Fara 7B: An Efficient Agentic Model for Computer Use Official Microsoft Blog Technical Report Paper Github Try Fara 7B on Microsoft Foundry Model Summary Developer: Microsoft Research Description: 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. Model Architecture: Multimodal decoder only language model that takes an image (screenshot) + text context. It directly predicts thoughts and actions with grounded arguments. Current production baselines leverage Qwen 2.5 VL (7B). Parameters: 7 Billion Inputs: User goal (text), current screenshot(s), history of previous outputs (thoughts + actions text) from the agent. Context Length: 128k Outputs: Generated text in response to the input, with a chain of thought block followed by a tool call block to indicate the action. GPUs: 64 H100s Training Time: 2.5 days Public Data Summary: N/A Dates: Trained between 26th October 2025 to 29th October 2025 Status: Static model tr…
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