Fara-7B by Microsoft: An agentic small language model designed for computer use

Summary

Fara-7B: An Efficient Agentic Model for Computer Use Overview Fara-7B is Microsoft's first agentic small language model (SLM) designed specifically for computer use. With only 7 billion parameters, Fara-7B is an ultra-compact Computer Use Agent (CUA) that achieves state-of-the-art performance within its size class and is competitive with larger, more resource-intensive agentic systems. Try Fara-7B locally as follows (see Installation for detailed instructions): # 1. Clone repository git clone https://github.com/microsoft/fara.git cd fara # 2. Setup environment python3 -m venv .venv source .venv/bin/activate pip install -e . playwright install Then in one process, host the model: vllm serve " microsoft/Fara-7B " --port 5000 --dtype auto Then you can iterative query it with: fara-cli --task " whats the weather in new york now " Hint: might need to do --tensor-parallel-size 2 with vllm command if you run out of memory What Makes Fara-7B Unique Unlike traditional chat models that generate text-based responses, Fara-7B leverages computer interfaces—mouse and keyboard—to perform multi-step tasks on behalf of users. The model: Operates visually by perceiving webpages and taking actions like scrolling, typing, and clicking on directly predicted coordinates by perceiving webpages and taking actions like scrolling, typing, and clicking on directly predicted coordinates Uses the same modalities as humans to interact with computers—no accessibility trees or separate parsing models required to interact with computers—no accessibility trees or separate parsing models required Enables on-device deployment due to its compact 7B parameter size, resulting in reduced latency and improved privacy as user data remains local due to its compact 7B parameter size, resulting in reduced latency and improved privacy as user data remains local Completes tasks efficiently, averaging only ~16 steps per task compared to ~41 for comparable models Fara-7B is trained using a novel synthetic data gen...