Description
Meta AI is democratizing access to large-scale language models with the release of OPT-175B, a 175-billion-parameter model trained on publicly available datasets. This initiative aims to foster broader community engagement in understanding foundational new AI technologies, moving beyond the limited access previously restricted to highly resourced labs. By providing both the pretrained models and the necessary training code, OPT-175B empowers academic researchers, civil society, policymakers, and industry research laboratories worldwide to conduct reproducible research and collectively advance the field.
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The release is underpinned by a commitment to open science and responsible AI development. Meta AI is sharing detailed documentation of the development process, including a full logbook of the day-to-day training, compute usage, and human overhead. This transparency allows other researchers to build upon their work and analyze potential harms using quantifiable metrics on a shared model. OPT-175B was trained using Meta’s open-source Fully Sharded Data Parallel (FSDP) API and NVIDIA’s tensor parallel abstraction within Megatron-LM, achieving significant energy efficiency with a carbon footprint 1/7th that of GPT-3.
In addition to OPT-175B, Meta AI is releasing a suite of smaller-scale baseline models, trained on the same data and using similar settings, to facilitate the study of scale effects. These include models with parameter counts ranging from 125 million to 30 billion. The release is governed by a noncommercial license, prioritizing research use cases and aiming to prevent misuse. By increasing the diversity of voices involved in defining ethical considerations, Meta AI hopes to drive progress in responsible AI development and establish clear guidelines for large language models.
This open collaboration is crucial for AI research to advance, allowing the scientific community to explore the potential of cutting-edge models while simultaneously probing for vulnerabilities. The release of OPT-175B and its associated resources aims to bring more perspectives to the forefront of large language model creation, aid in designing responsible release strategies, and introduce an unprecedented level of transparency to the field.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
OPT-175B Highlights
175 billion parameters
Trained on publicly available datasets
Includes pretrained models and training code
Noncommercial license for research use
Access granted to academic researchers and affiliated organizations
Detailed development process documentation
Full logbook of training process
Quantifiable metrics for harm analysis
Energy-efficient training methodology
Suite of smaller-scale baseline models released
Facilitates reproducible research
Promotes responsible AI development
Enhances transparency in LLM development
Getting Started with OPT-175B
Request access: Submit a request for access to OPT-175B.
Download code: Access the open-source code for training and deployment.
Obtain models: Download smaller-scale baseline models.
Set up environment: Prepare your research environment with necessary libraries.
Integrate model: Load and utilize the OPT-175B model for research tasks.
Analyze results: Conduct experiments and analyze model behavior and outputs.
Contribute to research: Share findings and contribute to the broader AI community.
OPT-175B's Use Cases
- NLP Research
- Bias Mitigation
- Model Interpretability
- Responsible AI
- Reproducible Science
- Text Generation
- AI Ethics
