Why Consumer LLMs Struggle with Agentic Actions (And How We Fix It)

When you ask GPT-4 to use a calculator, search the web, or run code—it does so seamlessly. Ask a smaller consumer LLM to do the same, and it often hallucinates the result or ignores the tool entirely.

The gap is not about model size alone. It is about training data.

The Tool-Use Gap

Foundation models like Claude and GPT-4 were trained on massive corpora of tool interactions:

• API documentation
• Function calling logs
• Execution traces

These datasets teach the model when to use tools, which tool to use, and how to parse the results.

Consumer models—optimized for size and speed—never had this advantage. They are great at text generation but struggle with action-oriented tasks.

What We Are Building

Open datasets specifically for tool-use training:

• Function call trajectories: Real-world examples of LLM → tool → result
• Tool selection reasoning: When and why a particular tool was chosen
• Error recovery: How models handle tool failures

The Impact

With better training data, consumer models can:

• Execute multi-step workflows reliably
• Use APIs and plugins effectively
• Maintain context across tool invocations

This lowers the barrier to building powerful AI agents—anyone can run them locally or cheaply in the cloud.

Get Involved

We need contributors to build these datasets. Whether you:

• Have tool-use logs to share
• Want to help annotate data
• Are building agentic apps and want better models

Join us. Together, we can close the gap.