Cerebras-GPT

What is Cerebras-GPT?

Cerebras-GPT is a family of seven GPT models released by Cerebras to the open-source community. These models range from 111 million to 13 billion parameters and were trained using the Chinchilla formula, offering high accuracy for a given compute budget. Cerebras-GPT stands out for its faster training times, lower costs, and energy efficiency compared to other available models. Utilizing the CS-2 systems within the Andromeda AI supercomputer, these models were trained using a simple data-parallel weight streaming architecture, enabling quick training without the need for model partitioning. The release of Cerebras-GPT aims to provide open, reproducible, and royalty-free advanced models for both research and commercial use.

Who created Cerebras-GPT?

Cerebras-GPT was created by Sean Lie, Chief Hardware Architect, and Co-founder of Cerebras. Cerebras, led by Sean Lie, aims to democratize large models by providing training infrastructure solutions and open-sourcing the Cerebras-GPT family of large generative models. The company focuses on making large models easily trainable, cost-effective, and energy-efficient, promoting community sharing and advancement in the AI industry.

How to use Cerebras-GPT?

To use Cerebras-GPT, follow this comprehensive guide:

1. Setup: Utilize the Cerebras CS-2 systems for training. These systems are equipped with sufficient memory to run even the largest models on a single device without splitting the model.

2. Designing the Cluster: Construct the purpose-built Cerebras Wafer-Scale Cluster around the CS-2 to facilitate easy scale-out. This cluster employs a unique HW/SW co-designed execution method known as "weight streaming" which allows independent scaling of model size and cluster size without the need for model parallelism.

3. Scaling Clusters: To scale to larger clusters, adjust the number of systems in a configuration file as needed. This architecture simplifies the process of scaling to multiple CS-2 systems in the Cerebras Wafer-Scale Cluster using straightforward data parallel scaling.

4. Training: Train Cerebras-GPT models on a 16x CS-2 Cerebras Wafer-Scale Cluster named Andromeda. This cluster streamlines experiments without the complexity associated with traditional distributed systems engineering and model parallel tuning required on GPU clusters.

5. Availability: The Cerebras Wafer-Scale Cluster is accessible on the cloud through the Cerebras AI Model Studio, enabling broad community access to easily train large models.

6. Conclusion: Cerebras aims to democratize large models by addressing training infrastructure challenges and making more models available to the community.

For detailed information and further instructions on using Cerebras-GPT, refer to the Cerebras documentation available on the Cerebras website.