alt.freeweights.text / Fara-7B-Onnx
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.textSubject:

Fara-7B-Onnx

Date: 2026-11-15Commit: 065f45bSigned: 2DC28CC7B26A283B ✓

Image text to text model. Onnx weights, 36.9 GB in all. Published by onnx-community under MIT.

text36.9GBFara-7B-Onnxonnxpermissive
FactsParamsnot statedSize36.9 GBFormatsonnxTaskimage text to textLicenseMITFrom sourcepermissive
$ fw get onnx-community/Fara-7B-Onnx
# manifest sha256 861faf5392cc… · 36.9 GB · 55 files
$ fw verify ./Fara-7B-Onnx onnx-community/Fara-7B-Onnx
From sourceHosted (signed)onnx-community/Fara-7B-OnnxLast check: 2026-11-15 05:27 UTC

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

Quantized of Fara-7B.

Overview

Size
36.9 GB
Weights
2.98 KB (smallest of 16 ONNX variants)
Parameters
not stated
Formats
onnx
Task
image text to text
License
MIT (permissive)
Files
55, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.text
Lineage

Original release from the publisher.

Base model: microsoft/Fara-7B

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 05:27 UTCpassed

Model card

From the publisher at commit 065f45b. Shown as plain text with links; images open at the publisher.

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

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

55 files · 36.9 GBWeights 2.98 KB (smallest of 16 ONNX variants). Sizes and sha256 from the signed manifest.
Show all 55 files with their sha256
FileSizesha256
.gitattributes1.61 KB829a62b3edd20919c5f0ab873ad4f32b5b275d738d011b3237db6e269d810ffe
README.md16.3 KBa57289d5fdb5381e0da335ccc0fa0b1fc092b90245790d8be8d2d29dd651a591
chat_template.jinja1.02 KBa0bc6f6fc7a29a80017a433e8f03a1cc1236e838a944a2d034295a60c4f2fddb
config.json2.59 KB6274525e0adf6a4b625cb208400a1a904dc8cd4071a19c286bf770a2996402fe
cpu/chat_template.jinja1.02 KBa0bc6f6fc7a29a80017a433e8f03a1cc1236e838a944a2d034295a60c4f2fddb
cpu/config.json2.59 KB6274525e0adf6a4b625cb208400a1a904dc8cd4071a19c286bf770a2996402fe
cpu/embedding.onnx2.98 KB285a561d11f6ac4f0c60ad45b4458774419d62366b78706632b3e57eccad32b0
cpu/embedding.onnx.data290 MB7bfd201128cf6227f2215702552dc7f9cd077331d3eaa849eaa4dae57db7a4ae
cpu/genai_config.json2.75 KB152f39911476f0727ae68f24b1d33f0ab38346953fa6735182a9dbda12bcfd64
cpu/generation_config.json214 B1b41e9c71c73d195c09ce93df6e3bfc22558a9207820e511735d7509826bde37
cpu/model_config.json6.71 KB548d0a75d58ad48ea468c17250392e11243a79638513a9d9adaa33ec786fd8f6
cpu/processor_config.json1.57 KB0c5f3b6f98ed8302d63c40389aa88e7c842b4dde75358f7ce82d3c7db69ee5be
cpu/text.onnx812 KBaa4c5aea977a6334aa720945fa217fd597430b3d37b3f69aff1b279f28ea06dd
cpu/text.onnx.data4.42 GB9a168365d303422e8dcaf6a57d025a5b5cbd95188629a853881bd6998621f2e6
cpu/tokenizer.json11.4 MB3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
cpu/tokenizer_config.json422 B318916b1f1800ab046f3aac2cf51ae2a796719bcdc6aedb9fea82b7cdc642745
cpu/vision.onnx4.65 MBe2b9e88c9895381ceae294a1c8f874ef32ee633ab0061570fed94e0de9642d23
cpu/vision.onnx.data371 MB0144c090ddb8edafc15c1108871ee5b720f6a307110dcddb20a358a6a32e5bd9
embedding.onnx3.06 KB28bd742794bd3d688de1296d83386e3bacf43e914fe62f86a4ccf5b81859f40b
embedding.onnx.data1.09 GB8553af1eebe326f48b7b091ce433465127363d33ddc0af5a0062e9dade5a46c2
genai_config.json3.48 KB0bdea53f0768949a381a7451bf60f0eda11f47416d37ed3b320f45973b9d8de7
generation_config.json214 B1b41e9c71c73d195c09ce93df6e3bfc22558a9207820e511735d7509826bde37
model.onnx853 KBe92fcffc30794e34f55956a26f833f5f48c24fb518909cba8adc98336074b79e
model.onnx.data14.1 GBbaee557ad78b461d41c333f8682eac88f17f2da654c63f667eb138877854f6f4
model_config.json6.64 KB50723c584b73e0ad3cf23181b92decbd1c59a59fb243478575005b093e452936
onnxruntime/vitisai-int4/LICENSE1.08 KBfa8235e5b48faca34e3ca98cf4f694ef08bd216d28b58071a1f85b1d50cb814d
onnxruntime/vitisai-int4/chat_template.jinja1.02 KBa0bc6f6fc7a29a80017a433e8f03a1cc1236e838a944a2d034295a60c4f2fddb
onnxruntime/vitisai-int4/genai_config.json9.33 KBf8e29649aac55a0501eb2ae64d65de6f2ce403d18dfdbb0eb3a23fca1535cb33
onnxruntime/vitisai-int4/inference_model.json63 Be97491cae697926c5cd38482f7ffaf2b2a4d4aadd7289c0b3c18288e4caaa245
onnxruntime/vitisai-int4/processor_config.json1.46 KBbb046cd8384fc5daba0012b1494a3345356a4b3c35dcd3a246fb9663e365336e
onnxruntime/vitisai-int4/qwen2_5_vl_webcua.data3.34 GB32c3012080efde1b54a8ce684b9029c73da05ee3c676662742ad3669c1f1346a
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_ctx_512.onnx1.67 MB86fbda5abb4dfd5deee003fbdc9a5ed7c1006ee9e332498ac94c206d709d5052
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_ctx_512_epcontext.onnx65.7 MB773bae33c217b3dac0b8b412836b222b1dae672cb22f83788c96f594f4ea2589
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_ctx_512_epcontext.onnx_VITISAI.bin7.88 GBc87db64a52f790be973436ce69407848fe181be263d221dc815a1a21ede7298e
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_embeddings_w4a32.quant.onnx349 MB17e9f756271089b5af067f934543a3dcf6b6f510426265743fd5ed27ae5fda5c
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_itr_1.onnx1.66 MBf0e7b5fb6fd7ab572d1287de44bd35ee065ee16bbb8aa5d53b76f96138d824a5
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_itr_1_epcontext.onnx65.7 MBdbf0453a87e5038565d5039a0266706bd93ca256837fb6cf2905ad5fdc02970d
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_lm_head_w4a32.quant.onnx349 MB55d5283bd599ebc6fb742fa14e264e73c8106a2e5183140a84ecb562fbd9b0d4
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_attn_block.onnx3.95 MB20facb31a86b9d4e8fdb25f0eacd4fb5e0cdbec0bb43884dce47ad6d33db6301
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_attn_block_epcontext.onnx3.94 KB7a5b11e179ac56dc4fc1cbb1a23de1cf0a7deb44bd23f385d7ddee655287ad5c
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_attn_block_epcontext.onnx_VITISAI.bin1.98 GBaf3875182c384a073adb780c6c1dbae5abc6daa65b510712a4af3b6282e9dfd7
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_attn_blocks_w8a16_per_channel.quant.onnx.data924 MBf5d4b588a46737212ed11fa1b20d43d53e10a877f57d0ac3d0b0207f77d36692
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_patch_embed.onnx6.02 MB8ebc2de24de3e87be8c4aa148f1ca59e7bafcc6afeed2c0bcf35ecc40909eb20
onnxruntime/vitisai-int4/qwen2_5_vl_webcua_visual_patch_merger.onnx178 MB024c5792c6eab12c5ec3ddd0566b1e1ca0229c484f26c50e6ca87807232df45f
onnxruntime/vitisai-int4/tokenizer.json11.4 MB9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
onnxruntime/vitisai-int4/tokenizer_config.json4.69 KB0a04a9d7d4a62b28482bdfe726c122756de85714fb64166ace92ae75b8f57614
onnxruntime/vitisai-int4/wnd_idx.npy4.07 KB78628c71d254c9493c8de04a9979fe359f2850c2b5d537a5efa30fde31c6ada9
preprocessor_config.json575 B549c158011407dfb750d9ec578047cf76f5bfe365cd0aa069a50137d3f98d9dd
processor_config.json1.57 KB0c5f3b6f98ed8302d63c40389aa88e7c842b4dde75358f7ce82d3c7db69ee5be
tokenizer.json11.4 MB3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
tokenizer_config.json422 B318916b1f1800ab046f3aac2cf51ae2a796719bcdc6aedb9fea82b7cdc642745
video_preprocessor_config.json1.73 KBcaa66a5efec40232005736e255bef439afa9853d7b1249e925ba627fc60d2758
vision.onnx4.92 MB1a8c5c4c90f59b954a457b06b1287335c96d9185fd5bc88f4a115f2391e52733
vision.onnx.data1.35 GBd327a9d27b265afaee1b18d1ee94dc67a296c42292cdef0410648107cb635382
vocab.json2.78 MBca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
065f45b8a3 pinned now2026-11-15 05:27 UTC55 files · 36.9 GB

