LLMs are machine learning models trained on massive text data. They can classify, summarize, and generate human-like text. Models like GPT-4, PaLM 2, and Claude demonstrate impressive coherence
Fine-tuning customizes LLM performance for specific use cases. Monthly storage fee of $2 per model. Mistral AI provides API and open-source mistral-finetune codebase
Generative AI creates new content and ideas across various domains. It can learn and reuse knowledge to solve new problems. Used for chatbots, media creation, and product development
LLMs are statistical language models trained on massive data for text generation and translation. Based on deep learning architectures like Google's 2017 Transformer. Can be trained on billions of text and other content
Meta released Llama 3 family of large language models on April 18, 2024. Available in 8B and 70B parameter sizes for both pre-training and instruction tuning. Uses transformer architecture with Grouped-Query Attention for scalability. Trained on over 15 trillion tokens from publicly available sources
LLMs are large language models trained on vast text datasets. IBM alignment models pioneered statistical language modeling in 1990s. Transformers architecture introduced in 2017, followed by BERT in 2018. GPT series launched in 2018, ChatGPT in 2022, GPT-4 in 2023