Instructions to use LoNebula/Nebula-1-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoNebula/Nebula-1-preview with Transformers:

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

pipe = pipeline("text-generation", model="LoNebula/Nebula-1-preview")

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

tokenizer = AutoTokenizer.from_pretrained("LoNebula/Nebula-1-preview")
model = AutoModelForCausalLM.from_pretrained("LoNebula/Nebula-1-preview", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoNebula/Nebula-1-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoNebula/Nebula-1-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoNebula/Nebula-1-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoNebula/Nebula-1-preview

SGLang

How to use LoNebula/Nebula-1-preview 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 "LoNebula/Nebula-1-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoNebula/Nebula-1-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LoNebula/Nebula-1-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoNebula/Nebula-1-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoNebula/Nebula-1-preview with Docker Model Runner:
```
docker model run hf.co/LoNebula/Nebula-1-preview
```

Nebula-1-preview / eval /hellaswag.json

LoNebula

Upload Nebula-1-preview (arm a0)

f79d269 verified 2 days ago

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373 Bytes

{
  "model": "Nebula-1-preview",
  "task": "hellaswag",
  "n_shot": 0,
  "metric": "acc_norm,none",
  "value": 0.274547,
  "stderr": 0.004454,
  "seed": 1234,
  "dtype": "bfloat16",
  "batch_size": 4,
  "lm_eval_version": "0.4.13",
  "transformers_version": "5.15.0",
  "torch_version": "2.11.0+cu128",
  "num_parameters_millions": 121.83,
  "measured_on": "16 Oct 2026"
}

	{
	"model": "Nebula-1-preview",
	"task": "hellaswag",
	"n_shot": 0,
	"metric": "acc_norm,none",
	"value": 0.274547,
	"stderr": 0.004454,
	"seed": 1234,
	"dtype": "bfloat16",
	"batch_size": 4,
	"lm_eval_version": "0.4.13",
	"transformers_version": "5.15.0",
	"torch_version": "2.11.0+cu128",
	"num_parameters_millions": 121.83,
	"measured_on": "16 Oct 2026"
	}