Audio-Text-to-Text
Transformers
Safetensors
step_audio_2
text-generation
audio-reasoning
chain-of-thought
multi-modal
step-audio-r1
custom_code
8-bit precision
compressed-tensors
Instructions to use TransWithAI/Step-Audio-R1-NVFP4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWithAI/Step-Audio-R1-NVFP4A16 with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TransWithAI/Step-Audio-R1-NVFP4A16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d1215c8b48480570e10a6e4593fcbd5da7cc035580b45b22fb5875e58f23df9
- Size of remote file:
- 4.94 GB
- SHA256:
- 8163e602803002f0fec5eec86605dcffc1eb2a8d9c78ffff87efed340f3439f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.