EleutherAI
by EleutherAI Institute • Distributed / Remote (Non-Profit) • Founded 2020
Open-Source AI Research for Interpretability, Alignment, and Language Models
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What is EleutherAI?
EleutherAI is a non-profit AI research institute dedicated to open-source large language model development, interpretability research, and AI alignment. Founded in 2020, the organization created landmark models like GPT-Neo, GPT-J, GPT-NeoX-20B, and the Pythia suite, all released under permissive Apache 2.0 licenses.
It also built The Pile, an 886 GB curated text dataset used to train models by Microsoft, Meta, and others. With the LM Evaluation Harness now serving as the standard benchmark framework across the industry, EleutherAI's tools and models have been downloaded over 70 million times and cited in 130+ academic publications.
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Who Is EleutherAI For
Pros & Cons
- Fully Open-Source Apache 2.0
- Industry-Standard Evaluation Framework
- Strong Academic Publication Record
- Reproducible Training Pipelines
- No Hosted Inference API
- Requires Significant GPU Resources
- Models Trail Commercial Frontiers
Frequently Asked Questions
5 questionsEleutherAI has released GPT-Neo (125M, 1.3B, 2.7B parameters), GPT-J (6B), GPT-NeoX-20B (20B), and the Pythia suite spanning 70M to 12B parameters. All models are available on Hugging Face under the Apache 2.0 license.
The LM Evaluation Harness is an open-source framework that provides a unified interface for running few-shot evaluations across hundreds of NLP tasks. It supports multiple model backends and is used by Hugging Face's Open LLM Leaderboard as the standard evaluation tool.
The Pile is an 886 GB curated English text dataset composed of 22 sub-datasets chosen for diversity. It has been used to train models by Microsoft, Meta, Yandex, Stanford, and many others, making it one of the most widely adopted open training corpora.
The general rule is approximately 2 bytes per parameter for inference. GPT-J-6B needs around 12 GB VRAM at float16, while GPT-NeoX-20B requires roughly 40 GB. Quantization techniques like 4-bit or 8-bit can reduce these requirements significantly.
Pythia is specifically designed for scientific research rather than maximum performance. It provides 154 intermediate training checkpoints, fully public training data order, and exact reproducibility, enabling researchers to study how model capabilities emerge during training.
What's New
monthlyNew research on detecting reward hacking signals in LLM reasoning through interpolation-based analysis methods
Major alignment research update covering new findings in reward hacking detection and mitigation strategies
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EleutherAI Pricing
Completely free
- Full access to all models (GPT-Neo, GPT-J, GPT-NeoX, Pythia)
- The Pile dataset (886 GB curated training corpus)
- LM Evaluation Harness benchmarking framework
- Apache 2.0 license for commercial and research use
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