LLMs Explained,
Pythia

The Pythia suite comprises 16 LLMs, which have undergone training on publicly available data in a consistent sequence. These models vary in parameter size, ranging from 70M to 12B. For each of the 16 models, there are 154 checkpoints accessible to the public, along with accompanying tools that facilitate the downloading and reconstruction of their precise training dataloaders, enabling further examination and analysis.

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An Overview of Pythia

Pythia aims to facilitate research in various domains related to large language models. It presents several intriguing case studies, including investigations into memorization, the impact of term frequency on few-shot performance, and techniques for reducing gender bias.

Pythia is a suite of 16 LLMs trained on public data

Suite of 16 trained LLMs

Pythia brings forth a suite of 16 large language models (LLMs) that have been meticulously trained in a controlled environment, ensuring that they all received the same data in the same order.

Pythia models have size ranges from 70M to 12B parameters

70M to 12B parameters

Ranging in size from 70M to 12B parameters, the Pythia models offer a diverse range of capacities and capabilities for in-depth analysis and exploration.

Access to 154 checkpoints for each model

Access to 154 checkpoints

Pythia Suite gives encourages further research and study with the model, hence it provides access to 154 checkpoints for each model and their training data loaders.

Model Details

Pythia is a suite of 16 large language models (LLMs) developed to study the behavior and dynamics of LLMs during training and scaling. These LLMs were trained on public data in the same order, ranging in size from 70M to 12B parameters. The purpose of Pythia is to provide a controlled and accessible environment for research in various areas related to LLMs. Pythia aims to offer insights into LLMs and their training dynamics through case studies on memorization, term frequency effects on few-shot performance, and reducing gender bias.

Model Type: Decoder-only autoregressive models
Language(s): English
License: Apache-2.0

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HuggingFace

Author Note

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Model highlights

The Pythia suite models are trained using various programming languages, including Python and associated libraries such as PyTorch. The suite is available under an open-source license, allowing researchers to access and use the models for their studies. Pythia provides public access to 154 checkpoints for each of the 16 models. Researchers can download and reconstruct the exact training data loaders for further study.

Parallel Attention and Feedforward

In parallel attention, the model processes multiple chunks or segments of input data simultaneously, allowing for efficient and parallelized computation. This helps improve the model's ability to handle longer sequences and speeds up training and inference. With the feedforward component, each layer applies a non-linear transformation to the input, enhancing the model's ability to capture complex patterns and dependencies in the data.

Flash Attention

The model architecture and hyperparameters largely follow Brown et al. (2020), with a few notable deviations. Instead of using sparse and dense
attention layers in alternation, the model uses only fully dense layers. The model uses flash attention during training for improved device throughput.

Rotary Embeddings

Rotary embeddings are a technique used in Large Language Models (LLMs) to enhance the positional encoding of input data. It introduces a dynamic rotational component to the embeddings. The model uses rotary embeddings as the positional embedding type of choice. The model also un embedding/unembedding matrices to make interpretability research easier.

Training Details

Training Data

The models in the Pythia suite are trained on public data using the GPTNeoX open-source library developed by EleutherAI. Two versions of the training data are used: the standard Pile and the deduplicated Pile. The standard Pile contains 334B tokens, while the deduplicated Pile contains 207B tokens. The models are trained for approximately 300B tokens, matching the scale of the original GPT-3 and OPT model suites.

Training Overview

The models are trained on approximately 300 billion tokens, which are carefully matched to the original GPT-3 and OPT model suites. Training uses Adam optimizer and the Zero Redundancy Optimizer (ZeRO) to scale to multi-machine setups efficiently. Data parallelism and tensor parallelism techniques are employed to optimize performance. Flash Attention is utilized to improve hardware throughput.

Training Objective

The primary objective of training the Pythia models is to understand the behavior and dynamics of large language models across different scales. Specific case studies are conducted, including analysis of memorization, the impact of term frequency on few-shot performance, and efforts to reduce gender bias.

Training Observation

A notable departure from standard training procedures is the use of larger batch sizes. Despite previous literature suggesting convergence issues for smaller models with large batch sizes, the Pythia models show no such issues. The use of larger batch sizes results in significant wall-clock speed-ups, allowing for faster training times.

Model Types

Models are named based on their total number of parameters, but for most analyses, the developers recommend using the number of non-embedding parameters as the measure of “size.” Models marked as “equivalent” have the same architecture and number of non-embedding parameters.

