Instructions to use Allen-UQ/CNY-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Allen-UQ/CNY-7B with Transformers:

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

pipe = pipeline("text-generation", model="Allen-UQ/CNY-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Allen-UQ/CNY-7B")
model = AutoModelForCausalLM.from_pretrained("Allen-UQ/CNY-7B", 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 Allen-UQ/CNY-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Allen-UQ/CNY-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Allen-UQ/CNY-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Allen-UQ/CNY-7B

SGLang

How to use Allen-UQ/CNY-7B 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 "Allen-UQ/CNY-7B" \
    --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": "Allen-UQ/CNY-7B",
		"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 "Allen-UQ/CNY-7B" \
        --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": "Allen-UQ/CNY-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Allen-UQ/CNY-7B with Docker Model Runner:
```
docker model run hf.co/Allen-UQ/CNY-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CNY-7B

Smaller reference checkpoint for CNY (Call Neighbours Yourself), a reinforcement learning framework that treats neighbour acquisition on a text-attributed graph as explicit graph-walk actions and supervises those actions with destination-conditioned on-policy self-distillation (OPSD).

Paper: Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation (EMNLP 2026), arXiv:2608.29588. The 14B counterpart is Allen-UQ/CNY-14B.

What this checkpoint is


Base model	`Qwen/Qwen2.5-7B-Instruct`
Training	GRPO with the OPSD credit term, `beta = 0.03`, entropy coefficient 0
Training mixture	Eight text-attributed graphs (seven node classification, WN18RR relation classification)
Selected step	280
Precision	bfloat16

This checkpoint is also the OPSD-on arm of the ablation in the paper, trained against an otherwise identical beta = 0 control that uses the same reward and rollouts.

Zero-shot held-out accuracy

Cora 7-way	Cora 2-way	WikiCS 10-way	WikiCS 5-way	Products 10-way	Products 5-way	FB15K237 10-way	Expla-Graph
75.37	89.63	74.32	81.20	86.00	90.30	76.41	88.45

Evaluated on the full test split of each dataset.

How the model is meant to be used

This is not a plain chat model. It expects the CNY multi-step walk prompt, in which the model observes the target node text, the label descriptions and a short preview per neighbour, then emits <walk> actions to reveal a neighbour's full text before committing to an answer. Prompting it as a standard instruct model will not exercise the learned walk policy.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Allen-UQ/CNY-7B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

For the walk environment, the prompt templates and the evaluation harness, use the code release (see the repository linked from the paper).

Limitations

CNY trains walk selection rather than a general graph reasoner. The walk policy is learned under a bounded step budget and a fixed preview format, and accuracy depends on the ego-node text being informative enough to direct the first walk.

Citation

@inproceedings{liu2026cny,
  title     = {Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation},
  author    = {Yilun Liu and Boyu Luo and Yanran Tang and Ruihong Qiu and Zi Huang},
  booktitle = {EMNLP},
  year      = {2026}
}