Language Models (LMs) are computational systems designed to understand, generate, and manipulate human language. They are trained on vast amounts of text data to learn patterns, structures, and nuances of language. Large Language Models (LLMs), like GPT-4, are advanced AI systems trained on vast amounts of text data. They can understand and generate human-like text, making them useful for tasks such as translation, summarization, and conversation. Their large size allows them to capture complex patterns and nuances in language, but they require significant computational resources. Small Language Models are more compact versions of language models. They are designed to perform specific tasks with fewer resources. While they may not have the same depth of understanding as LLMs, they are efficient and can be deployed in environments with limited computational power. These models are often used in applications where speed and resource efficiency are crucial.
At enlyft, we use sophisticated, patent-pending algorithms to track the use of various Language Models products and technologies. We track 13 products in the Language Models category, and have found 1,620 companies using these products.
Product |
Install base
# of companies we found using this product |
Market Share |
---|---|---|
GPT-4 | 678 | 41% |
LLaMA | 273 | 16% |
DALL E | 179 | 11% |
Rossum | 107 | 6% |
Anthropic | 84 | < 5% |
Google BERT | 65 | < 5% |
DALL E 2 | 65 | < 5% |
Stability AI | 39 | < 5% |
GPT-3 | 38 | < 5% |
Anthropic Claude | 34 | < 5% |
Mistral AI | 24 | < 5% |
Cohere | 20 | < 5% |
Google PaLM | 14 | < 5% |