LLM for reliable tool calling/searching

<!-- SC_OFF --><div class="md"><p>I’m currently exploring ways to optimize function calling in systems with a large number of tools. </p> <p>As the number of functions grows into

<!-- SC_OFF --><div class="md"><p>I've been thinking about this as local models get better at </p> <p>tool use — shell access, file system, HTTP requests, MCP. </p> <p>If a prompt

<!-- SC_OFF --><div class="md"><p>Most of my code is similar to agent-cpp from Mozilla. I create common_chat_templates_inputs Inputs from message history.</p> <p>auto params =

<!-- SC_OFF --><div class="md"><p><strong>Backstory:</strong><br /> I've recently been learning langchain and setting up agents that run with Ollama on the back end. I had an idea

<!-- SC_OFF --><div class="md"><p>Beginner here, now I have a good GPU and ollama using docker. Pulled the Gemma4 weights and was able to add it to cursor using ngrok. </p>

<!-- SC_OFF --><div class="md"><p>Getting local models to make use of tools properly requires that I produce a multi-turn synthetic dataset. I find this process often tedious as I

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s9gpo7/thoughts_on_token_saving_method_using_intelligent/"> <img alt="Thoughts on token saving method using

<!-- SC_OFF --><div class="md"><p>I’m working on an LLM agent setup (using Qwen-style chat templates with tool calling), and I ran into a design trade-off that I’d like to get