Instructions to use altaidevorg/gemma-judge-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use altaidevorg/gemma-judge-v0.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="altaidevorg/gemma-judge-v0.1")
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("altaidevorg/gemma-judge-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("altaidevorg/gemma-judge-v0.1")
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 altaidevorg/gemma-judge-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "altaidevorg/gemma-judge-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "altaidevorg/gemma-judge-v0.1",
		"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/altaidevorg/gemma-judge-v0.1

SGLang

How to use altaidevorg/gemma-judge-v0.1 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 "altaidevorg/gemma-judge-v0.1" \
    --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": "altaidevorg/gemma-judge-v0.1",
		"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 "altaidevorg/gemma-judge-v0.1" \
        --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": "altaidevorg/gemma-judge-v0.1",
		"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 altaidevorg/gemma-judge-v0.1 with Docker Model Runner:
```
docker model run hf.co/altaidevorg/gemma-judge-v0.1
```

Gemma Judge

This model is a compact yet highly capable LLM-as-a-judge model, fine-tuned from Gemma3 +B. It can be used for both direct feedback evaluations and A/B preference evaluations.

It is obtained by merging two models separately fine-tuned on feedback and preference tasks.

Models Merged

The following models were included in the merge:

Benchmarks

Model	Benchmark	Exact Match Accuracy (%)	Pearson r	Spearman ρ	Accuracy (%) (Pairwise)	Notes
🟪 altaidevorg/gemma-judge-v0.1	Feedback Bench / Preference Bench	73.0 / –	0.9198 / –	0.9210 / –	94.54	Strong unified performance across both tasks
🟨 Prometheus 2 (8×7B) (Kim et al., 2024)	Feedback Bench / Preference Bench	– / –	≈ 0.898 / –	≈ 0.90 / –	90.65	–

🧾 License

This model is released under the Apache 2.0 License.
However, because it is derived from Google’s Gemma 3, your use of this model must also comply with the Gemma Terms of Use.

By using this model, you agree to:

Follow Google’s Gemma Model Terms of Use, including restrictions on misuse and redistribution.
Attribute Google as the original provider of the Gemma 3 base model.

For full details, see: https://ai.google.dev/gemma/terms