From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.textSubject:

Fara-7B

Date: 2026-11-15Commit: 4fca9cdSigned: 2DC28CC7B26A283B ✓

Image text to text model with 8.3B parameters. Safetensors weights, 16.6 GB in all. Published by microsoft under MIT.

8.3B params16.6GBFara-7Bsafetensorspermissive
FactsParams8.3BSize16.6 GBFormatssafetensorsTaskimage text to textLicenseMITFrom sourcepermissive
$ fw get microsoft/Fara-7B
# manifest sha256 0519be5798b9… · 16.6 GB · 18 files
$ fw verify ./Fara-7B microsoft/Fara-7B
From sourceHosted (signed)microsoft/Fara-7BLast check: 2026-11-15 03:24 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Runs withAIBrixBentoMLCTranslate2ExLlamaV2ExLlamaV3exofastllmFlashInferKTransformersLitServeLMCacheLMDeployLocalAImistral.rsMLC LLMMooncakeSGLangText Generation WebUItinygradTransformersTriton Inference ServervLLMXinferenceFine-tune withAccelerateAlignment HandbookAxolotlColossal-AIDeepSpeedEasyR1FlashAttentionH2O LLM StudioJAXKerasLiger KernelLitGPTLLaMA-FactoryMegatron-LMms-swiftnanotronOLMo-coreOpenRLHFOumiPEFTPyTorch Lightningsd-scriptsSimpleTunerTorchTitantorchtuneTransformer EngineTRLUnslothverlConvert withAQLMAutoRoundCore ML ToolsExecuTorchGPTQModelLLM CompressormergekitNeural CompressorOliveONNXOptimum QuantoOptimumsafetensorsTensorRT Model OptimizertorchaoEvaluate withAutoevalsDeepEvalEvaluateHELMLightEvallm-evaluation-harnessOpenCompassOpikRagassimple-evalsSWE-benchTruLensWeaveScan withAgentDojoAI Fairness 360DeepTeamDetoxifyFairlearngarakGiskardGuardrailsMCP ScannerModelScanNeMo GuardrailspicklescanpromptfooWorks withAgent Development KitAG2Google AI Edge GalleryAiderany-llmAnythingLLMBAMLbrowser-useCAMELHugging Face Chat UIChatboxCherry StudioClineCodex CLICogneeCrewAIDeerFlowdocTRDSPyEasyOCREnchantedGemini CLIGitHub MCP ServergooseGPT ResearcherGradioGraphitiGuidanceHollamaInstructorKilo CodeLangflowLangGraphLiteLLMLlamaIndexLLMLocalAGILoLLMs WebUIMCP Python SDKMem0MetaGPTNextChatOllama JavaScriptOllama PythonolmOCROpenAI Agents SDKopencodeOpenHandsotermOutlinesPage AssistPlaywright MCPPortkey AI GatewayPrivateGPTQwen CodeRWKV RunnerSemantic KernelsmolagentsStrands AgentsSuryaVaneXGrammar

Overview

Size
16.6 GB
Weights
16.6 GB (safetensors)
Parameters
8.3B
Formats
safetensors
Task
image text to text
License
MIT (permissive)
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Publisher
microsoft
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Model card

From the publisher at commit 4fca9cd. Shown as plain text with links; images open at the publisher.

Fara-7B: An Efficient Agentic Model for Computer Use#

Update: We just released Fara1.5 which improves on Fara-7B dramatically and is available in three model sizes 4B, 9B and 27B!

Microsoft Hugging Face Dataset Foundry Github Paper

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 trained on public and private data

Release Date: November 24th, 2025

License: MIT

Model Dependencies: Qwen 2.5 VL

Additional Assets: N/A

Acceptable Use Policy: N/A


1. Model Overview#

Fara is a 7B Computer Use Agent (CUA) model specialized for taking actions on the web to accomplish high-level user tasks. Beyond understanding webpage layout and basic action mechanics, it plans and executes high-level goals like booking restaurants, applying for jobs, planning trips, and buying shopping lists. Its training relies on a large-scale, fully synthetic dataset of action trajectories generated and verified by a multi-agent pipeline.

