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

Libraries

How to use juspay/xor with Transformers:

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "juspay/xor"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/juspay/xor

SGLang

How to use juspay/xor 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" \
    --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use juspay/xor with Docker Model Runner:
```
docker model run hf.co/juspay/xor
```

prakharprakash commited on 23 days ago

Commit

0d4be71

verified ·

1 Parent(s): 7ff7442

Upload THIRD_PARTY_NOTICES.md with huggingface_hub

Browse files

Files changed (1) hide show

THIRD_PARTY_NOTICES.md +27 -0

THIRD_PARTY_NOTICES.md ADDED Viewed

	@@ -0,0 +1,27 @@

+# Third-party notices
+## Qwen3.6-35B-A3B
+The released model is derived from `Qwen/Qwen3.6-35B-A3B`, distributed under the Apache License 2.0.
+Source: https://huggingface.co/Qwen/Qwen3.6-35B-A3B
+## SGLang
+The runtime pulls the following prebuilt SGLang image by immutable digest:
+```text
+lmsysorg/sglang@sha256:6bcaa47db52f78ce0d67863b8b2431221b79bc23204a80cad757fa819d00e921
+```
+Source: https://github.com/sgl-project/sglang
+## Python
+The compatibility server image uses the official Python image:
+```text
+python:3.12.12-slim-bookworm@sha256:593bd06efe90efa80dc4eee3948be7c0fde4134606dd40d8dd8dbcade98e669c
+```
+Source: https://hub.docker.com/_/python