| Our N-Gen-2 model can process input sequences and generate output sequences, making it suitable for tasks like language translation, text summarization, and dialogue generation. | |
| Language Understanding: The model can understand natural language input and generate coherent responses based on the context provided. | |
| Imaginary Writing: It can generate imaginative and creative text, allowing for the generation of stories, poems, and other fictional content. | |
| No Pre-trained Model Usage: The model does not rely on pre-trained language models like GPT or BERT, making it more customizable and potentially better suited for specific tasks or domains. | |
| Encoder-Decoder Architecture: The model follows an Encoder-Decoder paradigm, where the encoder processes input sequences and the decoder generates corresponding output sequences. | |
| Flexible Text Generation: The model can generate text with varying lengths, from short sentences to longer passages, and can be controlled to limit the length of the generated output. | |
| Training Capabilities: The model can be trained using input-output pairs, allowing for supervised learning on specific datasets tailored to the task at hand. | |
| Overall, the N-GEN-2 model is a versatile architecture capable of generating natural language text for a wide range of applications, from storytelling to language translation, without relying on pre-trained models. | |
| Our Model Has 250 Billion Parameters leaving N-Gen-1 Far behind which had just 30 Million Parameters | |
| The Dataset of the N-Gen-2 is special made to train it for many task described above |