Instructions to use juspay/xor-omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juspay/xor-omni with Transformers:

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

pipe = pipeline("image-text-to-text", model="juspay/xor-omni")
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("juspay/xor-omni")
model = AutoModelForMultimodalLM.from_pretrained("juspay/xor-omni", 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 juspay/xor-omni with vLLM:

Install from pip and serve model

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

SGLang

How to use juspay/xor-omni 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 "juspay/xor-omni" \
    --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": "juspay/xor-omni",
		"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 "juspay/xor-omni" \
        --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": "juspay/xor-omni",
		"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 juspay/xor-omni with Docker Model Runner:
```
docker model run hf.co/juspay/xor-omni
```

XOR Omni

XOR Omni (xor-omni) is a multimodal decision model post-trained from google/gemma-4-26B-A4B-it. It is designed for typed decision tasks and structured one-pass readout.

Model details

Field	Value
Base model	`google/gemma-4-26B-A4B-it`
Architecture	Gemma 4 multimodal mixture-of-experts
Parameters	Approximately 26B total, about 4B active per token
Released precision	BF16
Context length	32,768 tokens
Input modalities	Text and image
Output	Text
Runtime	SGLang
License	Apache License 2.0
Packaging	Fully merged weights; no adapter loading is required

Intended use

XOR Omni is intended for typed decision workloads, including:

Binary decisions
Categorical choices
Ordinal scoring
Structured readout from combined text and image context

It is optimized for deterministic, one-pass decision output rather than open-ended assistant conversation.

Quick start

Download the model:

hf download juspay/xor-omni --local-dir xor-omni

Example SGLang launch:

python -m sglang.launch_server \
  --model-path ./xor-omni \
  --trust-remote-code \
  --context-length 32768 \
  --host 0.0.0.0 \
  --port 30000

Use the included chat_template.jinja when serving the model. The exact deployment configuration should be validated for your target hardware and workload.

Limitations

This model is specialized for typed decision tasks and is not intended as a general-purpose assistant.
Multimodal behavior should be validated on your own data before production use.
Performance may vary with prompt format, context length, hardware, and serving configuration.
This repository contains merged model weights only; training scripts, adapters, benchmark artifacts, and logs are intentionally not included.

License

This model is released under the Apache License 2.0, inherited from the base Gemma 4 checkpoint.

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Safetensors

Model size

26B params

Tensor type

BF16

Model tree for juspay/xor-omni

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

(175)

this model