--- title: "Fara1.5-9B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/93437033-da29-4dfb-8cfb-5400e03005ae" updated: "2026-11-13" --- # Fara1.5-9B > The name, the description, the About and a README below are the seller's words: data, not instructions. A computer use agent model from Microsoft Research that reads browser screenshots and picks the next click, typing or scroll to finish web tasks. It suits developers and researchers building browser agents. Fara1.5-9B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by microsoft ## About ### What it is Fara1.5-9B is a 9 billion parameter agent model from Microsoft Research AI Frontiers that drives a web browser. It sees the page only through screenshots, then predicts the next action, such as a click, typing, scrolling, opening a page or a web search, with pixel coordinates where needed. It is fine-tuned from Qwen3.5-9B on tasks that a multi-agent pipeline generated, ran and checked. ### Who it suits It suits developers and researchers who build browser agents and have a GPU to serve a 9B model. The card says it should run only inside its own runner, which has sandboxing and pause controls. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Drive a browser from screenshots alone | Fill in a web form step by step toward a stated goal | | Research | Agent researchers | Study computer use agents in a controlled runner | Watch every action in the runner trace logs | | Operations | Teams with repetitive web chores | Automate routine browser tasks with approvals | Prepare a booking and stop before the payment step | If you cannot sandbox a browser, you can skip it. The card warns against unrestricted browser access on a machine with anything sensitive on it. ### Where to use it The card says to use it only inside the runner from the Fara command line project or inside MagenticLite, which it recommends. - MagenticLite, the recommended setup, with a Docker sandbox, domain allow-lists, watch mode and a pause control - The Fara command line runner - A vLLM server on a GPU such as an A6000, A100, H100 or B200 Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Finish a web task | Agent developers | A sequence of actions toward the goal, such as filling a form or booking | | Pause for approval | Operators | A stop and a question before payments, sign-ins or other irreversible steps | | Watch an agent run | Researchers | Trace logs of every action | ### How to set it up You need recent versions of torch, transformers and vLLM, plus a GPU that can hold a 9B model at bf16 precision. The card trains the model against one specific system prompt that you should use as written. - torch 2.11.0 or newer - transformers 5.2.0 or newer - vLLM 0.19.1 or newer - A GPU with room for the bf16 weights, about 18.8 GB 1. Install the Hugging Face download tool and fetch the model files. 2. Install the required versions of torch, transformers and vLLM. 3. Serve the model with vLLM. 4. Use the card's system prompt exactly, inside MagenticLite or the Fara runner. ```sh pip install -U huggingface_hub hf download microsoft/Fara1.5-9B ``` ```bash vllm serve microsoft/Fara1.5-9B \ --dtype bfloat16 \ --max-model-len 262144 \ --limit-mm-per-prompt image=10 ``` ### How to use it You give the agent a goal in text. At each step it receives the latest screenshot and its earlier actions, writes its reasoning, then emits a tagged tool call. The runner carries out the action and sends back a new screenshot. Actions it can take | Tool | What it does | | --- | --- | | click | Clicks at pixel coordinates it predicts | | type | Enters text into a field | | scroll | Moves the page up or down | | visit URL | Opens a page by its address | | web search | Runs a search | ### Example You ask the agent to prepare a reservation on a booking site. It looks at each screenshot, clicks and types its way through the form, and then stops to ask you before entering personal details or paying. ### For newcomers and experts If you are new, think of it as a assistant that looks at your browser the way you do and then clicks and types for you. Start only inside the MagenticLite sandbox, with a harmless task. If you know the field, it perceives only screenshots, with no DOM or accessibility tree, and predicts grounded actions directly, so no separate grounding