Ilyas Mutlu
Author at Caywork
When selecting a large language model (LLM) for specific agent actions, it's essential to consider several key factors. This checklist will guide you through the decision making process, ensuring that you choose the most suitable model for your needs.
There are 5 points that you need to look when choosing the LLM.
- Use Case
- Training Input and Method
- Context token size and Model size
- Model Speed and Performance
- Model Cost
1. Use Case
The use case is one of the most critical factors in selecting an LLM. Determine how you plan to use the model whether for chat, translation, mathematical computations, or other applications. Review the model's promotional page to understand its intended use cases and performance metrics. Benchmarks can provide valuable insights into how well the model performs in real world scenarios.
2. Training Input and Method
Understanding the training input and method is vital. Consider whether the model was trained on public or private data, the sources of that data, and any potential biases present. This information can significantly influence the model's performance and reliability. Additionally, look for models that have undergone fine tuning or utilized human feedback during training, as these factors can enhance the model's effectiveness.
3. Context Token Size and Model Size
Context token size and model size are two distinct but related aspects to consider:
- Context Token Size: This refers to the maximum number of tokens the model can process in a single request. A larger context size is beneficial for handling extensive requests, such as generating blog posts or stories. Conversely, a smaller context size may lead to faster response times, which can be advantageous in certain applications.
- Model Size: This indicates the number of parameters in billions. Generally, larger models perform better on a wide range of tasks. However, they also consume more resources. If your application does not require extensive generalization, smaller models may be a more viable option.
4. Model Speed and Performance
Model speed is influenced by both context token size and model size. Some model deployers allocate additional resources to enhance speed, which is particularly important for chat applications and scenarios where quick responses are essential. However, if the agent's tasks do not require rapid responses such as background tool calls speed may be less critical for the user experience and system performance.
5. Model Cost
Cost is a significant factor in the decision making process. If your agent requires frequent operations, such as updating a database with new products, the cost of running the model can accumulate quickly. Consider not only the operational costs but also the expenses associated with hosting and maintaining the model within your infrastructure. Balancing performance with cost effectiveness is crucial for sustainable operations.