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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) 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/sft-pythia/runs/azscanfe)
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-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 | 0.2543|± |0.0127|
| | |none | 0|acc_norm | 0.2739|± |0.0130|
|arc_easy | 1|none | 0|acc | 0.5724|± |0.0102|
| | |none | 0|acc_norm | 0.4941|± |0.0103|
|boolq | 2|none | 0|acc | 0.6199|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.3819|± |0.0048|
| | |none | 0|acc_norm | 0.4736|± |0.0050|
|lambada_openai| 1|none | 0|perplexity | 7.1374|± |0.2014|
| | |none | 0|acc | 0.5626|± |0.0069|
|openbookqa | 1|none | 0|acc | 0.2040|± |0.0180|
| | |none | 0|acc_norm | 0.3140|± |0.0208|
|piqa | 1|none | 0|acc | 0.7138|± |0.0105|
| | |none | 0|acc_norm | 0.6997|± |0.0107|
|sciq | 1|none | 0|acc | 0.8400|± |0.0116|
| | |none | 0|acc_norm | 0.7620|± |0.0135|
|wikitext | 2|none | 0|word_perplexity|16.9719|± |N/A |
| | |none | 0|byte_perplexity| 1.6981|± |N/A |
| | |none | 0|bits_per_byte | 0.7639|± |N/A |
|winogrande | 1|none | 0|acc | 0.5343|± |0.0140|
hf (pretrained=lomahony/pythia-1b-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 | 0.2628|± |0.0129|
| | |none | 5|acc_norm | 0.2918|± |0.0133|
|arc_easy | 1|none | 5|acc | 0.6040|± |0.0100|
| | |none | 5|acc_norm | 0.5816|± |0.0101|
|boolq | 2|none | 5|acc | 0.5963|± |0.0086|
|hellaswag | 1|none | 5|acc | 0.3780|± |0.0048|
| | |none | 5|acc_norm | 0.4719|± |0.0050|
|lambada_openai| 1|none | 5|perplexity |10.2584|± |0.2936|
| | |none | 5|acc | 0.4832|± |0.0070|
|openbookqa | 1|none | 5|acc | 0.1980|± |0.0178|
| | |none | 5|acc_norm | 0.3220|± |0.0209|
|piqa | 1|none | 5|acc | 0.7057|± |0.0106|
| | |none | 5|acc_norm | 0.7095|± |0.0106|
|sciq | 1|none | 5|acc | 0.8980|± |0.0096|
| | |none | 5|acc_norm | 0.9000|± |0.0095|
|wikitext | 2|none | 5|word_perplexity|16.9719|± |N/A |
| | |none | 5|byte_perplexity| 1.6981|± |N/A |
| | |none | 5|bits_per_byte | 0.7639|± |N/A |
|winogrande | 1|none | 5|acc | 0.5446|± |0.0140|
|