Model Size	Non-Embedding Params	Layers	Model Dim	Heads
70 M	18,915,328	6	512	8
160 M	85,056,000	12	768	12
410 M	302,311,424	24	1024	16
1.0 B	805,736,448	16	2048	8
1.4 B	1,208,602,624	24	2048	16
2.8 B	2,517,652,480	32	2560	32
6.9 B	6,444,163,072	32	4096	32
12 B	11,327,027,200	36	5120	40

Model Tasks

The publisher provides detailed evaluation scores and plots throughout training for select benchmarks. They also report raw scores for the final trained models and comparisons to baseline model suites on a number of standard NLP tasks.

Language Modeling

Language modeling involves training models to predict the next word or sequence of words in a given context. It focuses on understanding and generating coherent and fluent text based on the patterns and relationships present in the training data.

Reasoning physical events and interactions

This task involves equipping language models with the ability to understand and reason about everyday physical events and interactions. It enables the models to make inferences and draw conclusions about the likely outcomes or consequences of different actions in various real-world scenarios.

Disambiguation of pronouns

The task of pronoun disambiguation aims to accurately determine the referent of a pronoun within a sentence. It requires language models to analyze the contextual information and resolve any ambiguity associated with pronouns, ensuring that the correct antecedent or referent is identified and understood.

Advanced science question answering

This task focuses on training language models to comprehend and answer complex questions related to scientific topics. It involves deep understanding of scientific concepts, logical reasoning, and the ability to extract relevant information from text sources to provide accurate and detailed answers to challenging scientific queries.

Reasoning social interactions and dynamics

Language models are trained to reason about social interactions and dynamics by understanding and interpreting social contexts, relationships, and behaviors. This enables them to generate responses, predict outcomes, or make inferences based on the given social scenario.

Logical thinking

The task of logical thinking involves training language models to engage in logical reasoning and draw valid conclusions based on logical principles. It includes tasks such as syllogism solving, mathematical problem-solving, and evaluating logical arguments, enabling the models to exhibit higher-order thinking skills and logical coherence in their responses.

Limitations

Pythia is a powerful LLM that can be used for a variety of tasks. It is important to be aware of the model's limitations, such as its bias and lack of interpretability, when using it for real-world applications.

Bias

Pythia, like other LLMs, is susceptible to bias. This can be a problem when the model is used for tasks such as translation and question answering.

Interpretability

Pythia, like other LLMs, is not very interpretable. This can make it difficult to understand how the model works and why it makes the predictions that it does.

Other LLMs

OPT

Meta AI first introduced OPT (Open Pre-trained Transformer) Language Model and released it in metaseq's repository on May 3rd, 2022

Galactica

Galactica is a large-scale language model developed by the research team at Meta Platforms, Inc.

LLaMA

Meta first introduced LLaMA in February 2023. LLaMA (Large Language Model Meta AI)

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Pythia

LLMs Explained,
Pythia

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Pythia

Pythia is a suite of 16 LLMs trained on public data

Suite of 16 trained LLMs

Pythia models have size ranges from 70M to 12B parameters

70M to 12B parameters

Access to 154 checkpoints for each model

Access to 154 checkpoints

Model Details

Model highlights

Training Details

Training Data

Training Overview

Training Objective

Training Observation

Model Types

Business Applications

Model Tasks

Language Modeling

Reasoning physical events and interactions

Disambiguation of pronouns

Advanced science question answering

Reasoning social interactions and dynamics

Logical thinking

Fine-tuning

Benchmark Results

Limitations

Bias

Interpretability

Other LLMs

OPT

Galactica

LLaMA

White Papers

Products

MENU

LLMs Explained,Pythia

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Pythia

Pythia is a suite of 16 LLMs trained on public data

Suite of 16 trained LLMs

Pythia models have size ranges from 70M to 12B parameters

70M to 12B parameters

Access to 154 checkpoints for each model

Access to 154 checkpoints

Model Details

Model highlights

Training Details

Training Data

Training Overview

Training Objective

Training Observation

Model Types

Business Applications

Model Tasks

Language Modeling

Reasoning physical events and interactions

Disambiguation of pronouns

Advanced science question answering

Reasoning social interactions and dynamics

Logical thinking

Fine-tuning

Benchmark Results

Limitations

Bias

Interpretability

Other LLMs

OPT

Galactica

LLaMA

LLMs Explained,
Pythia