Instructions to use OS-Copilot/OS-Shepherd-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OS-Copilot/OS-Shepherd-35B-A3B 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-35B-A3B")
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-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("OS-Copilot/OS-Shepherd-35B-A3B", 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-35B-A3B 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-35B-A3B"
# 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-35B-A3B",
		"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-35B-A3B

SGLang

How to use OS-Copilot/OS-Shepherd-35B-A3B 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-35B-A3B" \
    --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-35B-A3B",
		"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-35B-A3B" \
        --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-35B-A3B",
		"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-35B-A3B with Docker Model Runner:
```
docker model run hf.co/OS-Copilot/OS-Shepherd-35B-A3B
```

OS-Shepherd-35B-A3B / README.md

QiushiSun

Add concise model card and Apache 2.0 license

e9bcf61 verified 1 day ago

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	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	license: apache-2.0
	base_model: Qwen/Qwen3.5-35B-A3B
	datasets:
	- OS-Copilot/OS-Shepherd-100K
	language:
	- en
	tags:
	- computer-use
	- reward-model
	- trajectory-evaluation
	- multimodal
	- mixture-of-experts
	---

	# OS-Shepherd-35B-A3B

	OS-Shepherd-35B-A3B 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 [Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) on [OS-Shepherd-100K](https://huggingface.co/datasets/OS-Copilot/OS-Shepherd-100K) using the same SFT and GRPO recipe as OS-Shepherd-9B. The RL stage focuses on reducing false-success judgments.

	## Results

	\| Benchmark \| Accuracy \| Fail recall \|
	\|---\|---:\|---:\|
	\| OSReward \| 85.6 \| 86.2 \|
	\| OSReward-Hard \| 62.7 \| 60.1 \|

	Results use the fixed judging protocol described in the [OSReward paper](https://arxiv.org/abs/2607.28609).

	## Usage

	Use the canonical prompt and trajectory format from the [OSReward repository](https://github.com/OS-Copilot/OSReward). 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

	```bibtex
	@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}
	}
	```