Code mode can help smaller LLM models
Researchers have found that smaller LLM models can be improved by using a code mode to call tools, rather than relying on the model to generate a signature of the tool call. This approach has been shown to be effective, especially when multiple tool calls are involved, and can help reduce token spends. The code mode concept was likely introduced by Cloudfare, and is now being used by companies such as Anthropic to improve the performance of their LLM models.
- ▪Smaller LLM models can struggle to generate accurate tool call signatures, which can limit their effectiveness.
- ▪Using a code mode to call tools can help improve the performance of smaller LLM models, especially when multiple tool calls are involved.
- ▪The code mode concept has been adopted by companies such as Anthropic, which uses it to reduce token spends and improve the performance of their LLM models.
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Opening excerpt (first ~120 words) tap to expand
It's been clear that LLMs alone are not as powerful as "LLMs with tools". How does LLM call tools? It can generate a signature of the tool call, somewhat like a function call search_web("query") or bash("ls -l") etc. This usually calls a function written to either call the google API, exa, or execute a bash command locally. This is kind of fine as long as tool calls are properly formatted — some slightly less powerful models often make mistakes in generating those signatures. The training was so tedious that lot of models were released without the "tool calling" ability. We had to include training data involving special tokens, for example - |tool_call|.
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