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VibeVoice-Large-f16
aiguy110/VibeVoice-Large-f16
VibeVoice-Large-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 using incremental processing to avoid RAM exhaustion.
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Model card
From the Hugging Face model README
VibeVoice F16 Model (Incremental Conversion)
This model has been converted to float16 (f16) precision using incremental processing to avoid RAM exhaustion.
Conversion Details
- Original model: sheliak/VibeVoice-Large_Mirror
- Conversion method: Incremental shard-by-shard processing
- Mixed precision: True
- Total parameters: 9,343,355,363
- F16 parameters: 8,073,169,443 (86.4%)
- F32 parameters: 0 (0.0%)
- Model size: 15.04GB
- Memory savings: ~0.0%
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-Large-f16",
torch_dtype=torch.float16,
device_map="cpu"
)
processor = VibeVoiceProcessor.from_pretrained("./VibeVoice-Large-f16")
# Use --use_f16 flag with demo scripts
python demo/inference_from_file.py --model_path ./VibeVoice-Large-f16 --use_f16 --device cpu
RAM-Friendly Conversion
This model was converted using incremental processing, making it possible to convert large models on systems with limited RAM.
- Type
- vibevoice
- Created
- Sep 25, 2025
- Updated
- Sep 30, 2025