What does 2B and 7B mean in large language models?

When discussing large language models (LLMs), we often come across terms like 2B and 7B, which refer to the number of parameters in the model. Parameters are the trainable variables in a model that determine its behavior.

• 2B Model: A 2B model has 2 billion parameters, making it a relatively small model. It is suitable for mobile devices with limited resources and can perform simple tasks like text generation and translation.
• 7B Model: A 7B model has 7 billion parameters, making it a relatively large model. It is suitable for both mobile devices and PCs, balancing performance and efficiency. It can perform more complex tasks like writing articles, answering questions, and generating code.

Here are the parameter counts of some popular large language models:

• GPT-3: 175 billion
• LaMDA: 137 billion
• Bloom: 176 billion
• WuDao 2.0: 175 trillion

Impact of parameter count on LLM capability

Generally, the more parameters an LLM has, the more complex it is, the more knowledge it can learn, and the better it can understand language and perform complex tasks. For example, a 2B model can perform simple text generation and translation, while a 7B model can be used for more complex tasks like writing articles, answering questions, and generating code.

Comparison of different scale LLMs

Conclusion

The number of parameters in an LLM has a significant impact on its capabilities and application scenarios. When choosing an LLM, it is necessary to consider the application requirements and resource availability. For example, if you need to deploy a model on a mobile device, you need to consider the model size and parameter count and choose a model that consumes fewer resources. If you need to perform complex tasks, you need to choose a model with more parameters and better performance.