	Nebula-S-v1	Nebula-S-v1-4bit
Backbone precision	bf16	4-bit (nf4)
Adapter precision	bf16	bf16
Backbone size	~8 GB	~2 GB
Total size	~9 GB	~3 GB
VRAM needed	~18 GB	~6 GB
Requires	CUDA / MPS / CPU	CUDA only (bitsandbytes)

Quick Start

pip install torch transformers>=4.51.0 bitsandbytes accelerate huggingface-hub

Option 1: Using huggingface_hub

python

from huggingface_hub import snapshot_download
import sys

snapshot_download("punitdecomp/Nebula-S-v1-4bit", local_dir="./Nebula-S-v1-4bit")
sys.path.insert(0, "./Nebula-S-v1-4bit")
from nebula_s import load_nebula_s

model, tokenizer = load_nebula_s("./Nebula-S-v1-4bit", device="cuda")

Option 2: Using git clone

bash

git lfs install
git clone https://huggingface.co/punitdecomp/Nebula-S-v1-4bit

python

import sys
sys.path.insert(0, "./Nebula-S-v1-4bit")
from nebula_s import load_nebula_s

model, tokenizer = load_nebula_s("./Nebula-S-v1-4bit", device="cuda")

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)
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)

License

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

decompute/Nebula-S-v1-4bit

Nebula-S-v1-4bit

What's different from Nebula-S-v1?

Quick Start

Option 1: Using huggingface_hub

Option 2: Using git clone

Generate a response

License

decompute/
Nebula-S-v1-4bit