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: 916 Bytes

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{
  "additional_special_tokens": [
    "<|im_start|>",
    "<|im_end>",
    "<think>",
    "</think>",
    "source_1",
    "source_2",
    "source_3",
    "source_4",
    "source_5",
    "source_6",
    "source_7",
    "source_8",
    "source_9",
    "source_10",
    "<ref",
    "</ref>",
    "→",
    "↺",
    "※",
    "?maybe?",
    "●",
    "◐",
    "○",
    "⚠",
    "☐",
    "☑",
    "✓",
    "⟨H≈0.1⟩",
    "⟨H≈0.2⟩",
    "⟨H≈0.3⟩",
    "⟨H≈0.4⟩",
    "⟨H≈0.5⟩",
    "⟨H≈0.6⟩",
    "⟨H≈0.7⟩",
    "⟨H≈0.8⟩",
    "⟨H≈0.9⟩",
    "⟨H≈1.0⟩",
    "⟨H≈1.1⟩",
    "⟨H≈1.2⟩",
    "⟨H≈1.3⟩",
    "⟨H≈1.4⟩",
    "⟨H≈1.5⟩",
    "⟨H≈1.6⟩",
    "⟨H≈1.7⟩",
    "⟨H≈1.8⟩"
  ],
  "bos_token": "<|begin_of_text|>",
  "eos_token": "<|end_of_text|>",
  "pad_token": "[PAD]",
  "unk_token": "[UNK]"
}