There are several alternatives to GPT (which is the model behind ChatGPT) that you can use for natural language processing tasks such as text generation, language translation, and language understanding. Some examples include:

• BERT: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer model developed by Google. BERT is trained on a large corpus of text and can be fine-tuned on a variety of natural language understanding tasks, such as question answering and sentiment analysis.

• RoBERTa: RoBERTa (from Robustly Optimized BERT Pre-training) is a variation of BERT that is trained for longer and with more data. RoBERTa has shown improvements over BERT on a wide range of natural language understanding tasks.

• T5: T5 (Text-to-Text Transfer Transformer) is another pre-trained transformer model developed by Google. T5 is trained on a large corpus of text and can be fine-tuned to perform a variety of natural language processing tasks, such as text summarization and text-to-text generation.

• GPT-2, GPT-3 : GPT-2 and GPT-3 (Generative Pre-trained Transformer) are both developed by OpenAI. GPT-2 is a more powerful version of GPT and GPT-3 is more powerful than GPT-2. GPT-3 is trained on a large corpus of text and can be fine-tuned to perform a wide range of natural language processing tasks, including language translation and language understanding.

These are just a few examples of pre-trained models that you can use for natural language processing tasks. Many other pre-trained models have been developed by different research groups and companies, so there are many alternatives available.