Instructions to use Arsh9210/Nemotron-Labs-Audex-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Nemotron-Labs-Audex-2B / audex_causal_speech_decoder /README.md

Arsh9210

Added audex_causal_speech_decoder/README.md

0b41c93 verified 10 days ago

preview code

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Standalone Audex Causal Speech Decoder

Audex streaming decoder for speech codec token frames packaged as Hugging Face remote code.

from transformers import AutoModel

decoder = AutoModel.from_pretrained(
    "path/to/audex_causal_speech_decoder",
    trust_remote_code=True,
).cuda().eval()
session = decoder.create_session(chunk_frames=75)

for sample_rate, samples in session.push([[123], [456]]):
    ...
for sample_rate, samples in session.flush():
    ...