Instructions to use decompute/Nebula-S-v1-4bit-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decompute/Nebula-S-v1-4bit-optimized with Transformers:

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

pipe = pipeline("text-generation", model="decompute/Nebula-S-v1-4bit-optimized")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decompute/Nebula-S-v1-4bit-optimized")
model = AutoModelForCausalLM.from_pretrained("decompute/Nebula-S-v1-4bit-optimized")
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-v1-4bit-optimized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decompute/Nebula-S-v1-4bit-optimized"
# 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-v1-4bit-optimized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/decompute/Nebula-S-v1-4bit-optimized

SGLang

How to use decompute/Nebula-S-v1-4bit-optimized 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-v1-4bit-optimized" \
    --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-v1-4bit-optimized",
		"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-v1-4bit-optimized" \
        --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-v1-4bit-optimized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use decompute/Nebula-S-v1-4bit-optimized with Docker Model Runner:
```
docker model run hf.co/decompute/Nebula-S-v1-4bit-optimized
```

Nebula-S-v1-4bit-optimized / README.md

punitdecomp

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7f129c3 verified about 2 months ago

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	---
	license: apache-2.0
	tags:
	- nebula-s
	- svms
	- math-reasoning
	- competition-math
	- quantized
	- int4
	- hqq
	library_name: transformers
	---

	# Nebula-S-v1-lite

	Lightweight (~3GB) version of [Nebula-S-v1](https://huggingface.co/punitdecomp/Nebula-S-v1), pre-quantized to int4 using [HQQ](https://github.com/mobiusml/hqq) (Half-Quadratic Quantization).

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

	\| Variant \| Download \| Runtime \| Platform \|
	\|---\|---\|---\|---\|
	\| [Nebula-S-v1](https://huggingface.co/punitdecomp/Nebula-S-v1) \| ~9 GB \| ~9 GB \| Universal (bf16) \|
	\| [Nebula-S-v1-4bit](https://huggingface.co/punitdecomp/Nebula-S-v1-4bit) \| ~3 GB \| ~3 GB \| CUDA only (bnb) \|
	\| Nebula-S-v1-lite \| ~3 GB \| ~3 GB \| Mac + CUDA + CPU \|

	## Quick Start

	```bash
	pip install torch transformers>=4.51.0 hqq huggingface-hub
	```

	### Option 1: Using huggingface_hub

	```python
	from huggingface_hub import snapshot_download
	import sys

	snapshot_download("punitdecomp/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

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

	```python
	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

	```python
	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

	```python
	# 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.