Instructions to use WootzappLab/fara-9b-baseline-eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

fara-9b-baseline-eval

File size: 5,240 Bytes

---
license: mit
base_model: microsoft/Fara1.5-9B
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - fara
  - computer-use
  - browser-automation
  - dom
  - baseline
  - evaluation
  - reproducibility
---

# Fara 1.5-9B W8 baseline evaluation

This repository preserves the exact Fara 1.5-9B checkpoint used for both W8 DOM browser-agent baseline evaluations. The checkpoint was not trained or fine-tuned for either evaluation. Its files are an exact mirror of the pinned Microsoft checkpoint.

## Common checkpoint and serving setup

| Component | Value |
|---|---|
| Base checkpoint | [`microsoft/Fara1.5-9B`](https://huggingface.co/microsoft/Fara1.5-9B) |
| Base checkpoint revision | `1a93677cd89d5601bc2ed759791e981f3a520032` |
| Fara source revision | `a675d6d61c41c47ae87bacefeab22caad18e3e84` |
| Model dtype | BF16 |
| KV-cache dtype | BF16 through vLLM `auto` |
| Maximum model length | 32,768 tokens |
| Tensor parallel size | 1 |
| Runtime | vLLM 0.19.1, PyTorch 2.10.0, Transformers 5.6.2 |
| Hardware | One NVIDIA H100 80 GB HBM3 |
| GCP instance | `w8-fara-9b-8115f370-head-3ibtw2ah-compute` |
| Deployed DOM adapter SHA256 | `6184fba5dee3cae079b20fe1abb1812e589e68b97737a28dac94b16e7f5c5811` |
| Deployed serving script SHA256 | `1d1c9c60280b15a73d633717776aafd23d087f6af447524ef9cc8315e6d70fb3` |

The DOM serving integration translated the recorder's OpenAI Responses contract to the Fara inference endpoint. It did not modify the model weights.

## Evaluation 1: 90-task baseline

| Measure | Result |
|---|---:|
| Selected tasks | 90 |
| Passed | 6 |
| Verified failures | 60 |
| Execution errors | 24 |
| Infrastructure errors | 0 |
| Pass@1 | 6.67% |
| Pass rate among verified tasks | 9.09% |

### Evaluation 1 provenance

| Component | Exact source |
|---|---|
| Evaluation artifacts | [`WootzappLab/fara-baseline-artifact`](https://huggingface.co/datasets/WootzappLab/fara-baseline-artifact/tree/eval-fara15-dom-baseline) |
| Immutable artifact commit | `30840bf827a58cfd0bdc8c86fa730ab32960448c` |
| Task dataset | [`WootzappLab/cua-bench`](https://huggingface.co/datasets/WootzappLab/cua-bench) |
| Dataset revision | `745eebb8f07abb92fdaae356bb6bee3a1f2975af` |
| Recorder, browser harness, environments, and verifier | [`tokenbender/w8-cua-bench`](https://github.com/tokenbender/w8-cua-bench/tree/environment-work) |
| Harness revision | `66037db6bae4b00a2b5860aa08ebb69b559e6871` |
| Evaluation date | 2026-09-25 |

Execution errors remain in the denominator. The artifact repository contains the raw recordings, logs, rubrics, verifier outputs, structured tables, and integrity manifests.

## Evaluation 2: 40-task baseline

| Measure | Result |
|---|---:|
| Selected tasks | 40 |
| Passed | 8 |
| Verified failures | 24 |
| Execution errors | 8 |
| Infrastructure errors | 0 |
| Pass@1 | 20.0% |
| Pass rate among verified tasks | 25.0% |

### Evaluation 2 provenance

| Component | Exact source |
|---|---|
| Evaluation artifacts | [`WootzappLab/Fara-40-baseline-artifact`](https://huggingface.co/datasets/WootzappLab/Fara-40-baseline-artifact/tree/eval-fara15-fresh-40-provenance-complete) |
| Immutable artifact commit | `f9c09ba743ab447b59e290a60ed61daa73808a66` |
| Results bundle SHA256 | `43f4ed88b79d9c0abe7a733ac6499b0000364e3f1f676a29320e83d1682e081b` |
| Exact task snapshot | Included in the artifact repository |
| Task snapshot SHA256 | `745f113b9c45f1a8dc3581ebc671435a3eafdfdbfb38bcaa6df1097ec10ab2e3` |
| Task dataset | [`WootzappLab/cua-bench-baseline-40`](https://huggingface.co/datasets/WootzappLab/cua-bench-baseline-40) |
| Dataset repository revision | `c2dd9ed8f23cbc477f49be7b7704e42bf4d46de6` (`baseline-40`) |
| Recorder, browser harness, environments, and verifier | [`tokenbender/w8-cua-bench`](https://github.com/tokenbender/w8-cua-bench/tree/environment-work) |
| Harness revision | `09976c86454f9a87e2fa6a3a3589d2e4d24f180e` |
| Infrastructure revision | `eb5dc3d9e0cbfbe389334b0600002f8db3a795dd` |
| Evaluation date | 2026-09-28 |

Execution errors remain in the denominator. The published dataset and the snapshot bundled with the artifacts contain the same task bytes, identified by the SHA256 above.

## Repository contents

- Model configuration, tokenizer, processor, chat template, and Safetensors weights copied from the pinned upstream revision
- `CHECKPOINT_FILES.json`: upstream Git blob identities and LFS SHA256 hashes for the checkpoint files
- `MANIFEST.json`: machine-readable checkpoint provenance
- `PROVENANCE.md`: checkpoint revision and experiment-boundary receipt
- `CHECKSUMS.sha256`: hashes for the repository's reproducibility metadata

## Load the checkpoint

```python
import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "WootzappLab/fara-9b-baseline-eval"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
```

## License and attribution

The mirrored base checkpoint is released by Microsoft under the MIT License. See the upstream [Fara 1.5-9B model card](https://huggingface.co/microsoft/Fara1.5-9B) for its original documentation, intended use, and limitations.