Since OpenAI has not open-sourced the code for ChatGPT, replicating the chatbot is a herculean task, and even the big-tech are struggling. But, AI startup Colossal-AI has found a way to build your own ChatGPT with less computing resources. 

For this, the company has leveraged a PyTorch-based implementation, that covers all three stages from pre-training, reward model training, and reinforcement learning. The company offers a demo version of the training process that requires only 1.62 GB of GPU memory, and can be done on a single consumer-grade GPU, with 10.3x growth on one GPU model capacity. 

Check out the GitHub repository here.

Colossal-AI said that compared to the original PyTorch, the single-machine process is 7.7 times faster and a single-GPU inference can be 1.42 times faster which is achievable on a single line of code. For fine-tuning, users can increase the capacity of the model by up to 3.7 times with one line of code on a single GPU, while running at a high speed. 

The original PyTorch implementation typically requires a 780 million parameter model on A100 80GB, which costs $14,999. Colossal-AI, on the other hand, boosts it to a single GPU by 10.3 times to 8 billion parameters. 

There are multiple versions available of a single-GPU scale, a multiple-GPUs scale on a single node, along with a 175 billion parameter scale. Developers can also import OPT, GPT-3, and BLOOM pre-trained language models from Hugging Face. 

Learn more about the process by checking out the documentation and code.

The post Now Build ChatGPT On Your Own Device appeared first on Analytics India Magazine.