Instructions to use decompute/Nebula-S-v1-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decompute/Nebula-S-v1-lite with Transformers:

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

pipe = pipeline("text-generation", model="decompute/Nebula-S-v1-lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decompute/Nebula-S-v1-lite")
model = AutoModelForCausalLM.from_pretrained("decompute/Nebula-S-v1-lite")
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 decompute/Nebula-S-v1-lite with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/decompute/Nebula-S-v1-lite

SGLang

How to use decompute/Nebula-S-v1-lite 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 "decompute/Nebula-S-v1-lite" \
    --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": "decompute/Nebula-S-v1-lite",
		"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 "decompute/Nebula-S-v1-lite" \
        --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": "decompute/Nebula-S-v1-lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use decompute/Nebula-S-v1-lite with Docker Model Runner:
```
docker model run hf.co/decompute/Nebula-S-v1-lite
```

Nebula-S-v1-lite

Lightweight (~3GB) version of Nebula-S-v1, pre-quantized to int4 using HQQ (Half-Quadratic Quantization).

Runs on Mac (MPS), CUDA, and CPU.

Variant	Download	Runtime	Platform
Nebula-S-v1	~9 GB	~9 GB	Universal (bf16)
Nebula-S-v1-4bit	~3 GB	~3 GB	CUDA only (bnb)
Nebula-S-v1-lite	~3 GB	~3 GB	Mac + CUDA + CPU

Quick Start

pip install torch transformers>=4.51.0 hqq huggingface-hub

Option 1: Using huggingface_hub

from huggingface_hub import snapshot_download
import sys

snapshot_download("decompute/Nebula-S-v1-lite", local_dir="./Nebula-S-v1-lite")
sys.path.insert(0, "./Nebula-S-v1-lite")
from nebula_s import load_nebula_s

# Auto-detects device (mps on Mac, cuda on NVIDIA, cpu fallback)
model, tokenizer = load_nebula_s("./Nebula-S-v1-lite")

Option 2: Using git clone

git lfs install
git clone https://huggingface.co/punitdecomp/Nebula-S-v1-lite

import sys
sys.path.insert(0, "./Nebula-S-v1-lite")
from nebula_s import load_nebula_s

model, tokenizer = load_nebula_s("./Nebula-S-v1-lite")

Generate a response

messages = [{"role": "user", "content": "Solve step by step: what is 17 * 23?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

device = next(model.parameters()).device
inputs = tokenizer(text, return_tensors="pt").to(device)

response = model.generate(
    inputs["input_ids"], inputs["attention_mask"],
    tokenizer, max_new_tokens=1024, temperature=0.7
)
print(response)

Explicit device

# Mac
model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="mps")

# NVIDIA GPU
model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="cuda")

# CPU
model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="cpu")

License

Apache 2.0. Backbone derived from an Apache-2.0 licensed base model.

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