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")
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

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
```

Nebula

File size: 3,110 Bytes

---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- nebula
- reasoning
- text-generation
- transformers
---

# Nebula

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/YF0kEDYMGJhcM3Lbl2EOD.png" alt="Nebula logo" width="100">
</p>

## 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 |