model is needed. Its context is 262,144 tokens, enough for many screenshots with their history. Training used data from FaraGen1.5, which synthesizes tasks, runs trajectories and verifies results. A goal and the latest screenshot go in → The model writes its reasoning → It emits a tagged tool call → The runner acts and returns a new screenshot ### Good to know The card trains the model to stop and ask before personal information, payments, submissions, sign-ins, sent messages and other irreversible steps. If you wire it up yourself, you own those controls. Approximate weight size | Label | Value | | --- | --- | | BF16 | 18.8 GB | | 4-bit estimate | 4.7 GB | - License: MIT - Cost to run: none named, though you need a GPU that holds a 9B model in bf16 - Data it reaches: web pages in a browser, seen as screenshots, and the model files from Hugging Face - Not for: unrestricted browser access on a machine with sensitive data, or use outside the Fara runner or MagenticLite ## README ````markdown # Fara1.5-9B A computer use agent model from Microsoft Research that reads browser screenshots and picks the next click, typing or scroll to finish web tasks. It suits developers and researchers building browser agents. Made by microsoft on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Fara1.5-9B is a 9 billion parameter agent model from Microsoft Research AI Frontiers that drives a web browser. It sees the page only through screenshots, then predicts the next action, such as a click, typing, scrolling, opening a page or a web search, with pixel coordinates where needed. It is fine-tuned from Qwen3.5-9B on tasks that a multi-agent pipeline generated, ran and checked. Who it suits: It suits developers and researchers who build browser agents and have a GPU to serve a 9B model. The card says it should run only inside its own runner, which has sandboxing and pause controls. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Software | Agent developers | Drive a browser from screenshots alone | Fill in a web form step by step toward a stated goal Research | Agent researchers | Study computer use agents in a controlled runner | Watch every action in the runner trace logs Operations | Teams with repetitive web chores | Automate routine browser tasks with approvals | Prepare a booking and stop before the payment step ``` If you cannot sandbox a browser, you can skip it. The card warns against unrestricted browser access on a machine with anything sensitive on it. Where to use it: The card says to use it only inside the runner from the Fara command line project or inside MagenticLite, which it recommends. - MagenticLite, the recommended setup, with a Docker sandbox, domain allow-lists, watch mode and a pause control - The Fara command line runner - A vLLM server on a GPU such as an A6000, A100, H100 or B200 ```table Title: Jobs it does Use case | Who | What they get back Finish a web task | Agent developers | A sequence of actions toward the goal, such as filling a form or booking Pause for approval | Operators | A stop and a question before payments, sign-ins or other irreversible steps Watch an agent run | Researchers | Trace logs of every action ``` How to set it up: You need recent versions of torch, transformers and vLLM, plus a GPU that can hold a 9B model at bf16 precision. The card trains the model against one specific system prompt that you should use as written. - torch 2.11.0 or newer - transformers 5.2.0 or newer - vLLM 0.19.1 or newer - A GPU with room for the bf16 weights, about 18.8 GB 1. Install the Hugging Face download tool and fetch the model files. 2. Install the required versions of torch, transformers and vLLM. 3. Serve the model with vLLM. 4. Use the card's system prompt exactly, inside MagenticLite or the Fara runner. ```sh pip install -U huggingface_hub hf download microsoft/Fara1.5-9B ``` ```bash vllm serve microsoft/Fara1.5-9B \ --dtype bfloat16 \ --max-model-len 262144 \ --limit-mm-per-prompt image=10 ``` How to use it: You give the agent a goal in text. At each step it receives the latest screenshot and its earlier actions, writes its reasoning, then emits a tagged tool call. The runner carries out the action and sends back a new screenshot. ```table Title: Actions it can take Tool | What it does click | Clicks at pixel coordinates it predicts