Which programming languages should I learn to develop large language models?

 Developing large language models requires a strong understanding of natural language processing (NLP) and machine learning, as well as experience with programming languages commonly used in these fields. Here are some programming languages that are commonly used in developing large language models:

1. Python: Python is one of the most widely used programming languages in the field of NLP and machine learning. It has a large number of libraries and frameworks, including TensorFlow, PyTorch, and Keras, which are commonly used in developing large language models.
2. C++: C++ is a high-performance programming language that is commonly used in the development of large language models, especially for low-level optimization and processing speed.
3. Java: Java is a general-purpose programming language that is commonly used in large-scale applications, including NLP and machine learning. It is known for its scalability and performance.
4. Rust: Rust is a systems programming language that is designed for high performance and reliability. It is increasingly being used in the development of large language models due to its performance and security features.
5. Julia: Julia is a relatively new programming language that is gaining popularity in the field of scientific computing, including NLP and machine learning. It is known for its speed and ease of use, and has a growing number of libraries and frameworks for NLP and machine learning.

In addition to these languages, other programming languages like JavaScript, Scala, and R can also be used in developing large language models, depending on the specific requirements and tools used.