Nebula-S-3B
Nebula-S-3B is an internal reasoning model package with custom runtime components.
This package intentionally does not include upstream lineage, source training records, or private provenance. Those records are maintained separately in restricted internal release files.
Contents
core/: model weights, tokenizer, and generation configurationruntime_weights.safetensors: runtime weight artifactmodeling_nebula.py: local runtime loadernebula_runtime.py: import-friendly loader aliasrelease_metadata.json: neutral package metadatarelease_manifest.internal.json: file checksums for this release
Install
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
Smoke test
Run this from inside the extracted model directory:
python modeling_nebula.py .
Local usage
from nebula_runtime import load_model
model, tokenizer = load_model("./Nebula-S-3B")
messages = [{"role": "user", "content": "Solve: what is 2+2?"}]
if getattr(tokenizer, "chat_template", None):
prompt = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False,
)
else:
prompt = "User: Solve: what is 2+2?\nAssistant:"
inputs = tokenizer(
prompt,
add_special_tokens=False,
return_tensors="pt",
).to(next(model.parameters()).device)
text = model.generate(
inputs["input_ids"],
inputs["attention_mask"],
tokenizer,
max_new_tokens=512,
temperature=0,
)
print(text)
Creating a tuned successor release
This downloadable package is an inference artifact. To create a tuned successor release, use the approved restricted training workspace rather than modifying this folder in place.
Recommended internal flow:
- Create a new release ID, for example
nebula_s_3b_v0_1_1. - Add approved examples or correction data to the internal training dataset.
- Train a candidate runtime artifact in the restricted training environment.
- Compare the candidate against this release on fixed evaluation prompts and tasks.
- Repackage the candidate with the internal packaging tool.
- Run package validation: smoke load, leak scan, strict runtime-weight validation, checksum manifest, and license/notice review.
- Promote only the sanitized downloadable package.
Do not upload private provenance, source training records, optimizer state, source data paths, or build logs with this package.
License and Use Restrictions
Nebula-S-SVMS2-3B is released under the Decompute Non-Commercial Research License v1.0.
This is a restricted-access non-commercial research release. It is not an open-source release.
Permitted Use
The model may be used only for personal, academic, and non-commercial research or evaluation.
Prohibited Use
The model may not be used for commercial use, revenue-generating use, production use, paid API use, SaaS use, customer-facing deployment, enterprise workflow automation, redistribution, sublicensing, mirroring, uploading converted versions, uploading quantized versions, uploading fine-tuned versions, or creating/distributing derivative models.
The model and its outputs may not be used to train, improve, distill, benchmark for marketing purposes, or evaluate commercial models, products, services, or platforms.
For commercial licensing, contact hina@decompute.run.
Evaluation Results
The following results are from Decompute internal evaluations of Nebula-S-SVMS2-3B.
| Benchmark | Score |
|---|---|
| GPQA | 86.85 |
| HMMT Nov 2025 | 80.00 |
| GSM8K | 93.78 |
| MMLU-Pro | 83.00 |
These scores are reported from internal evaluation runs. Evaluation settings, prompts, decoding parameters, and extraction methods may affect comparability with public leaderboard results.