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What language is used to build ChatGPT?

What language is used to build ChatGPT?

What language is used to build ChatGPT?

An article by Anni Yang (@anniyangx at medium)

ChatGPT, like its predecessors, is primarily built using Python. Python is a versatile and widely used programming language, particularly in the fields of natural language processing (NLP) and artificial intelligence (AI). Several key libraries and frameworks are used in the development of ChatGPT:

1. PyTorch:

PyTorch is a popular open-source machine learning library used for building neural networks. It is widely used in deep learning projects, including those involving natural language processing.

2. Transformers:

The “transformers” library, which is based on PyTorch, plays a critical role in the development of ChatGPT. It provides pre-trained models for various NLP tasks, including the GPT (Generative Pre-trained Transformer) architecture.

3. Hugging Face Transformers:

Hugging Face’s “transformers” library is a widely-used resource for working with transformer models, including GPT models. It simplifies the process of loading pre-trained models and fine-tuning them for specific tasks.

4. Tokenizers:

Tokenization is a crucial step in processing text data for AI models. The “tokenizers” library, often used alongside transformers, helps tokenize and preprocess text efficiently.

5. Python Libraries:

Various Python libraries are used for data manipulation, web APIs, and other components of the project, including libraries like NumPy, pandas, Flask (for web-based applications), and more. It’s worth noting that while Python is the primary language for building ChatGPT and similar AI models, many of the underlying neural network operations and optimizations are often implemented in lower-level languages like C++ or CUDA for efficiency.

The development of AI models like ChatGPT is a collaborative effort involving researchers, engineers, and developers who leverage a combination of programming languages, libraries, and frameworks to create and fine-tune these sophisticated language models.



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