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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("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
```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.
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