OPT: Open Pre-trained Transformer Language Models
FreeOpen, reproducible large language models for research
FreeFree tier
Inputs: textOutputs: text
About OPT: Open Pre-trained Transformer Language Models
OPT (Open Pre-trained Transformer Language Models) is a suite of autoregressive language models released by Meta AI, ranging from 125 million to 175 billion parameters. OPT models are designed to be fully open-source, providing access to model weights, code, and training details to facilitate research and reproducibility in natural language processing.
Key Features
Suite of autoregressive language models from 125M to 175B parameters
Fully open-source: model weights, code, and training details publicly available
Trained on a diverse corpus of text, including books, web text, and more
Supports text generation and few-shot learning tasks
Released under a permissive license for research use
Pros & Cons
Pros
- Fully open-source, promoting transparency and reproducibility
- Wide range of model sizes to suit different computational budgets
- Strong baseline for comparing with other LLMs
- Actively used by the research community
Cons
- Requires significant computational resources for large model sizes (e.g., 175B)
- Trained on uncurated internet data, which may contain biases and toxic content
- Not as well-tuned for chat or instruction following as some later models
Best For
Academic research into large language model behavior and capabilitiesText generation and completion tasksFew-shot and zero-shot learning evaluationsBenchmarking and reproducibility studies in NLP
FAQ
What does OPT stand for?
OPT stands for Open Pre-trained Transformer, referring to the open-source release of pre-trained transformer language models.
Who created OPT?
OPT was created by researchers at Meta AI (formerly Facebook AI Research).
Is OPT free to use?
Yes, OPT is released as open-source and is free to use for research purposes, subject to the terms of the license.