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---
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) 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/ydaj2ks8)
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-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 | 0.2679|± |0.0129|
| | |none | 0|acc_norm | 0.2978|± |0.0134|
|arc_easy | 1|none | 0|acc | 0.6120|± |0.0100|
| | |none | 0|acc_norm | 0.5282|± |0.0102|
|boolq | 2|none | 0|acc | 0.6260|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.4097|± |0.0049|
| | |none | 0|acc_norm | 0.5212|± |0.0050|
|lambada_openai| 1|none | 0|perplexity | 6.4836|± |0.1838|
| | |none | 0|acc | 0.5789|± |0.0069|
|openbookqa | 1|none | 0|acc | 0.2120|± |0.0183|
| | |none | 0|acc_norm | 0.3340|± |0.0211|
|piqa | 1|none | 0|acc | 0.7100|± |0.0106|
| | |none | 0|acc_norm | 0.7144|± |0.0105|
|sciq | 1|none | 0|acc | 0.8540|± |0.0112|
| | |none | 0|acc_norm | 0.7830|± |0.0130|
|wikitext | 2|none | 0|word_perplexity|15.8394|± |N/A |
| | |none | 0|byte_perplexity| 1.6763|± |N/A |
| | |none | 0|bits_per_byte | 0.7453|± |N/A |
|winogrande | 1|none | 0|acc | 0.5872|± |0.0138|
hf (pretrained=lomahony/pythia-1.4b-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 | 0.2892|± |0.0133|
| | |none | 5|acc_norm | 0.3097|± |0.0135|
|arc_easy | 1|none | 5|acc | 0.6444|± |0.0098|
| | |none | 5|acc_norm | 0.6309|± |0.0099|
|boolq | 2|none | 5|acc | 0.6333|± |0.0084|
|hellaswag | 1|none | 5|acc | 0.4065|± |0.0049|
| | |none | 5|acc_norm | 0.5215|± |0.0050|
|lambada_openai| 1|none | 5|perplexity | 9.7040|± |0.2887|
| | |none | 5|acc | 0.4951|± |0.0070|
|openbookqa | 1|none | 5|acc | 0.2220|± |0.0186|
| | |none | 5|acc_norm | 0.3100|± |0.0207|
|piqa | 1|none | 5|acc | 0.7029|± |0.0107|
| | |none | 5|acc_norm | 0.7127|± |0.0106|
|sciq | 1|none | 5|acc | 0.9170|± |0.0087|
| | |none | 5|acc_norm | 0.9160|± |0.0088|
|wikitext | 2|none | 5|word_perplexity|15.8394|± |N/A |
| | |none | 5|byte_perplexity| 1.6763|± |N/A |
| | |none | 5|bits_per_byte | 0.7453|± |N/A |
|winogrande | 1|none | 5|acc | 0.5699|± |0.0139|
|