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 Arsh9210/Nemotron-Labs-Audex-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arsh9210/Nemotron-Labs-Audex-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arsh9210/Nemotron-Labs-Audex-2B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Arsh9210/Nemotron-Labs-Audex-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Arsh9210/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 "Arsh9210/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": "Arsh9210/Nemotron-Labs-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arsh9210/Nemotron-Labs-Audex-2B
- SGLang
How to use Arsh9210/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 "Arsh9210/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": "Arsh9210/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 "Arsh9210/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": "Arsh9210/Nemotron-Labs-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arsh9210/Nemotron-Labs-Audex-2B with Docker Model Runner:
docker model run hf.co/Arsh9210/Nemotron-Labs-Audex-2B
| """Configuration for Nemotron-Dense Audex audio-understanding HF checkpoints.""" | |
| from __future__ import annotations | |
| from typing import Any, Optional | |
| from .modeling_nemotron_dense import NemotronDenseConfig | |
| class NemotronDenseAudexConfig(NemotronDenseConfig): | |
| """Nemotron-Dense text config plus NV-Whisper audio metadata. | |
| This class intentionally preserves all LLM fields from the baseline | |
| `NemotronDenseConfig` so existing `model.*` and `lm_head.*` weights load | |
| unchanged. Audio-specific fields describe the extra modules added by | |
| `modeling_nemotron_h_audio.py`. | |
| """ | |
| model_type = "nemotron_dense_audex" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| audio_config: Optional[dict[str, Any]] = None, | |
| audio_model_type: str = "NV-Whisper", | |
| sound_model_type: Optional[str] = None, | |
| audio_preprocessor_path: str = "audio_preprocessor", | |
| sound_token: str = "<so_embedding>", | |
| sound_start_token: str = "<so_start>", | |
| sound_end_token: str = "<so_end>", | |
| sound_token_id: Optional[int] = None, | |
| sound_start_token_id: Optional[int] = None, | |
| sound_end_token_id: Optional[int] = None, | |
| sound_embedding_size: int = 750, | |
| sound_clip_duration: float = 30.0, | |
| sound_target_rate: int = 16000, | |
| audio_encoder_hidden_size: int = 1280, | |
| audio_projector_intermediate_size: int = 4096, | |
| audio_projector_activation: str = "relu2", | |
| audio_projector_norm_eps: float = 1e-5, | |
| **kwargs, | |
| ): | |
| self.audio_config = audio_config or { | |
| "model_type": "qwen2_audio_encoder", | |
| "num_mel_bins": 128, | |
| "encoder_layers": 32, | |
| "encoder_attention_heads": 20, | |
| "encoder_ffn_dim": 5120, | |
| "d_model": audio_encoder_hidden_size, | |
| "activation_function": "gelu", | |
| "scale_embedding": False, | |
| "max_source_positions": 1500, | |
| } | |
| self.audio_model_type = audio_model_type | |
| self.sound_model_type = sound_model_type | |
| self.audio_preprocessor_path = audio_preprocessor_path | |
| self.sound_token = sound_token | |
| self.sound_start_token = sound_start_token | |
| self.sound_end_token = sound_end_token | |
| self.sound_token_id = sound_token_id | |
| self.sound_start_token_id = sound_start_token_id | |
| self.sound_end_token_id = sound_end_token_id | |
| self.sound_embedding_size = sound_embedding_size | |
| self.sound_clip_duration = sound_clip_duration | |
| self.sound_target_rate = sound_target_rate | |
| self.audio_encoder_hidden_size = audio_encoder_hidden_size | |
| self.audio_projector_intermediate_size = audio_projector_intermediate_size | |
| self.audio_projector_activation = audio_projector_activation | |
| self.audio_projector_norm_eps = audio_projector_norm_eps | |
| super().__init__(**kwargs) | |