Instructions to use ZonglinY/MOOSE-Star-R1D-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZonglinY/MOOSE-Star-R1D-7B with Transformers:

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

pipe = pipeline("text-generation", model="ZonglinY/MOOSE-Star-R1D-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZonglinY/MOOSE-Star-R1D-7B")
model = AutoModelForCausalLM.from_pretrained("ZonglinY/MOOSE-Star-R1D-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZonglinY/MOOSE-Star-R1D-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZonglinY/MOOSE-Star-R1D-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": "ZonglinY/MOOSE-Star-R1D-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZonglinY/MOOSE-Star-R1D-7B

SGLang

How to use ZonglinY/MOOSE-Star-R1D-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 "ZonglinY/MOOSE-Star-R1D-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": "ZonglinY/MOOSE-Star-R1D-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 "ZonglinY/MOOSE-Star-R1D-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": "ZonglinY/MOOSE-Star-R1D-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZonglinY/MOOSE-Star-R1D-7B with Docker Model Runner:
```
docker model run hf.co/ZonglinY/MOOSE-Star-R1D-7B
```

Model size showing 333 KB instead of 15.2 GB

by ZonglinY - opened Apr 5

Discussion

ZonglinY

Owner Apr 5

•

edited Apr 5

Hi HF team,

The model card/collection page shows the model size as 333 KB, but the actual total size is 8B (4 safetensors shards).

It seems the displayed size is reading from model.safetensors.index.json (333 KB) instead of summing all shard files.

You can verify the correct size under the Files tab — all 4 shards are present:

model-00001-of-00004.safetensors (4.88 GB)
model-00002-of-00004.safetensors (4.93 GB)
model-00003-of-00004.safetensors (4.33 GB)
model-00004-of-00004.safetensors (1.09 GB)

Could you help refresh the metadata? Thanks!

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