Text Generation
Transformers
Safetensors
English
nemotron_labs_audex
nvidia
nemotron-labs-audex
reasoning
general-purpose
SFT
audio-language-modeling
audio-understanding
text-to-speech
text-to-audio
speech-recognition
speech-translation
Instructions to use nvidia/Nemotron-Labs-Audex-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Nemotron-Labs-Audex-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-Audex-2B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-Audex-2B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/Nemotron-Labs-Audex-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Nemotron-Labs-Audex-2B" # 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-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nvidia/Nemotron-Labs-Audex-2B
- SGLang
How to use nvidia/Nemotron-Labs-Audex-2B 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-Audex-2B" \ --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-Audex-2B", "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-Audex-2B" \ --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-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nvidia/Nemotron-Labs-Audex-2B with Docker Model Runner:
docker model run hf.co/nvidia/Nemotron-Labs-Audex-2B
| [build-system] | |
| requires = ["setuptools>=61"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "audex-2b-vllm-plugin" | |
| version = "0.0.1" | |
| description = "Out-of-tree Audex-2B audio-understanding model and processor for vLLM 0.20." | |
| requires-python = ">=3.10" | |
| # Registers the Audex-2B model in every vLLM process (engine core + TP workers) | |
| # via vLLM's general-plugin mechanism. The dense backbone is registered by the | |
| # separate nemotron-dense-vllm plugin. The import package is `audex_2b_vllm` | |
| # (distinct from the 30B `audex_30b_a3b_vllm`) so both can be co-installed. | |
| [project.entry-points."vllm.general_plugins"] | |
| audex_2b = "audex_2b_vllm.plugin:register" | |
| [tool.setuptools] | |
| packages = ["audex_2b_vllm"] | |