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 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`
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`
Immutable artifact commit	`30840bf827a58cfd0bdc8c86fa730ab32960448c`
Task dataset	`WootzappLab/cua-bench`
Dataset revision	`745eebb8f07abb92fdaae356bb6bee3a1f2975af`
Recorder, browser harness, environments, and verifier	`tokenbender/w8-cua-bench`
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`
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`
Dataset repository revision	`c2dd9ed8f23cbc477f49be7b7704e42bf4d46de6` (`baseline-40`)
Recorder, browser harness, environments, and verifier	`tokenbender/w8-cua-bench`
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

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 for its original documentation, intended use, and limitations.

Downloads last month: 33

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for WootzappLab/fara-9b-baseline-eval

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

microsoft/Fara1.5-9B

Finetuned

(1)

this model