244 lines
12 KiB
Markdown
244 lines
12 KiB
Markdown
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---
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language:
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- en
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tags:
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- pytorch
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- causal-lm
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- pythia
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- pythia_v0
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license: apache-2.0
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datasets:
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- EleutherAI/the_pile_deduplicated
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---
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The *Pythia Scaling Suite* is a collection of models developed to facilitate
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interpretability research. It contains two sets of eight models of sizes
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70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
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models: one trained on the Pile, and one trained on the Pile after the dataset
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has been globally deduplicated. All 8 model sizes are trained on the exact
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same data, in the exact same order. All Pythia models are available
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[on Hugging Face](https://huggingface.co/models?other=pythia).
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The Pythia model suite was deliberately designed to promote scientific
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research on large language models, especially interpretability research.
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Despite not centering downstream performance as a design goal, we find the
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models <a href="#evaluations">match or exceed</a> the performance of
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similar and same-sized models, such as those in the OPT and GPT-Neo suites.
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Please note that all models in the *Pythia* suite were renamed in January
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2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
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comparing the old and new names</a> is provided in this model card, together
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with exact parameter counts.
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## Pythia-1B-deduped
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### Model Details
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- Developed by: [EleutherAI](http://eleuther.ai)
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- Model type: Transformer-based Language Model
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- Language: English
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- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
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for training procedure, config files, and details on how to use.
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- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
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- License: Apache 2.0
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- Contact: to ask questions about this model, join the [EleutherAI
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Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
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Please read the existing *Pythia* documentation before asking about it in the
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EleutherAI Discord. For general correspondence: [contact@eleuther.
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ai](mailto:contact@eleuther.ai).
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<figure>
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| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
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| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
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| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
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| 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
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| 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10<sup>-4</sup> | OPT-350M |
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| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
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| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
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| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
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| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
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| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
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<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
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non-deduped models of a given size have the same hyperparameters. “Equivalent”
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models have <b>exactly</b> the same architecture, and the same number of
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non-embedding parameters.</figcaption>
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</figure>
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### Uses and Limitations
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#### Intended Use
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The primary intended use of Pythia is research on the behavior, functionality,
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and limitations of large language models. This suite is intended to provide
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a controlled setting for performing scientific experiments. To enable the
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study of how language models change in the course of training, we provide
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143 evenly spaced intermediate checkpoints per model. These checkpoints are
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hosted on Hugging Face as branches. Note that branch `143000` corresponds
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exactly to the model checkpoint on the `main` branch of each model.
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You may also further fine-tune and adapt Pythia-1B-deduped for deployment,
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as long as your use is in accordance with the Apache 2.0 license. Pythia
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models work with the Hugging Face [Transformers
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Library](https://huggingface.co/docs/transformers/index). If you decide to use
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pre-trained Pythia-1B-deduped as a basis for your fine-tuned model, please
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conduct your own risk and bias assessment.
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#### Out-of-scope use
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The Pythia Suite is **not** intended for deployment. It is not a in itself
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a product and cannot be used for human-facing interactions.
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Pythia models are English-language only, and are not suitable for translation
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or generating text in other languages.
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Pythia-1B-deduped has not been fine-tuned for downstream contexts in which
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language models are commonly deployed, such as writing genre prose,
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or commercial chatbots. This means Pythia-1B-deduped will **not**
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respond to a given prompt the way a product like ChatGPT does. This is because,
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unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
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Learning from Human Feedback (RLHF) to better “understand” human instructions.
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#### Limitations and biases
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The core functionality of a large language model is to take a string of text
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and predict the next token. The token deemed statistically most likely by the
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model need not produce the most “accurate” text. Never rely on
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Pythia-1B-deduped to produce factually accurate output.
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This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
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known to contain profanity and texts that are lewd or otherwise offensive.
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See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
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discussion of documented biases with regards to gender, religion, and race.
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Pythia-1B-deduped may produce socially unacceptable or undesirable text,
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*even if* the prompt itself does not include anything explicitly offensive.
