You can deploy this model and start earning money today!

aiguy110/
VibeVoice-1.5B-f16

Name: VibeVoice-1.5B-f16
Availability: InStock

safetensors

vibevoice

Views

No views yet

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

python

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

aiguy110/VibeVoice-1.5B-f16

VibeVoice F16 Model

Conversion Details

Usage

Notes

aiguy110/
VibeVoice-1.5B-f16