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

Libraries

How to use OrionLLM/Nebula with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OrionLLM/Nebula")
model = AutoModelForCausalLM.from_pretrained("OrionLLM/Nebula", 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 OrionLLM/Nebula with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OrionLLM/Nebula

SGLang

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

Docker Model Runner
How to use OrionLLM/Nebula with Docker Model Runner:
```
docker model run hf.co/OrionLLM/Nebula
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nebula

Nebula logo

1. Introduction

Nebula is a 320M-parameter generalist Small Reasoning Model trained on 200B+ tokens, designed for edge AI and on-device deployment.

Nebula is designed to deliver an unusually strong balance of memory, general reasoning, math, and retrieval-friendly behavior for its size class, aiming to outperform many small models of a similar parameter range on non-code, industry-style benchmarks.

2. Reasoning style

Nebula’s reasoning traces use an intentionally compact style with dense, short, frequently non-verbal sentences, optimized for efficiency under limited model capacity.

Traces use the following stenographic notation integrated into special tokens:

Logical markers

Token	Meaning	Usage
→	derivation / implication	For very short causal/logical flow
↺	iterative return / refinement loop	For backtracking, reconsidering priors, RAG re-querying
?	uncertainty/questions to resolve	Can be appended to short expressions/words, not only interrogatives
!/※	insight/breakthroughs	Emphatic mark for knowledge discovery
≈	approximation/estimates	For intermediary hypothesis / uncertain preliminary statements
∴	therefore / final step	Use sparingly to mark stable conclusions

Uncertainty

Token	Meaning	Usage
●	high confidence	well-supported empirical/theoretical ground; “anchor points.”
◐	medium/partial confidence	incomplete data; plausible but unverified links
○	low confidence	speculation, missing context, weak inference chain
⚠	bias/premise risk	domain mismatch, cultural assumptions, language-switch artifacts
?maybe?	soft speculation	marks tentative ideas, branches that might collapse later

Verification process

Token	Meaning	Usage
☐	unverified hypothesis	raw claim, no cross-check yet
☑	intermediate verification	one source/argument supports it
✓	confirmed/validated	multiple independent supports (●-level)

This reasoning format is designed to remain expressive while being lightweight enough for a small model.

3. Fine-Tuning/RL

Nebula has been successfully fine-tuned for a variety of tasks

Because Nebula is a reasoning-oriented model, it is expected to train well with reinforcement learning methods such as GRPO, both for verifiable tasks (with objective rewards) and for subjective tasks using an LLM-as-a-judge.

4. Benchmarks

Model	MMLU
Nebula	40.0
SmolLM2-360M	35.8
Gemma 3 270M (IT)	26.5
Granite-4.0-H-350M	36.21

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for OrionLLM/Nebula

Quantizations

1 model