Fara perceives browser inputs via screenshots, while internal reasoning and state history are recorded textually. Based on recent screenshots and a full history of actions, it predicts the next action with necessary arguments (e.g., coordinates for clicks).

1.1 Alignment Approach#

Fara-7B uses a robust post-training safety approach leveraging open-source and in-house synthetic datasets. It incorporates critical point recognition—situations requiring user permission or sensitive information—to safely halt actions. The model is trained to refuse harmful tasks and undergoes automated red teaming to assess risks, including grounding, jailbreaks, harmful content, and copyright violations.

1.2 Safeguards#

Fara-7B is trained to refuse tasks in categories that violate usage policy:

TypeDescriptionExamples
Illegal ActivitiesTasks requiring unlawful actionsTerrorism-related searches, piracy, unauthorized access, weapons creation
Deceptive TasksTasks misleading or impersonatingFake forms, fraudulent listings, phishing
High-Risk/Regulated DomainsTasks requiring professional oversightMedical, legal, financial advice or approvals
Harassment, Exploitation, HateTasks harming or discriminatingHarassment content, stalking, sexualizing minors
Unsafe Technical UseMisuse of automationLarge-scale scraping, spam, system disruption
MisinformationSpreading false claimsPublishing unverified claims
SexualErotic or pornographic tasksErotic roleplay, porn searches

Critical points where the agent stops include entering personal info, completing purchases, making calls, sending emails, submitting applications, and signing into accounts.


2. Usage#

Sample Usage#

You can try Fara-7B locally by setting up the environment and hosting the model. For full instructions, refer to the GitHub repository.

# 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

2.1 Primary Use Cases#
  • Automating web tasks such as shopping, booking travel, restaurant reservations, info-seeking, or account workflows.
  • Performs actions step-by-step using multimodal understanding from browser screenshots.
  • On-device execution provides privacy guarantees and lower latency.
2.2 Out-of-Scope Use Cases#
  • Model not evaluated for all downstream purposes; consider limitations of LLMs for accuracy, safety, and fairness.
  • Must adhere to applicable laws and regulations.
  • English-only support.
2.3 Distribution Channels#
  • Hugging Face
  • Azure AI Foundry
2.4 Input Formats#

Given the nature of the training data, always use the ChatML template with the following system prompt for inference:


System Prompt:

You are a web automation agent that performs actions on websites to fulfill user requests by calling various tools.

You should stop execution at Critical Points. A Critical Point occurs in tasks like:

  • Checkout
  • Book
  • Purchase
  • Call
  • Email
  • Order

A Critical Point requires the user's permission or personal/sensitive information (name, email, credit card, address, payment information, resume, etc.) to complete a transaction (purchase, reservation, sign-up, etc.), or to communicate as a human would (call, email, apply to a job, etc.).

Guideline: Solve the task as far as possible up until a Critical Point.

Examples:

  • If the task is to "call a restaurant to make a reservation," do not actually make the call. Instead, navigate to the restaurant's page and find the phone number.
  • If the task is to "order new size 12 running shoes," do not place the order. Instead, search for the right shoes that meet the criteria and add them to the cart.

Some tasks, like answering questions, may not encounter a Critical Point at all.


