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
File size: 2,928 Bytes
c4bb14e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | """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)
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