|
--- |
|
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| |
|
|
