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)

# pip install -U transformers accelerate
# 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=256)
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
```

nielsr HF Staff commited on 8 days ago

Commit

e006365

verified ·

1 Parent(s): 54b5fd7

Update pipeline tag and add paper, project page, and code links

Browse files

This PR improves the model card by:

- updating the `pipeline_tag` from `image-text-to-text` to `video-text-to-text`, since RoboJudge-9B judges generated embodied videos from video and text inputs
- linking the model to the paper [RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation](https://huggingface.co/papers/2610.27782)
- adding links to the project page and GitHub repository

Files changed (1) hide show

README.md +9 -4

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
-license: apache-2.0
 library_name: transformers
-pipeline_tag: image-text-to-text
 tags:
 - video
 - multimodal
@@ -13,6 +13,8 @@ tags:
 # RoboJudge-9B
 RoboJudge-9B is a multimodal judge for generated embodied-manipulation videos.
 It scores two axes on a 1–5 scale:
@@ -21,6 +23,10 @@ It scores two axes on a 1–5 scale:
 - **Instruction Alignment (IA):** agent match, object correctness, and goal
   completion.
 ## Benchmark results on RoboJudge-Bench
@@ -30,9 +36,8 @@ It scores two axes on a 1–5 scale:
 | IA | 0.775 |
 | Overall | 0.719 |
 ## 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.

 ---
 library_name: transformers
+license: apache-2.0
+pipeline_tag: video-text-to-text
 tags:
 - video
 - multimodal
 # RoboJudge-9B
+This model was presented in the paper [RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation](https://huggingface.co/papers/2610.27782).
 RoboJudge-9B is a multimodal judge for generated embodied-manipulation videos.
 It scores two axes on a 1–5 scale:
 - **Instruction Alignment (IA):** agent match, object correctness, and goal
   completion.
+## Links
+- Project page: https://spherelab.ai/RoboJudge/
+- Code: https://github.com/Sphere-AI-Lab/RoboJudge
 ## Benchmark results on RoboJudge-Bench
 | IA | 0.775 |
 | Overall | 0.719 |
 ## 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.