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If you plan on using text generated through, for example, the Hosted Inference
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API, we recommend having a human curate the outputs of this language model
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before presenting it to other people. Please inform your audience that the
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text was generated by Pythia-1B-deduped.
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### Quickstart
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Pythia models can be loaded and used via the following code, demonstrated here
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for the third `pythia-70m-deduped` checkpoint:
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```python
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from transformers import GPTNeoXForCausalLM, AutoTokenizer
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model = GPTNeoXForCausalLM.from_pretrained(
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"EleutherAI/pythia-70m-deduped",
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revision="step3000",
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cache_dir="./pythia-70m-deduped/step3000",
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"EleutherAI/pythia-70m-deduped",
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revision="step3000",
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cache_dir="./pythia-70m-deduped/step3000",
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)
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inputs = tokenizer("Hello, I am", return_tensors="pt")
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tokens = model.generate(**inputs)
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tokenizer.decode(tokens[0])
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```
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Revision/branch `step143000` corresponds exactly to the model checkpoint on
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the `main` branch of each model.<br>
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For more information on how to use all Pythia models, see [documentation on
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GitHub](https://github.com/EleutherAI/pythia).
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### Training
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#### Training data
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Pythia-1B-deduped was trained on the Pile **after the dataset has been
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globally deduplicated**.<br>
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[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
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English. It was created by EleutherAI specifically for training large language
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models. It contains texts from 22 diverse sources, roughly broken down into
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five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
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prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
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miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
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paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
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methodology, and a discussion of ethical implications. Consult [the
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datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
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about the Pile and its component datasets. The Pile can be downloaded from
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the [official website](https://pile.eleuther.ai/), or from a [community
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mirror](https://the-eye.eu/public/AI/pile/).
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#### Training procedure
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All models were trained on the exact same data, in the exact same order. Each
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model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
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model are saved every 2,097,152,000 tokens, spaced evenly throughout training.
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This corresponds to training for just under 1 epoch on the Pile for
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non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
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All *Pythia* models trained for the equivalent of 143000 steps at a batch size
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of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch
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size of 4M tokens listed were originally trained for 71500 steps instead, with
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checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for
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consistency with all 2M batch models, so `step1000` is the first checkpoint
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for `pythia-1.4b` that was saved (corresponding to step 500 in training), and
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`step1000` is likewise the first `pythia-6.9b` checkpoint that was saved
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(corresponding to 1000 “actual” steps).<br>
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See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
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procedure, including [how to reproduce
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it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
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Pythia uses the same tokenizer as [GPT-NeoX-
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20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
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### Evaluations
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All 16 *Pythia* models were evaluated using the [LM Evaluation
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Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
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the results by model and step at `results/json/*` in the [GitHub
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repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br>
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Expand the sections below to see plots of evaluation results for all
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Pythia and Pythia-deduped models compared with OPT and BLOOM.
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<details>
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<summary>LAMBADA – OpenAI</summary>
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/>
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</details>
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<details>
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<summary>Physical Interaction: Question Answering (PIQA)</summary>
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/>
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</details>
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<details>
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<summary>WinoGrande</summary>
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/>
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</details>
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<details>
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<summary>AI2 Reasoning Challenge – Challenge Set</summary>
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/>
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</details>
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<details>
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<summary>SciQ</summary>
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/>
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</details>
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### Naming convention and parameter count
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*Pythia* models were renamed in January 2023. It is possible that the old
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naming convention still persists in some documentation by accident. The
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current naming convention (70M, 160M, etc.) is based on total parameter count.
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<figure style="width:32em">
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| current Pythia suffix | old suffix | total params | non-embedding params |
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| --------------------: | ---------: | -------------: | -------------------: |
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| 70M | 19M | 70,426,624 | 18,915,328 |
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| 160M | 125M | 162,322,944 | 85,056,000 |
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| 410M | 350M | 405,334,016 | 302,311,424 |
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| 1B | 800M | 1,011,781,632 | 805,736,448 |
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| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
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| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
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| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
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| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
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</figure>
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