Function Signatures:

You are provided with function signatures within XML tags:

{
  "type": "function",
  "function": {
    "name": "computer_use",
    "description": "Use a mouse and keyboard to interact with a computer, and take screenshots.\
* This is an interface to a desktop GUI. You do not have access to a terminal or applications menu. You must click on desktop icons to start applications.\
* Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions. E.g. if you click on Firefox and a window doesn't open, try wait and taking another screenshot.\
* The screen's resolution is 1428x896.\
* Whenever you intend to move the cursor to click on an element like an icon, you should consult a screenshot to determine the coordinates of the element before moving the cursor.\
* If you tried clicking on a program or link but it failed to load, even after waiting, try adjusting your cursor position so that the tip of the cursor visually falls on the element that you want to click.\
* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked.\
* When a separate scrollable container prominently overlays the webpage, if you want to scroll within it, you typically need to mouse_move() over it first and then scroll().\
* If a popup window appears that you want to close, if left_click() on the 'X' or close button doesn't work, try key(keys=['Escape']) to close it.\
* On some search bars, when you type(), you may need to press_enter=False and instead separately call left_click() on the search button to submit the search query. This is especially true of search bars that have auto-suggest popups for e.g. locations\
* For calendar widgets, you usually need to left_click() on arrows to move between months and left_click() on dates to select them; type() is not typically used to input dates there.",
    "parameters": {
      "properties": {
        "action": {
          "description": "The action to perform. The available actions are:\
* key: Performs key down presses on the arguments passed in order, then performs key releases in reverse order. Includes 'Enter', 'Alt', 'Shift', 'Tab', 'Control', 'Backspace', 'Delete', 'Escape', 'ArrowUp', 'ArrowDown', 'ArrowLeft', 'ArrowRight', 'PageDown', 'PageUp', 'Shift', etc.\
* type: Type a string of text on the keyboard.\
* mouse_move: Move the cursor to a specified (x, y) pixel coordinate on the screen.\
* left_click: Click the left mouse button.\
* scroll: Performs a scroll of the mouse scroll wheel.\
* visit_url: Visit a specified URL.\
* web_search: Perform a web search with a specified query.\
* history_back: Go back to the previous page in the browser history.\
* pause_and_memorize_fact: Pause and memorize a fact for future reference.\
* wait: Wait specified seconds for the change to happen.\
* terminate: Terminate the current task and report its completion status.",
          "enum": ["key", "type", "mouse_move", "left_click", "scroll", "visit_url", "web_search", "history_back", "pause_and_memorize_fact", "wait", "terminate"],
          "type": "string"
        },
        "keys": {"description": "Required only by action=key.", "type": "array"},
        "text": {"description": "Required only by action=type.", "type": "string"},
        "coordinate": {"description": "(x, y) coordinates for mouse actions. Required only by action=left_click, action=mouse_move, and action=type.", "type": "array"},
        "pixels": {"description": "Amount of scrolling. Positive = up, Negative = down. Required only by action=scroll.", "type": "number"},
        "url": {"description": "The URL to visit. Required only by action=visit_url.", "type": "string"},
        "query": {"description": "The query to search for. Required only by action=web_search.", "type": "string"},
        "fact": {"description": "The fact to remember for the future. Required only by action=pause_and_memorize_fact.", "type": "string"},
        "time": {"description": "Seconds to wait. Required only by action=wait.", "type": "number"},
        "status": {"description": "Status of the task. Required only by action=terminate.", "type": "string", "enum": ["success", "failure"]}
      },
      "required": ["action"],
      "type": "object"
    }
  }
}

For each function call, return a JSON object with the function name and arguments within XML tags:

{ "name": "<function-name>", "arguments": <args-json-object> }


- Function signatures provided for all actions (`key`, `type`, `mouse_move`, `left_click`, `scroll`, `visit_url`, `web_search`, `history_back`, `pause_and_memorize_fact`, `wait`, `terminate`).  


