Pythia: A Suite of 16 LLMs for In-Depth Analysis

Pythia: A Suite of 16 LLMs for In-Depth Research
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Right now massive language fashions and LLM-powered chatbots like ChatGPT and GPT-4 have built-in properly into our each day lives. 

Nonetheless, decoder-only autoaggressive transformer fashions have been used extensively for generative NLP functions lengthy earlier than LLM functions grew to become mainstream. It may be useful to know how they evolve throughout coaching and the way their efficiency adjustments as they scale.

Pythia, a venture by Eleuther AI is a collection of 16 massive language fashions that present reproducibility for research, evaluation, and additional analysis. This text is an introduction to Pythia.



As talked about, Pythia is a collection of 16 massive language fashions— decoder-only autoregressive transformer fashions—skilled on publicly obtainable dataset. The fashions within the suite have sizes starting from 70M to 12B parameters.

  • The complete suite was skilled on the identical information in the identical order. This facilitates reproducibility of the coaching course of. So we cannot solely replicate the coaching pipeline but in addition analyze the language fashions and research their habits in depth.
  • It additionally offers services for downloading the coaching information loaders and greater than 154 mannequin checkpoints for every of the 16 language fashions.



Now let’s delve into the main points of the Pythia LLM suite.


Coaching Dataset


The Pythia LLM suite was skilled on the next datasets:

  • Pile dataset with 300B tokens 
  • Deduplicated Pile dataset with 207B tokens.

There are 8 completely different mannequin sizes with the smallest and largest fashions having 70M and 12B parameters, respectively. Different mannequin sizes embrace 160M, 410M, 1B, 1.4B, 2.8B, and 6.9B.

Every of those fashions was skilled on each the Pile and the deduplicated Pile datasets leading to a complete of 16 fashions. The next desk reveals the mannequin sizes and a subset of hyperparameters. 


Pythia: A Suite of 16 LLMs for In-Depth Research
Fashions and hyperparameters | Image source


For full particulars of the hyperparameters used, learn Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.


Coaching Course of


Right here’s an outline of the structure and coaching course of: 

  • All fashions have totally dense layers and use flash attention.
  • For simpler interpretability untied embedding matrices are used.
  • A batch dimension of 1024 is used with sequence size of 2048. This huge batch dimension considerably reduces the wall-clock coaching time.
  • The coaching course of additionally leverages optimization strategies reminiscent of information and tensor parallelism

For the coaching course of, the GPT-Neo-X library (contains options from the DeepSpeed library) developed by Eleuther AI is used.


Mannequin Checkpoints 


There are 154 checkpoints for every mannequin. There’s one checkpoint each 1000 iterations. As well as, there are checkpoints at log-spaced intervals earlier within the coaching course of: 1, 2, 4, 8, 16, 32, 64, 128, 256, and 512.


How Does Pythia Examine to Different Language Fashions?


The Pythia LLM suite was evaluated towards the obtainable language modeling benchmarks together with OpenAI’s LAMBADA variant. It was discovered that the efficiency of Pythia is corresponding to the OPT and BLOOM language fashions.



The important thing benefit of Pythia LLM suite is the reproducibility. The dataset is publicly obtainable, pre-tokenized information loaders, and 154 mannequin checkpoints are additionally publicly obtainable. The complete listing of hyperparameters has been launched, too. This makes replicating the mannequin coaching and evaluation easier.

In [1], the authors clarify their rationale for selecting an English language dataset over a multilingual textual content corpus. However having reproducible coaching pipelines for multilingual massive language fashions might be useful. Particularly in encouraging extra analysis and research of the dynamics of multilingual massive language fashions.



The analysis additionally presents attention-grabbing case research leveraging the reproducibility of the coaching course of of huge language fashions within the Pythia suite.


Gender Bias


All massive language fashions are susceptible to bias and misinformation. The research focuses on mitigating gender bias by modifying the pretraining information such {that a} mounted proportion has pronouns of a selected gender. This pretraining can be reproducible.




Memorization in massive language fashions can be one other space that has been broadly studied. The sequence memorization is modeled as a Poisson level course of. The research goals at understanding if the situation of the particular sequence within the coaching dataset influences memorization. It was noticed that the situation doesn’t have an effect on memorization.


Impact of Pretraining Time period Frequencies


For language fashions with 2.8B parameters and higher, the incidence of task-specific phrases within the pre-training corpus was discovered to enhance the mannequin’s efficiency on duties reminiscent of query answering.

There may be additionally a correlation between the mannequin dimension and the efficiency on extra concerned duties reminiscent of arithmetic and mathematical reasoning.


Pythia: A Suite of 16 LLMs for In-Depth Research
Efficiency on arithmetic addition process | Image source



Let’s sum up the important thing factors in our dialogue.

  • Pythia by Eleuther AI is a collection of 16 LLMs skilled on publicly obtainable Pile and deduplicated Pile datasets.
  • The scale of the LLMs vary from 70M to 12B parameters.
  • The coaching information and mannequin checkpoints are open-source and it’s attainable to reconstruct the precise coaching information loaders. So the LLM suite might be useful in understanding the coaching dynamics of huge language fashions higher.

As a subsequent step, you’ll be able to discover the Pythia suite of fashions and mannequin checkpoints on Hugging Face Hub.



[1] Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling, arXiv, 2023
Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, information science, and content material creation. Her areas of curiosity and experience embrace DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and occasional! At the moment, she’s engaged on studying and sharing her information with the developer group by authoring tutorials, how-to guides, opinion items, and extra.

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