Instructions to use vkola-lab/LUNAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vkola-lab/LUNAR with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vkola-lab/LUNAR")
model = AutoModelForCausalLM.from_pretrained("vkola-lab/LUNAR", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vkola-lab/LUNAR with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vkola-lab/LUNAR

SGLang

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

Docker Model Runner
How to use vkola-lab/LUNAR with Docker Model Runner:
```
docker model run hf.co/vkola-lab/LUNAR
```

Model Card for LUNAR

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct on the NACC dataset. It has been trained using TRL.

Quick start

system_prompt = "Please reason step by step, and put your final answer within \\boxed{}."

question = """Question: The patient is a 70 year old ...

Using the information provided, determine the patient’s cognitive status.

Answer Choices:
A. Normal Cognition
B. Mild Cognitive Impairment
C. Dementia
"""

Option 1: Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "vkola-lab/LUNAR"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": question}
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=10000,
    do_sample=True,
    temperature=1,
    top_p=1.0,
)

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)
print(response)

Option 2: vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="vkola-lab/LUNAR")
sampling_params = SamplingParams(temperature=1, top_p=1.0, max_tokens=10000)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": question}
]

outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs[0].text)