Instructions to use OS-Copilot/OS-Shepherd-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OS-Copilot/OS-Shepherd-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="OS-Copilot/OS-Shepherd-9B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("OS-Copilot/OS-Shepherd-9B")
model = AutoModelForMultimodalLM.from_pretrained("OS-Copilot/OS-Shepherd-9B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OS-Copilot/OS-Shepherd-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OS-Copilot/OS-Shepherd-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OS-Copilot/OS-Shepherd-9B",
		"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/OS-Copilot/OS-Shepherd-9B

SGLang

How to use OS-Copilot/OS-Shepherd-9B 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 "OS-Copilot/OS-Shepherd-9B" \
    --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": "OS-Copilot/OS-Shepherd-9B",
		"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 "OS-Copilot/OS-Shepherd-9B" \
        --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": "OS-Copilot/OS-Shepherd-9B",
		"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 OS-Copilot/OS-Shepherd-9B with Docker Model Runner:
```
docker model run hf.co/OS-Copilot/OS-Shepherd-9B
```

OS-Shepherd-9B / README.md

QiushiSun

Add concise model card and Apache 2.0 license

db11f26 verified about 2 months ago

preview code

Raw History Blame

1.75 kB

metadata

library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
base_model: Qwen/Qwen3.5-9B
datasets:
  - OS-Copilot/OS-Shepherd-100K
language:
  - en
tags:
  - computer-use
  - reward-model
  - trajectory-evaluation
  - multimodal

OS-Shepherd-9B

OS-Shepherd-9B is an open multimodal reward model for judging computer-use agent trajectories. Given a task instruction, screenshots, and the agent's reasoning and actions, it determines whether the task was completed and returns a reasoned SUCCESS or FAIL verdict.

It is fine-tuned from Qwen3.5-9B on OS-Shepherd-100K using SFT followed by GRPO, with the RL stage focused on reducing false-success judgments.

Results

Benchmark	Accuracy	Fail recall
OSReward	86.1	86.0
OSReward-Hard	60.2	57.6

Results use the fixed judging protocol described in the OSReward paper.

Usage

Use the canonical prompt and trajectory format from the OSReward repository. A recent Transformers, vLLM, or SGLang version with Qwen3.5 multimodal support is required.

This model is intended for trajectory evaluation, data filtering, and reward-model research. It is not a computer-control policy and may still miss fine-grained visual failures, especially on hard cases.

License

Apache License 2.0. See LICENSE.

Citation

@article{sun2026osreward,
  title={OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models},
  author={Sun, Qiushi and others},
  journal={arXiv preprint arXiv:2607.28609},
  year={2026}
}