### 2.5 Technical Requirements & Integration

- Required packages: `torch >=2.7.1`, `transformers >=4.53.3`, `vllm >=0.10.0`  
- Tested on NVIDIA A6000, A100, H100 GPUs (Ubuntu 24.04.3 LTS)  
- Recommended on vLLM server with bf16 precision  
- Provided implementation via Magentic-UI in Docker sandbox for safe web execution  

### 2.6 Responsible AI Considerations

- English-only; other languages may have degraded performance  
- Potential stereotype reinforcement or inappropriate content  
- Verify outputs, especially in high-stakes or regulated domains  
- Misuse includes fraud, spam, malware generation  
- Use safety services like Azure AI Content Safety where possible  
- Recommended: human-in-the-loop, sandboxing, access control, output verification  

---

## 3. Data Overview

### 3.1 Training, Testing, Validation Datasets

- Multi-agent data generation pipeline produces synthetic trajectories from seed URLs and open-source tasks  
- Records screenshots, thoughts, action traces, and verification via verifier agents  
- Includes high-quality public datasets: image and text modalities  
- Specialized data: grounding, UI understanding (VQA, captioning, OCR), safety/refusal datasets  

---

## 4. Quality and Performance Evaluation
### Table: Online Agent Evaluation Results

| Model                        | Params | WebVoyager | Online-M2W | DeepShop | WebTailBench |
|------------------------------|--------|------------|------------|----------|---------------|
| **SoM Agents**               |        |            |            |          |               |
| SoM Agent (GPT-5)            | -      | 90.6       | 57.7       | 49.1     | 60.4          |
| SoM Agent (o3)               | -      | 79.3       | 55.4       | 49.7     | 52.7          |
| SoM Agent (GPT-4o)           | -      | 65.1       | 34.6       | 16.0     | 30.8          |
| GLM-4.1V-9B-Thinking         | 9B     | 66.8       | 33.9       | 32.0     | 22.4          |
| **Computer Use Models**      |        |            |            |          |               |
| OpenAI computer-use-preview  | -      | 70.9       | 42.9       | 24.7     | 25.7          |
| UI-TARS-1.5-7B               | 7B     | 66.4       | 31.3       | 11.6     | 19.5          |
| Fara-7B                      | 7B     | 73.5       | 34.1       | 26.2     | 38.4          |

The table reports task completion success rates on WebVoyager, Online-Mind2Web, DeepShop, and WebTailBench for both SoM agents and native computer-use agents.
Scores are averaged over 3 runs.

### 4.2 Safety Evaluation & Red-Teaming

- Post-training safety with critical point design  
- Red-teaming on Azure: grounding, jailbreaks, harmful content, copyright  

### Guidelines for Safe Use

- Human-in-the-loop monitoring recommended  
- Do not share sensitive data  
- Run in sandboxed environments  
- Limit internet access via allow-lists/block-lists  
- Avoid use in commercial, high-stakes, or regulated domains  

**Security Considerations:**  
- Automates interactions across websites, apps, OS; requires strict access control, sandboxing, and monitoring  


**Attribution:** Our model is based on Qwen 2.5 VL. Qwen 2.5 VL has an Apache 2.0 license. Fara-7B is released with an MIT License. Apache 2.0 and MIT are compatible licenses.


---

## Appendix: Benchmarks

| Benchmark | Link |
|-----------|------|
| WebVoyager | [MinorJerry/WebVoyager](https://huggingface.co/datasets/MinorJerry/WebVoyager) |
| Online-Mind2Web | [osunlp/Online-Mind2Web](https://huggingface.co/datasets/osunlp/Online-Mind2Web) |
| DeepShop | [DeepShop/DeepShop](https://huggingface.co/datasets/DeepShop/DeepShop) |
| WebTailBench | [microsoft/WebTailBench](https://huggingface.co/datasets/microsoft/WebTailBench) |
| ScreenSpot v1 | [rootsautomation/ScreenSpot](https://huggingface.co/datasets/rootsautomation/ScreenSpot) |
| ScreenSpot v2 | [Voxel51/ScreenSpot-v2](https://huggingface.co/datasets/Voxel51/ScreenSpot-v2) |
| AgentHarm | [ai-safety-institute/AgentHarm](https://huggingface.co/datasets/ai-safety-institute/AgentHarm) |

