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

Libraries

How to use HuggingFriends/RoboJudge-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFriends/RoboJudge-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("HuggingFriends/RoboJudge-9B")
model = AutoModelForMultimodalLM.from_pretrained("HuggingFriends/RoboJudge-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 HuggingFriends/RoboJudge-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFriends/RoboJudge-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": "HuggingFriends/RoboJudge-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/HuggingFriends/RoboJudge-9B

SGLang

How to use HuggingFriends/RoboJudge-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 "HuggingFriends/RoboJudge-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": "HuggingFriends/RoboJudge-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 "HuggingFriends/RoboJudge-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": "HuggingFriends/RoboJudge-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 HuggingFriends/RoboJudge-9B with Docker Model Runner:
```
docker model run hf.co/HuggingFriends/RoboJudge-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RoboJudge-9B

RoboJudge-9B is a multimodal judge for generated embodied-manipulation videos. It scores two axes on a 1–5 scale:

Physical Adherence (PA): agent consistency, scene consistency, and interaction realism.
Instruction Alignment (IA): agent match, object correctness, and goal completion.

The model is trained with full-parameter supervised fine-tuning followed by 200 steps of reinforcement learning. The release code, exact prompts, and evaluation scripts are available at https://github.com/SiyuanMaCS/robojudge-iclr2027.

Benchmark results

Metric	Pearson r with human ratings
PA	0.645
IA	0.775
Overall	0.719

The exact released predictions reproduce PA 0.644617, IA 0.775028, and pooled Overall 0.719211 with the repository evaluator.

Download and inference

git clone https://github.com/SiyuanMaCS/robojudge-iclr2027.git
cd robojudge-iclr2027
pip install -r requirements.txt

hf download HuggingFriends/RoboJudge-9B \
  --local-dir checkpoints_hf/RoboJudge-9B

CKPT=checkpoints_hf/RoboJudge-9B \
DATA_ROOT=/path/to/mllm-as-embodied-world-judge \
OUT=outputs/robojudge_predictions.jsonl \
bash code/run_inference.sh

A smaller BF16 sharded copy and the exact one-epoch SFT checkpoint are available at https://huggingface.co/HuggingFriends/robojudge-iclr2027-checkpoints.

Checkpoint identity

Weight format: safetensors
Original training dtype: FP32
model.safetensors SHA256: 34e17912b402182ab8588494d2715461c7120761e98f8b055b1adbbee400e103

Data

Video assets: https://huggingface.co/datasets/HuggingFriends/mllm-as-embodied-world-judge
Release metadata and predictions: https://huggingface.co/datasets/HuggingFriends/robojudge-iclr2027-reviewer-bundle

License

The RoboJudge release code and model weights are provided under Apache-2.0. Use of the associated datasets is additionally subject to the terms of their upstream sources.

Downloads last month: 12

Safetensors

Model size

9B params

Tensor type

F32

Model tree for HuggingFriends/RoboJudge-9B

Quantizations

1 model