# LFM 2.5 Agentic Model for Tool Calling

> Source: <https://www.dotnetperls.com/2026_7_29_lfm-25-agentic-model-tool-calling>
> Published: 2026-07-29 07:00:00+00:00

Large and powerful **LLMs** are great, but sometimes a **smaller**, **local model** is sufficient. For example, with an MCP server, a small local model can call tools and be used to accomplish things. I continued to search for small agentic models and found **LFM 2.5**.

This is a model created by **Liquid AI**, an American company, and it is focused on **agentic tool calling**. I downloaded the `LFM2.5-8B-A1B-UD-Q4_K_XL.gguf`

file and tested in `llama-cpp`

.

Basically this model is focused on **tool calling** exclusively. This means:

• For complex analysis of text, LFM is not a good choice.

• For trying to create a customer service AI system, where users indicate what their problem is, and the model routes them to the appropriate MCP tools, it is a good choice.

I found that with a few more MCP server tools, I could get LFM 2.5 to **find available tools** and then **call them** based on my messages. The model has 8 billion parameters, but only 1 billion active, so it is **extremely fast**—I get nearly 200 tokens per second with a GPU, and it even works acceptably without a GPU. I have found LFM 2.5 to be an excellent agentic tool calling model, but beyond that, a larger model would be needed.