | 5|acc_norm | 0.2596|± | 0.0044|
|lambada_openai| 1|none | 5|perplexity |19968.0749|± |1423.3001|
| | |none | 5|acc | 0.0202|± | 0.0020|
|openbookqa | 1|none | 5|acc | 0.1440|± | 0.0157|
| | |none | 5|acc_norm | 0.2420|± | 0.0192|
|piqa | 1|none | 5|acc | 0.5359|± | 0.0116|
| | |none | 5|acc_norm | 0.5229|± | 0.0117|
|sciq | 1|none | 5|acc | 0.3240|± | 0.0148|
| | |none | 5|acc_norm | 0.4310|± | 0.0157|
|wikitext | 2|none | 5|word_perplexity| 550.5954|± |N/A |
| | |none | 5|byte_perplexity| 3.2550|± |N/A |
| | |none | 5|bits_per_byte | 1.7027|± |N/A |
|winogrande | 1|none | 5|acc | 0.5154|± | 0.0140|
|