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: 1,812 Bytes
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"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
}
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