Model Releases
microsoft/Fara1.5-27B · Hugging Face
Fara1.5-27B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting struc
Fara1.5-27B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls — click, type, scroll, visit URL, web search, and so on — to complete tasks end-to-end. The model is vision-only at perception time: it sees the browser through screenshots, not the DOM or accessibility tree. Internal reasoning and trajectory history are tracked as text. Given the latest screenshot and prior actions, it predicts the next action with grounded arguments (e.g., pixel coordinates for a click). Fara1.5-27B is supervised fine-tuned from Qwen3.5-27B on data generated by FaraGen1.5, our multi-agent pipeline that synthesizes web tasks, executes trajectories to solve them, and verifies the results before training. It's co-designed with MagenticLite, and that's the recommended deployment for both research and production. Primary use cases Automating repetitive web tasks: filling forms, shopping, booking travel, restaurant reservations, information seeking, account workflows. Fara1.5-27B can also serve as a grounding model for other agents that need pixel-accurate action prediction. Out of scope Languages other than English (training data is English-only) High-stakes domains (legal, health, financial advice) where inaccurate actions could cause harm Allocation decisions affecting legal status, housing, employment, or credit Unsandboxed deployments with access to sensitive accounts or files Commercial or real-world production use without additional testing and safeguards Known limitations Vision-only perception means the model can be misled by deceptive or low-quality page rendering, prompt injections embedded in page content, or visual ambiguity in UI elements Multi-step trajectories accumulate error — a misclick early in a sequence can compound Run-to-run variance on multi-turn tasks is non-trivial; benchmark numbers are averaged over multiple runs The model can hallucinate page state or misattribute information from earlier screenshots Additional Models: (I don't see 9B model on HF even though model cards mentions 9B, Added below) https://huggingface.co/microsoft/Fara1.5-4B https://huggingface.co/microsoft/Fara1.5-9B submitted by /u/pmttyji [link] [comments]
Related
- WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
- HUGGING FACE JUST AUTOMATED THEIR ENTIRE POST-TRAINING TEAM WITH AN AGENT. It reads papers, runs GPU experiments, iterates, and builds resea…
- Agent Data Injection Attacks are Realistic Threats to AI Agents
- PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agents
- DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions
Source: r/LocalLLaMA | 2026-07-22