Use it

$ fw get onnx-community/Fara-7B-Onnx
# manifest sha256 861faf5392cc… · 36.9 GB · 55 files
$ fw verify ./Fara-7B-Onnx onnx-community/Fara-7B-Onnx
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/Fara-7B-Onnx.json --dir ./Fara-7B-Onnx $ python3 fw.py verify ./Fara-7B-Onnx ./onnx-community__Fara-7B-Onnx.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/Fara-7B-Onnx --revision 065f45b8a35e22536c8b415fd2d3e0798a7a0298 --local-dir ./Fara-7B-Onnx

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
a57289d5fdb5381e0da335ccc0fa0b1fc092b90245790d8be8d2d29dd651a591
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.textSubject: Fara-7B-OnnxDate: 2026-11-15Commit: 065f45bSigned: 2DC28CC7B26A283B ✓
Upstream
onnx-community/Fara-7B-Onnx
Pinned commit
065f45b8a35e22536c8b415fd2d3e0798a7a0298
Manifest
Fara-7B-Onnx.json · .minisig
Manifest sha256
861faf5392ccb6af630b2f48badf5bd9269ca6af3d64d3c48105d2bffb2746b0
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:27 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/Fara-7B-Onnx.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/Fara-7B-Onnx.json.minisig $ minisign -Vm Fara-7B-Onnx.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

microsoft/Fara-7B · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/Fara-7B-Onnx
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. Fara-7B-Onnx.json. Provenance. Takedown history: none.
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