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Open Pretrained Transformers (OPT) by Facebook is a suite of decoder-only pre-trained transformers. Announcement. OPT-175B text generation hosted by Alpa.

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  • Open source
  • Free forever
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What is OPT?

OPT (Open Pretrained Transformers) is a suite of large language models released by Meta (Facebook) as open-source software. It includes models ranging from 125 million to 175 billion parameters, with OPT-175B being the largest. The models are decoder-only transformers trained on diverse internet text data. OPT was designed to provide researchers and developers with access to capable language models without the restrictions or costs associated with proprietary alternatives. The 175B version can generate coherent text, answer questions, and perform various language tasks, though it requires significant computational resources to run locally.

Key features

Multiple model sizes

125M to 175B parameters, allowing selection based on computational constraints and performance needs

Open-source architecture

Full model weights and training code available for inspection, modification, and redistribution

Decoder-only transformer design

Efficient for text generation tasks without requiring custom encoder architectures

Pre-trained on diverse data

Models trained on a broad range of internet text to support multiple language tasks

Hosted inference option

OPT-175B available through Alpa's hosted service for users without local GPU resources

Pros & cons

Advantages

  • Freely available without licensing fees or API costs; suitable for research, education, and commercial use
  • Transparent training methodology; researchers can analyse model behaviour and limitations directly
  • Smaller variants run on consumer hardware; not all applications require the 175B model
  • No usage restrictions or content filters imposed by a commercial provider

Limitations

  • Requires substantial VRAM to run larger models; the 175B variant needs multiple high-end GPUs or specialised inference optimisation
  • May produce inaccurate, biased, or harmful outputs; users bear responsibility for monitoring and controlling deployment
  • Limited commercial support compared to proprietary services; debugging and optimisation fall on the user

Use cases

Research into large language model behaviour, scaling laws, and language understanding

Fine-tuning on domain-specific data for customer service, content generation, or technical writing

Educational projects to learn how transformer models work without proprietary API dependencies

Building language applications on-premises where data cannot leave a secure environment

Prototyping chatbots, question-answering systems, and text summarisation tools

Ready to try OPT?

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Pricing

Free

Free

Full access to all model weights and code; local inference on your hardware; OPT-175B inference available through Alpa's hosted service

Get started with OPT

Click through to OPT and start using it now.

Get OPT
  • Open source
  • Free forever