OPT

OPT (Open Pre-trained Transformers) is a 350 million parameter decoder-only language model developed by Meta AI. Released in May 2022, it's part of a suite of open-access transformer models designed to democratize large language model research. Architecture: Decoder-only transformer, similar to GPT-3 Training: Pretrained on massive English text collections via causal language modeling (CLM) Accessibility: Fully open-source, enabling reproducible and responsible AI research Performance: Trained to match GPT-3 class models while incorporating latest best practices in data collection and efficient training Text generation tasks Zero-shot and few-shot learning Natural language understanding and generation Researchers and developers seeking an openly available, lightweight language model for: Academic research on LLM behavior and limitations Fine-tuning for specific applications Studying bias, robustness, and toxicity in language models Prototyping without paid API dependencies Model Type: Text Generation Transformer Framework Support: PyTorch, TensorFlow, JAX Language: Primarily English (with minimal non-English data from CommonCrawl) License: Other (consult official documentation) OPT-350m represents Meta's commitment to open AI research, providing the community with a capable, interpretable alternative to proprietary language models.