Text Generation
MLX
Safetensors
English
nvfp4
4-bit precision
quantized
mixture-of-experts
apple-silicon
custom_code
audio
audio-language-modeling
audio-understanding
text-to-speech
text-to-audio
speech-recognition
speech-translation
long-context
Instructions to use txgsync/Nemotron-Labs-Audex-30B-A3B-NVFP4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use txgsync/Nemotron-Labs-Audex-30B-A3B-NVFP4-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("txgsync/Nemotron-Labs-Audex-30B-A3B-NVFP4-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use txgsync/Nemotron-Labs-Audex-30B-A3B-NVFP4-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "txgsync/Nemotron-Labs-Audex-30B-A3B-NVFP4-mlx" --prompt "Once upon a time"
- Xet hash:
- c42b2326ee5672c6f48316e1d9637a7466e3c0fcaa99eea3509cddfe272d6b68
- Size of remote file:
- 1.31 GB
- SHA256:
- a395101dd53cc85593d3930923b0ba04b5aff00b7a1655f644481b857f458eee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.