Cerebras-GPT is a family of open, compute-efficient, large language models (LLMs) developed by Cerebras Systems, designed to provide a scalable and accessible alternative to existing large language models like GPT-3 and GPT-4. It consists of seven models with varying sizes from 111 million (M) to 13 billion (B) parameters, trained on the Pile dataset using a compute-optimal method referred to as the “Chinchilla recipe”. This method is aimed at achieving high accuracy for a given compute budget by utilizing 20 data tokens for every parameter in the model【5†source】【6†source】.
Pros of Cerebras-GPT:
- Compute Efficiency: By following the Chinchilla training recipe across every model size, Cerebras-GPT sets a new benchmark for training accuracy, efficiency, and openness. It is designed to achieve the lowest loss per unit of compute across all model sizes, making it a highly efficient choice for large-scale AI training tasks【5†source】【6†source】.
- Open Source and Accessibility: All seven models, along with their weights and training recipes, are released under the Apache 2.0 license, making them freely available for download and use by the research community. This open approach facilitates broader access and experimentation, allowing for the creation of powerful, industry-specific applications with minimal effort【6†source】.
- Simplicity in Training: Cerebras-GPT showcases the capability of the Cerebras CS-2 systems, which allow for simple, data-parallel training of very large models without the complexity and technical expertise required for training on GPUs【5†source】.
Cons of Cerebras-GPT:
- Relative Capability: When directly compared to other models like LLaMA, ChatGPT, or GPT-4, Cerebras-GPT may not be as capable in performing tasks. It’s been noted that while it provides GPT-3-like performance, it may not excel in all aspects or tasks when compared to these more advanced models【7†source】.
- Undertraining Relative to Size: Despite its efficiency, Cerebras-GPT is considered to be “intentionally undertrained” relative to other models to achieve a training compute optimal state. This means that while it’s efficient, its performance might not match the very high standards set by larger models in the LLaMA or GPT series【7†source】.
Use Cases:
Cerebras-GPT can be particularly useful for research and development within the AI community, providing a versatile and efficient tool for exploring new optimizations, workflows, and applications. Its open-source nature and the availability of models of various sizes enable experimentation with fine-tuning and the development of specialized applications across industries【6†source】.
Pricing:
The information regarding the specific pricing of using Cerebras-GPT models, especially in the context of cloud or hardware access, wasn’t directly available. However, the open-source nature of the models themselves suggests that there are no direct costs associated with downloading and using the models for research or commercial ventures under the Apache 2.0 license. Any costs would likely be related to the computational resources required for training or fine-tuning the models on specific tasks, which would depend on the infrastructure chosen by the user【6†source】.
In conclusion, Cerebras-GPT offers a promising approach to democratizing access to large language models by combining efficiency, openness, and simplicity in training. While it may not match the raw performance of the largest models available, its accessibility and efficiency make it a valuable addition to the AI research and development landscape.
