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

Libraries

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

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

pipe = pipeline("text-generation", model="decompute/Nebula-S-v1")
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")
model = AutoModelForCausalLM.from_pretrained("decompute/Nebula-S-v1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use decompute/Nebula-S-v1 with Docker Model Runner:
```
docker model run hf.co/decompute/Nebula-S-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nebula-S-v1

Nebula-S-v1 is a reasoning-enhanced language model using the SVMS (Structured-Vector Multi-Stream) architecture.

Architecture

SVMS adds a multi-stream reasoning layer on top of a frozen 4B-parameter backbone:

Structured Consistency: Topological constraint forcing cross-stream coherence
Learned Router: Per-token stream weighting
Delta Logits: Learnable correction to backbone predictions

Component	Details
Trainable	400M / 4.4B total

Quick Start

pip install torch transformers huggingface-hub

Option 1: Using huggingface_hub

from huggingface_hub import snapshot_download
import sys



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

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

Option 2: Using git clone

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

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

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

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)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
response = model.generate(
    inputs["input_ids"], inputs["attention_mask"],
    tokenizer, max_new_tokens=2048, temperature=0.7
)
print(response)

Training

Data: Orca Math Word Problems (200K)
Steps: 1000
Method: Adapter-only (backbone frozen)

Evaluation Results

Nebula-S-v1 was evaluated on several challenging benchmarks:

Benchmark	Score
GSM8K	90%
GPQA	70.5%
HMMT (November 2025)	67%
MMLU-Pro	79.7%

These results demonstrate strong performance for a 4B-parameter model, particularly on math reasoning (GSM8K) and advanced knowledge/competition-level tasks.

License

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

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for decompute/Nebula-S-v1

Quantizations

3 models