Instructions to use nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16

SGLang

How to use nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16
```

Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 / model-00004-of-00024.safetensors

fitsumreda

Two-tower mask-diffusion weights (corrected conversion, iter_0085937)

da2556e verified about 1 month ago

Download

History Blame

5.36 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: c49e18c81c5eacd7c3112ea103eda81d736b19fa2e76ba87b7d8cdc6bc664641
Size of remote file:: 5.36 GB
SHA256:: de3dcf5bfa06f8d5762de6a98f0ac0b963cc4b5825cbc0415fd504ef5eb765c4

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.