How to Build Your Own ChatGPT with Less Computing Resources?

We have compiled a guide on how to build your own ChatGPT with less computing resources

Replicating the chatbot is a monumental undertaking since OpenAI has not made the code for ChatGPT open-sourced, and even big-tech companies are having difficulty. Nevertheless, AI firm Colossal-AI has discovered a method for creating your own ChatGPT with less computer power.

The company has used a PyTorch-based approach to achieve this aim, which includes pre-training, reward model training, and reinforcement learning. With 10.3x growth on one GPU model capacity, they provide a sample version of the training procedure that uses just 1.62 GB of GPU memory and can be completed on a single consumer-grade GPU.

According to Colossal-AI, a single-machine process maybe 7.7 times quicker than the original PyTorch, and a single-GPU inference can be 1.42 times faster, which is possible with just one line of code. Users may improve the model's capacity for fine-tuning by up to 3.7 times with just one line of code executing quickly on a single GPU.

An A100 80GB model with 780 million parameters is normally needed for the original PyTorch implementation, which costs US$14,999. On the other hand, Colossal-AI multiplies it by 10.3 to reach 8 billion parameters on a single GPU.

A single-GPU scale, a multiple-GPU scale on a single node, and a 175-billion parameter scale are all accessible in various configurations. Furthermore, those available from Hugging Face are pre-trained language models for OPT, GPT-3, and BLOOM.