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
Added audex_causal_speech_decoder/configuration_audex_causal_speech_decoder.py
Browse files
audex_causal_speech_decoder/configuration_audex_causal_speech_decoder.py
ADDED
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# coding=utf-8
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# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from transformers import PretrainedConfig
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class AudexCausalSpeechDecoderConfig(PretrainedConfig):
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model_type = "audex_causal_speech_decoder"
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def __init__(
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self,
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hidden_dim: int = 2048,
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depth: int = 12,
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heads: int = 32,
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pos_meb_dim: int = 64,
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hop_length: int = 320,
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vq_dim: int = 2048,
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lookahead_steps: int = 4,
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sample_rate: int = 16000,
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codebook_levels: list[int] | None = None,
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codebook_size: int = 65536,
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token_embed_dim: int = 8,
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embed_tokens_from_codes: bool = True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_dim = hidden_dim
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self.depth = depth
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self.heads = heads
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self.pos_meb_dim = pos_meb_dim
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self.hop_length = hop_length
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self.vq_dim = vq_dim
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self.lookahead_steps = lookahead_steps
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self.sample_rate = sample_rate
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self.codebook_levels = codebook_levels or [4, 4, 4, 4, 4, 4, 4, 4]
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self.codebook_size = codebook_size
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self.token_embed_dim = token_embed_dim
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self.embed_tokens_from_codes = embed_tokens_from_codes
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