About the GPT-J Model (Large Language Model)

GPT-J is an open-source 6 billion parameter generative pre-trained transformer model developed by EleutherAI. As a GPT-3-inspired architecture, it includes distinctive features like Rotary Position Embeddings and dense attention, contributing to its efficiency in natural language processing tasks. It was trained on the Pile dataset using the Mesh Transformer JAX library and shows commendable performance in code generation and text continuation tasks.

Overview

• Use Case: Text generation, code generation, natural language processing tasks
• Creator: EleutherAI
• Architecture: Generative Pre-trained Transformer with Rotary Position Embeddings and dense attention
• Parameters: 6B
• Release Date: 2021-06-04
• License: Apache 2.0
• Context Length: 2,048 tokens

GPU Memory Requirements

Default (FP16) inference requires approximately 12 GB of GPU memory.

Training Data

The Pile dataset - a diverse 825GB language modeling dataset

Evaluation Benchmarks

• LAMBADA
• HellaSwag
• WinoGrande
• ARC
• PIQA

References

• https://arankomatsuzaki.wordpress.com/2021/06/04/gpt-j/
• https://github.com/kingoflolz/mesh-transformer-jax
• https://huggingface.co/EleutherAI/gpt-j-6b
• https://arxiv.org/abs/2101.00027
• https://en.wikipedia.org/wiki/GPT-J

Notes

• Model is not designed for factual accuracy, only probabilistic text generation
• Fine-tuning recommended for specific tasks
• Potential biases present from training data
• Performance comparable to GPT-3 of similar size in various tasks
• GPT-J does not have a dedicated research paper; primary documentation is creator Aran Komatsuzaki's blog post and the mesh-transformer-jax GitHub repository
• Training dataset documented in The Pile paper (arXiv:2101.00027)
• Developed by Ben Wang and Aran Komatsuzaki at EleutherAI