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---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-410m](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/quq2097z)
See [Pythia-410m](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-410m-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 | 0.2355|± |0.0124|
| | |none | 0|acc_norm | 0.2594|± |0.0128|
|arc_easy | 1|none | 0|acc | 0.5051|± |0.0103|
| | |none | 0|acc_norm | 0.4478|± |0.0102|
|boolq | 2|none | 0|acc | 0.6113|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.3372|± |0.0047|
| | |none | 0|acc_norm | 0.4001|± |0.0049|
|lambada_openai| 1|none | 0|perplexity |21.8172|± |0.7736|
| | |none | 0|acc | 0.3755|± |0.0067|
|openbookqa | 1|none | 0|acc | 0.1940|± |0.0177|
| | |none | 0|acc_norm | 0.2960|± |0.0204|
|piqa | 1|none | 0|acc | 0.6719|± |0.0110|
| | |none | 0|acc_norm | 0.6687|± |0.0110|
|sciq | 1|none | 0|acc | 0.7700|± |0.0133|
| | |none | 0|acc_norm | 0.6540|± |0.0151|
|wikitext | 2|none | 0|word_perplexity|23.8136|± |N/A |
| | |none | 0|byte_perplexity| 1.8091|± |N/A |
| | |none | 0|bits_per_byte | 0.8553|± |N/A |
|winogrande | 1|none | 0|acc | 0.5320|± |0.0140|
hf (pretrained=lomahony/pythia-410m-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 | 0.2355|± |0.0124|
| | |none | 5|acc_norm | 0.2790|± |0.0131|
|arc_easy | 1|none | 5|acc | 0.5274|± |0.0102|
| | |none | 5|acc_norm | 0.5072|± |0.0103|
|boolq | 2|none | 5|acc | 0.5226|± |0.0087|
|hellaswag | 1|none | 5|acc | 0.3367|± |0.0047|
| | |none | 5|acc_norm | 0.3991|± |0.0049|
|lambada_openai| 1|none | 5|perplexity |37.4791|± |1.3737|
| | |none | 5|acc | 0.3049|± |0.0064|
|openbookqa | 1|none | 5|acc | 0.1620|± |0.0165|
| | |none | 5|acc_norm | 0.2900|± |0.0203|
|piqa | 1|none | 5|acc | 0.6708|± |0.0110|
| | |none | 5|acc_norm | 0.6676|± |0.0110|
|sciq | 1|none | 5|acc | 0.8630|± |0.0109|
| | |none | 5|acc_norm | 0.8430|± |0.0115|
|wikitext | 2|none | 5|word_perplexity|23.8136|± |N/A |
| | |none | 5|byte_perplexity| 1.8091|± |N/A |
| | |none | 5|bits_per_byte | 0.8553|± |N/A |
|winogrande | 1|none | 5|acc | 0.5272|± |0.0140|
|