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
```

HuggingFriends commited on 13 days ago

Commit

54b5fd7

verified ·

1 Parent(s): 4ce7121

Update README.md

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README.md +1 -38

README.md CHANGED Viewed

@@ -21,12 +21,8 @@ It scores two axes on a 1–5 scale:
 - **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 |
 |---|---:|
@@ -34,39 +30,6 @@ scripts are available at
 | 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
-```bash
-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

 - **Instruction Alignment (IA):** agent match, object correctness, and goal
   completion.
+## Benchmark results on RoboJudge-Bench
 | Metric | Pearson r with human ratings |
 |---|---:|
 | IA | 0.775 |
 | Overall | 0.719 |
 ## License