Downloads · 30 days
7
25% of all-time downloads
VibeVoice-1.5B-f16
aiguy110/VibeVoice-1.5B-f16
VibeVoice-1.5B-f16 is a machine learning model from aiguy110. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model has been converted to float16 (f16) precision for reduced memory usage.
All-time downloads
28
Public
Parameters
2.7B
5.9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors5.9 GB · 100%
Parameter types
How the weights are stored.
F162.5B · 91%
Model card
From the Hugging Face model README
VibeVoice F16 Model
This model has been converted to float16 (f16) precision for reduced memory usage.
Conversion Details
- Original model: microsoft/VibeVoice-1.5B
- Mixed precision: True
- Memory savings: ~45.7%
- Original size: 10.07 GB
- Converted size: 5.47 GB
Usage
from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
# Load with f16 precision
model = VibeVoiceForConditionalGenerationInference.from_pretrained(
"./VibeVoice-1.5B-f16",
torch_dtype=torch.float16,
device_map="cpu" # or "cuda" for GPU
)
processor = VibeVoiceProcessor.from_pretrained("./VibeVoice-1.5B-f16")
# Use --use_f16 flag with demo scripts
python demo/inference_from_file.py --model_path ./VibeVoice-1.5B-f16 --use_f16 --device cpu
Notes
- F16 precision may result in minor quality differences compared to f32
- Some operations automatically upcast to f32 for numerical stability
- Optimized for CPU inference, but also works on CUDA GPUs
- Type
- vibevoice
- Created
- Sep 25, 2025
- Updated
- Sep 25, 2025