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
| { | |
| "architectures": [ | |
| "NemotronDenseAudexForConditionalGeneration" | |
| ], | |
| "audio_config": { | |
| "activation_function": "gelu", | |
| "d_model": 1280, | |
| "encoder_attention_heads": 20, | |
| "encoder_ffn_dim": 5120, | |
| "encoder_layers": 32, | |
| "max_source_positions": 1500, | |
| "model_type": "qwen2_audio_encoder", | |
| "num_mel_bins": 128, | |
| "scale_embedding": false | |
| }, | |
| "audio_encoder_hidden_size": 1280, | |
| "audio_model_type": "NV-Whisper", | |
| "audio_preprocessor_path": "audio_preprocessor", | |
| "audio_projector_activation": "relu2", | |
| "audio_projector_intermediate_size": 4096, | |
| "audio_projector_norm_eps": 1e-05, | |
| "auto_map": { | |
| "AutoConfig": "configuration_nemotron_h_audio.NemotronDenseAudexConfig", | |
| "AutoModel": "modeling_nemotron_h_audio.NemotronDenseAudexForConditionalGeneration", | |
| "AutoModelForCausalLM": "modeling_nemotron_h_audio.NemotronDenseAudexForConditionalGeneration" | |
| }, | |
| "base_model_tp_plan": null, | |
| "dtype": "float32", | |
| "eos_token_id": 11, | |
| "head_dim": 128, | |
| "hidden_act": "relu2", | |
| "hidden_size": 2048, | |
| "intermediate_size": 9216, | |
| "max_position_embeddings": 131072, | |
| "model_type": "nemotron_dense_audex", | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 8, | |
| "rope_parameters": { | |
| "partial_rotary_factor": 1.0, | |
| "rope_theta": 100000000, | |
| "rope_type": "default" | |
| }, | |
| "sound_clip_duration": 30.0, | |
| "sound_embedding_size": 750, | |
| "sound_end_token": "<so_end>", | |
| "sound_end_token_id": 31, | |
| "sound_model_type": "hf:///nv-whisper", | |
| "sound_start_token": "<so_start>", | |
| "sound_start_token_id": 30, | |
| "sound_target_rate": 16000, | |
| "sound_token": "<so_embedding>", | |
| "sound_token_id": 29, | |
| "tie_word_embeddings": false, | |
| "tp_plan": null, | |
| "transformers_version": "5.4.0", | |
| "vocab_size": 205312 | |
| } | |