Quantize a model for faster inference
Calculate survival probability based on passenger details
Evaluate LLM over-refusal rates with OR-Bench
Multilingual Text Embedding Model Pruner
Display leaderboard of language model evaluations
Create and manage ML pipelines with ZenML Dashboard
Determine GPU requirements for large language models
Load AI models and prepare your space
Evaluate model predictions with TruLens
Benchmark LLMs in accuracy and translation across languages
Generate and view leaderboard for LLM evaluations
Open Persian LLM Leaderboard
Submit models for evaluation and view leaderboard
NNCF (Neural Network Compression Framework) quantization is a technique used to reduce the size of deep learning models and improve inference speed by converting model weights and activations from floating-point to lower-bit integer representations. This process maintains model accuracy while enabling faster and more efficient deployment on resource-constrained devices.
pip install nncf.from nncf import NNCFConfig to your code.NNCFConfig.What models are supported by NNCF quantization?
NNCF supports a wide range of models, including popular architectures like MobileNet, ResNet, and Inception. It is framework-agnostic and works with TensorFlow, PyTorch, and ONNX models.
Is NNCF quantization free to use?
Yes, NNCF is open-source and free to use under the Apache 2.0 license. It is actively maintained by Intel and the OpenVINO community.
How does NNCF ensure accuracy after quantization?
NNCF employs quantization-aware training and automatic accuracy recovery techniques to minimize accuracy loss. These methods fine-tune the model during quantization to maintain performance.