microsoft
Design a model specifically for tool calls
Design a model specifically for tool calls, tokens can be set to 16k, and there is a special 'tool call' tag for calling tools. If there is a large amount of data after the tool call, there is a special 'data input' tag and an 'index' tensor for segmented data input to the model, and the model will summarize the output results.
A model specifically optimized for tool calls would need efficient handling of structured input and output, ensuring the tool-call tag initiates the correct functions while the data input tag organizes large-scale inputs for processing. The indexing tensor idea is clever—it would help manage segmented data flow and prevent overwhelming the model with too much information at once. For implementation, you'd likely want:
- Token Capacity: Set to 16k tokens to handle extended queries and responses.
- Tool Call Optimization: A dedicated 'tool call' tag to signal external functions.
- Data Segmentation: A 'data input' tag coupled with an index tensor to manage large datasets effectively.
- Summarization Module: The model would be designed to summarize results, ensuring clarity and efficiency.
Here are some useful resources that might help with designing a model for tool calls:
- GitHub Discussion on Tool Calling Models: A discussion on standardizing tool calling with chat models, including sample code snippets and implementation details. You can check it out [here](https://github.com/langchain-ai/langchain/discussions/20343).
