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

Libraries

How to use juspay/xor-lite with Transformers:

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

pipe = pipeline("text-generation", model="juspay/xor-lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("juspay/xor-lite")
model = AutoModelForCausalLM.from_pretrained("juspay/xor-lite", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use juspay/xor-lite with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/juspay/xor-lite

SGLang

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

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

XOR Lite

XOR Lite (xor-lite) is a post-trained version of openbmb/MiniCPM5-2B for typed decision tasks. It is served through a TypeSafe-compatible /v1/systemone API

Release

Field	Value
Base checkpoint	`openbmb/MiniCPM5-2B`
Base revision	`12a3808a956f869c767195e9266b59c4d21d92e2`
Architecture	Dense `LlamaForCausalLM`
Parameters	Approximately 2.52 billion
Released precision	BF16
License	Apache License 2.0
Packaging	Fully merged weights; no adapter loading is required

Pin the published revision for reproducible use

hf download juspay/xor-lite --revision REPLACE_WITH_RELEASE_REVISION --local-dir xor-lite

Interface

The server accepts a state and a map of typed questions

noul: binary probability
choice: categorical decision and full probability distribution
score: expected ordinal score and full probability distribution

The serving layer performs constrained single-token candidate readout, forward and reverse option-order evaluation, probability calibration, and SystemOne schema conversion. It supports up to 255 candidates using the included MiniCPM-specific marker table. The released serving layer is part of the inference configuration required for reproducible results

Quick start

hf download juspay/xor-lite --revision REPLACE_WITH_RELEASE_REVISION --local-dir xor-lite

(cd xor-lite/serving && sha256sum -c xor-lite-serving.tar.gz.sha256)

mkdir -p xor-lite-runtime
tar -xzf xor-lite/serving/xor-lite-serving.tar.gz -C xor-lite-runtime --strip-components=1

cd xor-lite-runtime
cp .env.example .env
sed -i "s|^MODEL_DIR=.*|MODEL_DIR=$(cd ../xor-lite && pwd)|" .env
./run.sh

When the smoke test succeeds, the API is available at http://127.0.0.1:30002/v1/systemone

Public JEVBench self-run

XOR Lite was evaluated locally on all 231 public JEVBench decisions using harness commit fd54ea7dc02bbe29c6ac8f6e015a54cdcff26805, the existing typesafe adapter, and one request at a time

The run used the released configuration on 1 x NVIDIA H200 NVL (143 GB), tensor parallelism 1, data parallelism 1, with request caching disabled

Tier	Attempted	Valid	Correct	Accuracy	Brier	ECE	p50	p95
Easy	48	48	48	1.0000	0.1467	0.2294	0.0250 s	0.0279 s
Original	72	72	66	0.9167	0.2777	0.2986	0.0251 s	0.0291 s
Hard public	111	111	55	0.4955	0.6153	0.0902	0.0308 s	0.0659 s
All public	231	231	169	0.7316

Operational success, coverage, schema validity, and strict schema validity were 1.0000 for each tier. These are self-run public-tier results, not an official JEVBench rank. Latency is hardware-specific and was measured locally without network overhead

Validated runtime

Setting	Value
SGLang image	`lmsysorg/sglang@sha256:6bcaa47db52f78ce0d67863b8b2431221b79bc23204a80cad757fa819d00e921`
Tensor parallelism	1
Data parallelism	1
Validated GPU	1 x NVIDIA H200 NVL, 143 GB
Maximum prefill tokens	250,000
Static memory fraction	0.50
Probability temperature	`100.0` for `noul`, `choice`, and `score`

NVIDIA L4 validation

The immutable v1.0 release was also validated without modification on one NVIDIA L4 with 24 GB of memory using tensor parallelism 1 and data parallelism 1. The complete public JEVBench run reproduced the H200 result exactly at 169/231 correct, with no operational, coverage, or schema failures

Tier	Correct	Accuracy	p50	p95
Easy	48/48	1.0000	0.0458 s	0.0481 s
Original	66/72	0.9167	0.0465 s	0.0488 s
Hard public	55/111	0.4955	0.1175 s	0.5868 s
All public	169/231	0.7316

The loaded service used 13,803 MiB of the L4's 23,034 MiB. The run used NVIDIA driver 595.91.07 and the same pinned SGLang image shown above. The evidence archive has SHA-256 6389b280c9abfffd608a3864a1b8ed7570974ca415ad5f079d0224f429694ed9

The L4 result demonstrates that the H200 used for the original release measurement is not a minimum hardware requirement. It does not define or announce a hosted-service tariff

The service binds to localhost by default and does not require an API key. Remote deployments must add authentication, TLS, rate limits, and request-size limits at the ingress layer

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for juspay/xor-lite

Base model

openbmb/MiniCPM5-2B

Finetuned

(62)

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