Sidney Black


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GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Sidney Black | Stella Biderman | Eric Hallahan | Quentin Anthony | Leo Gao | Laurence Golding | Horace He | Connor Leahy | Kyle McDonell | Jason Phang | Michael Pieler | Usvsn Sai Prashanth | Shivanshu Purohit | Laria Reynolds | Jonathan Tow | Ben Wang | Samuel Weinbach
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission. In this work, we describe GPT-NeoX-20B’s architecture and training, and evaluate its performance. We open-source the training and evaluation code, as well as the model weights, at

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MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning
Constantin Eichenberg | Sidney Black | Samuel Weinbach | Letitia Parcalabescu | Anette Frank
Findings of the Association for Computational Linguistics: EMNLP 2022

Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2 % of the number of samples used to train SimVLM.