Files

18 files · 16.6 GBWeights 16.6 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 18 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md16.4 KBc55441f3f9ddae9d87ff5a138fb772fb0179ce2800cb6e143080fa681c513b3e
added_tokens.json605 B58b54bbe36fc752f79a24a271ef66a0a0830054b4dfad94bde757d851968060b
chat_template.jinja1.02 KBa0bc6f6fc7a29a80017a433e8f03a1cc1236e838a944a2d034295a60c4f2fddb
config.json2.49 KB66da8d32be7f31ed9aa94707e2c6fb9813a5744f61bfc4a73b68e5da06b1d642
generation_config.json214 Bfa2df5de2bac851a6affb6053d916cf8f1b8b7bf67d8603e73e51611b3119fb2
merges.txt1.67 MB8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
model-00001-of-00004.safetensors4.97 GBd293a0b9ab4add8829d3ac57ee88bc16121ca713bfcc0e76bc9e8aeae7057e89
model-00002-of-00004.safetensors4.99 GB8424c791566a077b69e589c2220972b1dc3584dea3d1409b9316d2d61f0d82e9
model-00003-of-00004.safetensors4.93 GB32d6e0f417c8c6aca30ac130d5728f28c3c2cbbf07933bed54a3712a59637442
model-00004-of-00004.safetensors1.69 GB7c0fe359fdfbf4d5d6a32f4024b91f8db77ebd94cabe16d7fe91bf6f357ba210
model.safetensors.index.json57.6 KB860981a0ea4d17f6c448c394a762d8d62d721599126da67b40deb04d94412798
preprocessor_config.json575 B549c158011407dfb750d9ec578047cf76f5bfe365cd0aa069a50137d3f98d9dd
special_tokens_map.json613 B76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd
tokenizer.json11.4 MB9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
tokenizer_config.json4.73 KBe04be4dd17e9f661bc487ba848a37f66a8f1adf2eef412f5d4ecb40cab4d39e7
video_preprocessor_config.json1.73 KBcaa66a5efec40232005736e255bef439afa9853d7b1249e925ba627fc60d2758
vocab.json2.78 MBca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
4fca9cdeef pinned now2026-11-15 03:24 UTC18 files · 16.6 GB

Use it

$ fw get microsoft/Fara-7B
# manifest sha256 0519be5798b9… · 16.6 GB · 18 files
$ fw verify ./Fara-7B microsoft/Fara-7B
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/microsoft/Fara-7B.json --dir ./Fara-7B $ python3 fw.py verify ./Fara-7B ./microsoft__Fara-7B.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/Fara-7B --revision 4fca9cdeeff278777751ec558257753b89cf078b --local-dir ./Fara-7B
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("microsoft/Fara-7B", revision="4fca9cdeeff278777751ec558257753b89cf078b") model = AutoModelForImageTextToText.from_pretrained("microsoft/Fara-7B", revision="4fca9cdeeff278777751ec558257753b89cf078b", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve microsoft/Fara-7B --revision 4fca9cdeeff278777751ec558257753b89cf078b

License

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MIT License
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Provenance

From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.textSubject: Fara-7BDate: 2026-11-15Commit: 4fca9cdSigned: 2DC28CC7B26A283B ✓
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0519be5798b9e0ab632db343bf44216ec4222772dd6248f880c227a8d2038651
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$ curl -O https://freeweights.allternit.com/manifests/microsoft/Fara-7B.json $ curl -O https://freeweights.allternit.com/manifests/microsoft/Fara-7B.json.minisig $ minisign -Vm Fara-7B.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

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Model tree

3 models in the catalog name this one as their parent.

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Pull, verify and run it in the Allternit app on Desktop, web or phone.

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Variants

3 quantized or adapter builds of Fara-7B. Each has its own license check and page.

From: laion <huggingface.co/laion>Subject: a2-rl-e2egit_large-50-32B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-large-15-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-v2-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_pymethods2test-large-80-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_unitsyn-python-large-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_mix_h2_language_proportional-65-8B