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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-410m) 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/3b0ltx73)
See [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) 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-2.8b-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 | 0.2901|± |0.0133|
| | |none | 0|acc_norm | 0.3404|± |0.0138|
|arc_easy | 1|none | 0|acc | 0.6469|± |0.0098|
| | |none | 0|acc_norm | 0.5766|± |0.0101|
|boolq | 2|none | 0|acc | 0.6361|± |0.0084|
|hellaswag | 1|none | 0|acc | 0.4557|± |0.0050|
| | |none | 0|acc_norm | 0.5984|± |0.0049|
|lambada_openai| 1|none | 0|perplexity | 5.2226|± |0.1377|
| | |none | 0|acc | 0.6210|± |0.0068|
|openbookqa | 1|none | 0|acc | 0.2640|± |0.0197|
| | |none | 0|acc_norm | 0.3760|± |0.0217|
|piqa | 1|none | 0|acc | 0.7481|± |0.0101|
| | |none | 0|acc_norm | 0.7481|± |0.0101|
|sciq | 1|none | 0|acc | 0.8800|± |0.0103|
| | |none | 0|acc_norm | 0.8180|± |0.0122|
|wikitext | 2|none | 0|word_perplexity|13.4928|± |N/A |
| | |none | 0|byte_perplexity| 1.6268|± |N/A |
| | |none | 0|bits_per_byte | 0.7020|± |N/A |
|winogrande | 1|none | 0|acc | 0.6125|± |0.0137|
hf (pretrained=lomahony/pythia-2.8b-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 | 0.3285|± |0.0137|
| | |none | 5|acc_norm | 0.3677|± |0.0141|
|arc_easy | 1|none | 5|acc | 0.6873|± |0.0095|
| | |none | 5|acc_norm | 0.6835|± |0.0095|
|boolq | 2|none | 5|acc | 0.6670|± |0.0082|
|hellaswag | 1|none | 5|acc | 0.4542|± |0.0050|
| | |none | 5|acc_norm | 0.5963|± |0.0049|
|lambada_openai| 1|none | 5|perplexity | 7.4076|± |0.2095|
| | |none | 5|acc | 0.5486|± |0.0069|
|openbookqa | 1|none | 5|acc | 0.2680|± |0.0198|
| | |none | 5|acc_norm | 0.3620|± |0.0215|
|piqa | 1|none | 5|acc | 0.7568|± |0.0100|
| | |none | 5|acc_norm | 0.7486|± |0.0101|
|sciq | 1|none | 5|acc | 0.9380|± |0.0076|
| | |none | 5|acc_norm | 0.9330|± |0.0079|
|wikitext | 2|none | 5|word_perplexity|13.4928|± |N/A |
| | |none | 5|byte_perplexity| 1.6268|± |N/A |
| | |none | 5|bits_per_byte | 0.7020|± |N/A |
|winogrande | 1|none | 5|acc | 0.5935|± |0.0138|
|