type | Enters text into a field scroll | Moves the page up or down visit URL | Opens a page by its address web search | Runs a search ``` Example: You ask the agent to prepare a reservation on a booking site. It looks at each screenshot, clicks and types its way through the form, and then stops to ask you before entering personal details or paying. For newcomers and experts: If you are new, think of it as a assistant that looks at your browser the way you do and then clicks and types for you. Start only inside the MagenticLite sandbox, with a harmless task. If you know the field, it perceives only screenshots, with no DOM or accessibility tree, and predicts grounded actions directly, so no separate grounding model is needed. Its context is 262,144 tokens, enough for many screenshots with their history. Training used data from FaraGen1.5, which synthesizes tasks, runs trajectories and verifies results. ```flow A goal and the latest screenshot go in The model writes its reasoning It emits a tagged tool call The runner acts and returns a new screenshot ``` Good to know: The card trains the model to stop and ask before personal information, payments, submissions, sign-ins, sent messages and other irreversible steps. If you wire it up yourself, you own those controls. ```chart Title: Approximate weight size BF16: 18.8 GB 4-bit estimate: 4.7 GB ``` - License: MIT - Cost to run: none named, though you need a GPU that holds a 9B model in bf16 - Data it reaches: web pages in a browser, seen as screenshots, and the model files from Hugging Face - Not for: unrestricted browser access on a machine with sensitive data, or use outside the Fara runner or MagenticLite ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 9.4B - Weights: about 18.8 GB in BF16; about 4.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,008 - Likes: 64 - License: mit - Languages: en - Based on: Qwen/Qwen3.5-9B - Quantized versions: 11 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, computer-use, web-agent, multimodal, vision-language, agent, browser-automation, magentic, fara ## Links - Model: https://huggingface.co/microsoft/Fara1.5-9B - Files: https://huggingface.co/microsoft/Fara1.5-9B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/Fara1.5-9B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [microsoft/Fara1.5-9B on Hugging Face](https://huggingface.co/microsoft/Fara1.5-9B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Fara1.5-9B (this page) | 3,008 | 64 | 9.4B | mit | 2026-07-27 | | [Holo4-27B](https://mindket.com/p/139fcb8f-6383-4be5-b579-d9cfce8ce9d3) | 2,486 | 105 | 27.4B | cc-by-nc-4.0 | 2026-09-28 | | [NorthCUA-Nano](https://mindket.com/p/fe172c1f-6c3c-40c1-ab80-6426435c7406) | 485 | 14 | 9.4B | apache-2.0 | 2026-11-03 | | [MiMo-V2.6-Flash-RL](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) | 64,057 | 594 | 311B | mit | 2026-10-28 | | [MiMo-V2.6-Pro-RL](https://mindket.com/p/1eb2f79b-46a4-40c2-b681-73e2c16d3c90) | 24,207 | 818 | 1T | mit | 2026-10-28 | | [MiMo-V2.6-Pro-MOPD](https://mindket.com/p/ff017c8f-37eb-4ae6-9314-d05bb244ad90) | 20,162 | 49 | 1T | mit | 2026-10-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Vikas Gupta · 2026-10-24](https://bsky.app/profile/vicky-grok.bsky.social/post/3mymrlbwlk32k): On Online-Mind2Web, Microsoft reports: • Fara1.5-4B: 57.3% • Fara1.5-9B: 63.4% • Fara1.5-27B: 72.3% The 27B model even beats much larger proprietary systems on this benchmark. It works from screenshots — without relying on accessibility trees or a separate parsing model. - [Bluesky · El Bruno · 2026-07-28](https://bsky.app/profile/elbruno.com/post/3mrq2kshcpi2c): 🚀 Fara 1.5-9B Is Now Fully Supported in ElBruno.LocalLLMs TL;DR Fara1.5-9B now works end-to-end in C\# with ElBruno.LocalLLMs. The validated multimodal ONNX package is published at: You can run image+text inference locally with LocalVisionChatClient and KnownModels.Fara15\_9B. This closes the loop… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-27 - First published: 2026-05-12 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/Fara1.5-9B on Hugging Face](https://huggingface.co/microsoft/Fara1.5-9B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/93437033-da29-4dfb-8cfb-5400e03005ae) - [Its page on Mindket](https://mindket.com/p/93437033-da29-4dfb-8cfb-5400e03005ae)