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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) 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/9507tygf)
See [Pythia-160m](https://huggingface.co/EleutherAI/pythia-410m) 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-160m-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 | 0.1894|± | 0.0115|
| | |none | 0|acc_norm | 0.2235|± | 0.0122|
|arc_easy | 1|none | 0|acc | 0.3889|± | 0.0100|
| | |none | 0|acc_norm | 0.3737|± | 0.0099|
|boolq | 2|none | 0|acc | 0.5346|± | 0.0087|
|hellaswag | 1|none | 0|acc | 0.2801|± | 0.0045|
| | |none | 0|acc_norm | 0.2949|± | 0.0046|
|lambada_openai| 1|none | 0|perplexity |439.3682|± |23.5771|
| | |none | 0|acc | 0.0984|± | 0.0041|
|openbookqa | 1|none | 0|acc | 0.1580|± | 0.0163|
| | |none | 0|acc_norm | 0.2260|± | 0.0187|
|piqa | 1|none | 0|acc | 0.5936|± | 0.0115|
| | |none | 0|acc_norm | 0.5865|± | 0.0115|
|sciq | 1|none | 0|acc | 0.5710|± | 0.0157|
| | |none | 0|acc_norm | 0.6290|± | 0.0153|
|wikitext | 2|none | 0|word_perplexity| 87.3261|± |N/A |
| | |none | 0|byte_perplexity| 2.3068|± |N/A |
| | |none | 0|bits_per_byte | 1.2059|± |N/A |
|winogrande | 1|none | 0|acc | 0.4878|± | 0.0140|
hf (pretrained=lomahony/pythia-160m-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 | 0.2022|± | 0.0117|
| | |none | 5|acc_norm | 0.2270|± | 0.0122|
|arc_easy | 1|none | 5|acc | 0.3733|± | 0.0099|
| | |none | 5|acc_norm | 0.3746|± | 0.0099|
|boolq | 2|none | 5|acc | 0.5413|± | 0.0087|
|hellaswag | 1|none | 5|acc | 0.2770|± | 0.0045|
| | |none | 5|acc_norm | 0.2853|± | 0.0045|
|lambada_openai| 1|none | 5|perplexity |1644.8526|± |87.8870|
| | |none | 5|acc | 0.0491|± | 0.0030|
|openbookqa | 1|none | 5|acc | 0.1400|± | 0.0155|
| | |none | 5|acc_norm | 0.2200|± | 0.0185|
|piqa | 1|none | 5|acc | 0.5892|± | 0.0115|
| | |none | 5|acc_norm | 0.5854|± | 0.0115|
|sciq | 1|none | 5|acc | 0.5100|± | 0.0158|
| | |none | 5|acc_norm | 0.6020|± | 0.0155|
|wikitext | 2|none | 5|word_perplexity| 87.3261|± |N/A |
| | |none | 5|byte_perplexity| 2.3068|± |N/A |
| | |none | 5|bits_per_byte | 1.2059|± |N/A |
|winogrande | 1|none | 5|acc | 0.5178|± | 0.0140|
|