transformers pytorch safetensors gpt_neox text-generation causal-lm pythia en dataset:Anthropic/hh-rlhf arxiv:2101.00027 license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- Anthropic/hh-rlhf
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---

[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|
