Image-Text-to-Text
Transformers
Safetensors
qwen3_5
fara
computer-use
browser-automation
dom
baseline
evaluation
reproducibility
conversational
Instructions to use WootzappLab/fara-9b-baseline-eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WootzappLab/fara-9b-baseline-eval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="WootzappLab/fara-9b-baseline-eval") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("WootzappLab/fara-9b-baseline-eval") model = AutoModelForMultimodalLM.from_pretrained("WootzappLab/fara-9b-baseline-eval", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WootzappLab/fara-9b-baseline-eval with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WootzappLab/fara-9b-baseline-eval" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WootzappLab/fara-9b-baseline-eval", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/WootzappLab/fara-9b-baseline-eval
- SGLang
How to use WootzappLab/fara-9b-baseline-eval with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "WootzappLab/fara-9b-baseline-eval" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WootzappLab/fara-9b-baseline-eval", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "WootzappLab/fara-9b-baseline-eval" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WootzappLab/fara-9b-baseline-eval", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use WootzappLab/fara-9b-baseline-eval with Docker Model Runner:
docker model run hf.co/WootzappLab/fara-9b-baseline-eval
File size: 1,556 Bytes
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"schema_version": 1,
"repository": "WootzappLab/fara-9b-baseline-eval",
"publisher": "IshaGarg1103",
"published_at": "2026-09-25",
"checkpoint": {
"source_repository": "microsoft/Fara1.5-9B",
"source_revision": "1a93677cd89d5601bc2ed759791e981f3a520032",
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"license": "MIT"
},
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"fara_repository": "https://github.com/microsoft/fara",
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"dataset_repository": "https://huggingface.co/datasets/WootzappLab/cua-bench",
"dataset_revision": "745eebb8f07abb92fdaae356bb6bee3a1f2975af",
"recorder_harness_repository": "https://github.com/tokenbender/w8-cua-bench",
"recorder_harness_branch": "environment-work",
"recorder_harness_revision": "66037db6bae4b00a2b5860aa08ebb69b559e6871",
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"infrastructure_revision": null,
"infrastructure_status": "pending branch push and pull request"
},
"evaluation": {
"run_id": "fara15-dom-baseline",
"date": "2026-09-25",
"gcp_instance": "w8-fara-9b-8115f370-head-3ibtw2ah-compute",
"selected_tasks": 90,
"passed": 6,
"verified_failed": 60,
"verified_tasks": 66,
"execution_errors": 24,
"infrastructure_errors": 0,
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"artifact_status": "pending upload",
"results_report_status": "pending"
}
}
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