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("punitdecomp/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.