microsoft/Fara1.5-9B
452.8k
1---2license: mit3library_name: transformers4pipeline_tag: image-text-to-text5language:6- en7base_model:8- Qwen/Qwen3.5-9B9tags:10- computer-use11- cua12- web-agent13- multimodal14- vision-language15- agent16- browser-automation17- magentic18- fara19---20 21# Fara1.5-9B22 23[](https://aka.ms/fara1.5)24[](https://github.com/microsoft/fara)25[](https://huggingface.co/papers/2606.20785)26[](https://aka.ms/fara1.5-9B-foundry)27 28 29Fara1.5-9B 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.30 31The 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).32 33Fara1.5-9B is supervised fine-tuned from Qwen3.5-9B 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.34 35Please use only with the harness developped in [Github/fara CLI](https://github.com/microsoft/fara) or [Magentic-Lite](https://github.com/microsoft/magentic-ui).36 37It's co-designed with **MagenticLite**, and that's the recommended deployment for both research and production.38 39## Highlights40 41- **End-to-end web task completion.** Fills forms, books reservations, applies for jobs, plans trips, runs shopping carts. Not just clicking around — sequencing actions toward a goal.42- **Vision-only perception.** Operates on screenshots alone, no DOM access required. Matches the input modality available to a human user.43- **Coordinate-grounded actions.** Predicts pixel-level click and drag targets directly. No separate grounding model needed.44- **Critical-points safety design.** Trained to stop and ask before personal info entry, payments, submissions, sign-ins, sending messages, or other irreversible actions — even if it could technically continue.45- **262K context.** Long enough for multi-screenshot trajectories with full action history.46- **On-device viable.** 9B parameters means it runs on a single A100/H100/B200 with room for the screenshot history.47 48## Model Details49 50| | |51|---|---|52| **Developer** | Microsoft Research AI Frontiers |53| **Architecture** | Multimodal decoder-only LM (image + text → text) |54| **Parameters** | 9B |55| **Context length** | 262,144 tokens |56| **Inputs** | User goal (text), current screenshot(s), prior agent thoughts and actions |57| **Outputs** | Chain-of-thought block followed by a tool-call block (XML-tagged) |58| **Training period** | January 2026 – April 2026 |59| **Training compute** | 32 × NVIDIA B200, 4 days |60| **Release date** | 21 May 2026 |61| **License** | MIT |62| **Base model** | Qwen3.5-9B |63 64## Recommended Deployment: MagenticLite65 66The safest way to run Fara1.5-9B is inside **MagenticLite**, which provides:67 68- **Sandboxing** — the browser runs in a Docker container with no access to host files or environment variables69- **Allow-lists** — restrict navigation to a user-specified set of domains70- **Watch-mode** — real-time monitoring of every action with full trace logs71- **Pause** — immediate halt of agent activity at any point72 73If you integrate Fara1.5-9B directly, you're responsible for these controls. Don't run this model with unrestricted browser access on a machine that has anything sensitive on it.74 75## Quickstart76 77### Requirements78 79- `torch >= 2.11.0`80- `transformers >= 5.2.0`81- `vllm >= 0.19.1`82- A GPU with enough memory for a 9B model in bf16 (A6000, A100, H100, and B200 have been tested)83 84### Serve with vLLM85 86```bash87vllm serve microsoft/Fara1.5-9B \88 --dtype bfloat16 \89 --max-model-len 262144 \90 --limit-mm-per-prompt image=1091```92 93### System prompt94 95Fara1.5-9B is trained against a specific system prompt. Use it verbatim for best results:96 97```text98You are Fara, a computer use agent (CUA) specialized for web browsers. You are developed by Microsoft AI Frontiers. You assist users with completing and automating tasks that require the use of a web browser.99 100The model was trained in the timeframe of January - April 2026. You can effectively perform tasks even beyond this range by accessing the web browser and using the latest information on the live web. But your knowledge cutoff is limited to early 2026, so you may not be aware of events or developments that occurred after that time, without explicitly browsing and searching for latest information on the web.101 102This edition of the model was trained using SFT on top of Qwen3.5-9B, using a synthetic data mixture generated and developed by Microsoft AI Frontiers.103 104A critical point is a situation where we must pause and request information or confirmation from the user before proceeding. There are three types:105 106Case 1: Missing User Information — The task requires personal information that the user has not provided (e.g., email, phone number, address, payment details). Never fabricate or assume personal information. Fill in only what the user has explicitly