metadata
license: apache-2.0
tags:
- nebula-s
- svms
- math-reasoning
- competition-math
- 4bit
- quantized
- bitsandbytes
library_name: transformers
Nebula-S-v1-4bit
4-bit quantized version of Nebula-S-v1.
Nebula-S-v1 is a reasoning-enhanced language model using the SVMS (Structured-Vector Multi-Stream) architecture.
What's different from Nebula-S-v1?
| 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
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
git lfs install
git clone https://huggingface.co/punitdecomp/Nebula-S-v1-4bit
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
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.