provided, then pause and ask for any missing required fields.107 108Case 2: Underspecified Task — The task description is ambiguous or missing details needed to make a decision at the current step. Pause and ask for clarification.109 110Case 3: Irreversible Action — We are about to perform an action that cannot be undone (e.g., submitting a form, completing a purchase, sending a message, deleting data). If the user explicitly authorized the action, proceed. Otherwise, stop and ask for confirmation.111 112Only stop at a critical point if (1) required information is missing, (2) the task is ambiguous, OR (3) an irreversible action lacks explicit user authorization.113```114 115The full system prompt, including the complete `computer_use` tool schema, ships with the model in MagenticLite.116 117### Tool schema118 119Fara1.5-9B emits actions as `<tool_call>...</tool_call>` XML blocks containing a JSON object that calls the `computer_use` function. Supported actions:120 121| Action | Purpose |122|---|---|123| `left_click`, `right_click`, `double_click`, `triple_click` | Mouse clicks at `(x, y)` |124| `mouse_move`, `left_click_drag` | Cursor positioning and drag |125| `type`, `key` | Keyboard input |126| `scroll`, `hscroll` | Page scrolling |127| `visit_url`, `history_back` | Browser navigation |128| `web_search` | Search query |129| `pause_and_memorize_fact` | Persist a fact across the trajectory |130| `ask_user_question` | Surface a clarifying question to the user |131| `wait` | Sleep for N seconds |132| `terminate` | End the task with a final answer |133 134The screen resolution Fara is most commonly trained with is **1440×900**. Match this in your sandbox for the most reliable grounding.135 136### Minimal agent loop137 138Note: We strongly recommend to use only with the harness developped in [Github/fara](https://github.com/microsoft/fara) or [Magentic-Lite](https://github.com/microsoft/magentic-ui).139 140```python141# Pseudocode for a single-step interaction. Use vLLM's OpenAI-compatible142# endpoint and pass the screenshot as an image content part.143 144screenshot_b64 = capture_browser_screenshot() # base64 PNG145 146messages = [147 {"role": "system", "content": FARA_SYSTEM_PROMPT},148 {"role": "user", "content": [149 {"type": "text", "text": "Book a table for 2 at a sushi place in Sunnyvale for Friday 7pm."},150 {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{screenshot_b64}"}},151 ]},152]153 154response = client.chat.completions.create(155 model="microsoft/Fara1.5-9B",156 messages=messages,157 temperature=0.0,158 max_tokens=2048,159)160 161# Parse the assistant's reply: it will contain chain-of-thought text162# followed by <tool_call>{"name": "computer_use", "arguments": {...}}</tool_call>.163# Execute the action in your sandboxed browser, capture the new screenshot,164# append the assistant turn and a tool/observation turn, and loop until165# the model emits action="terminate". We only keep the most recent 3 screenshots166# in the chat history.167```168 169A reference implementation of the full agent loop, including screenshot capture and Docker sandboxing, is in MagenticLite.170 171## Critical Points: Safety by Design172 173Fara1.5-9B is trained to **pause and ask the user** at three types of critical points:174 1751. **Missing user information.** If the task needs personal data (name, email, phone, address, payment) the user hasn't provided, fill in what's available and stop to ask for the rest. Never fabricate.1762. **Underspecified tasks.** If the goal is ambiguous at the current decision point (e.g., "book me a flight" with no destination), stop and clarify.1773. **Irreversible actions.** Submitting forms, completing purchases, sending messages, deleting data — stop unless the user explicitly authorized the action upfront.178 179Concrete actions Fara is trained to stop on without explicit authorization:180 181- Entering personal information (name, email, phone)182- Entering credit card or shipping/billing details183- Completing purchases or bookings184- Making phone calls185- Sending emails186- Submitting job applications187- Signing into accounts188 189If the user grants authorization, the model proceeds. The principle is that costly, irreversible actions require a human in the loop.190 191## Evaluation192 193We evaluate Fara1.5-9B on end-to-end web agent benchmarks. For WebTailBench, we report outcome success.194 195| Model | [WebVoyager](https://github.com/MinorJerry/WebVoyager) | [Online-Mind2Web](https://huggingface.co/datasets/osunlp/Online-Mind2Web) | [WebTailBench](https://huggingface.co/datasets/microsoft/WebTailBench) |196| ----------- | -----------------------------------------------------: | ------------------------------------------------------------------------: | ---------------------------------------------------------------------: |197| Fara1.5-4B | 80.8 | 57.3 | 27.4 |198| Fara1.5-9B | 86.6 | 63.4 | 32.3 |199| Fara1.5-27B | 89.3 | 72.3 | 40.2 |200 201 202## Training203 204### Approach205 206Supervised Fine-Tuning on top of Qwen3.5-9B. Training mix is generated by FaraGen1.5, our multi-agent data pipeline, supplemented with curated public datasets for grounding and UI understanding.207 208### Data sources209 210- **Synthetic trajectories** — Tasks seeded from URLs sampled from a large web index and from open-source seed datasets (e.g., Mind2Web). A multi-agent system attempts each task while recording screenshots, thoughts, and actions. A verifier agent then keeps only successful trajectories.211- **Grounding** — Curated datasets for predicting actions and pixel coordinates from screenshots, with images, text, and bounding boxes.212- **UI understanding** — Visual question answering, captioning, and OCR over web page screenshots collected by the pipeline.213- **Safety and instruction following** — Refusal data covering harmful or unsafe tasks the model should decline.214 215### Scale216 217- Approximately 1 billion text tokens218- Less than 1 billion training images219- Latest data acquisition: March 20, 2026220- Training start: January 2026221 222The corpus is static. Future updates will ship as separately versioned models with their own model cards.223 224## Intended Use and Limitations225 226### Primary use cases227 228Automating repetitive web tasks: filling forms, shopping, booking travel, restaurant reservations, information seeking, account workflows. Fara1.5-9B can also serve as a grounding model for other agents that need pixel-accurate action prediction.229 230### Out of scope231 232- Languages other than English (training data is English-only)233- High-stakes domains (legal, health, financial advice) where inaccurate actions could cause harm234- Allocation decisions affecting legal status, housing, employment, or credit235- Unsandboxed deployments with access to sensitive accounts or files236- Commercial or real-world production use without additional testing and safeguards237 238### Known limitations239 240- **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 elements241- **Multi-step trajectories accumulate error** — a misclick early in a sequence can compound242- **Run-to-run variance** on multi-turn tasks is non-trivial; benchmark numbers are averaged over multiple runs243- The model can hallucinate page state or misattribute information from earlier screenshots244 245## Responsible AI Considerations246 247Computer use is a powerful capability. An agent that can click, type, and submit in a real browser can also do those things wrong. We strongly recommend:248 249- **Human-in-the-loop monitoring** of Fara's actions on the live web with a fast way to halt execution250- **Sandboxed execution** — run Fara in an isolated container with no access to host files, environment variables, or sensitive credentials251- **Allow-listed or block-listed browsing** — restrict the agent's reachable surface to limit exposure to malicious pages252- **No credential or PII sharing** with the agent unless absolutely required and the user has authorized it253- **Output verification** — Fara can hallucinate, misattribute sources, or be misled by deceptive content; verify before acting on its outputs254 255### Safety evaluation256 257Fara1.5-9B was evaluated via automated red-teaming on Azure. Coverage included groundedness, jailbreak resistance, harmful content (hate, violence, self-harm), and copyright violations. Safety post-training includes both refusal data for malicious tasks and the critical-points framework described above.258 259### Known model risks260 261Like all language models, Fara1.5-9B can produce unfair, unreliable, or offensive outputs. Specific concerns:262 263- **Quality of service** varies across English dialects and is worse for non-English content264- **Representation harms** may persist despite safety post-training due to base-model biases265- **Information reliability** — generated content may be inaccurate or outdated266- **Prompt injection** — Fara reads page content, and adversarial content on a page may attempt to redirect its behavior267 268Developers deploying Fara1.5-9B should apply responsible AI best practices and ensure compliance with applicable laws and regulations. Using safety services like [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) is recommended.269 270## License271 272Released under the **MIT License**.273 274## Contact275 276For information requests under the EU AI Act and related inquiries: **MSFTAIActRequest@microsoft.com**277 278Authorized representative: Microsoft Ireland Operations Limited, 70 Sir John Rogerson's Quay, Dublin 2, D02 R296, Ireland.