Quantize a model for faster inference
Measure BERT model performance using WASM and WebGPU
Browse and evaluate ML tasks in MLIP Arena
Calculate VRAM requirements for LLM models
Track, rank and evaluate open LLMs and chatbots
Display benchmark results
Multilingual Text Embedding Model Pruner
Evaluate adversarial robustness using generative models
Text-To-Speech (TTS) Evaluation using objective metrics.
Benchmark AI models by comparison
Measure over-refusal in LLMs using OR-Bench
Upload a machine learning model to Hugging Face Hub
Predict customer churn based on input details
NNCF quantization is a technique used to optimize neural networks by reducing the precision of their weights and activations. This process, also known as model quantization, enables faster inference while maintaining acceptable accuracy. The Neural Network Compression Framework (NNCF) provides tools to apply quantization and other optimization methods to deep learning models. It is primarily designed to help deploy models efficiently on various hardware platforms.
Install NNCF: Start by installing the NNCF library using pip or another package manager.
pip install nncf
Load your model: Import your pre-trained model from a supported framework like TensorFlow or PyTorch.
Apply quantization: Use NNCF's built-in functions to apply quantization to your model. For example:
from nncf import Quantization
quantized_model = Quantization.apply(model)
Evaluate accuracy: Validate the performance of your quantized model to ensure it meets your requirements.
Fine-tune if necessary: If the accuracy is compromised, use quantization-aware training (QAT) to fine-tune the model.
Export the model: Once satisfied with the results, export the quantized model for deployment.
Deploy the model: Use the optimized model in your application, leveraging the speed improvements of quantization.
What is the primary purpose of NNCF quantization?
The primary purpose of NNCF quantization is to reduce the computational and memory requirements of neural networks, enabling faster inference while maintaining acceptable model performance.
How does NNCF quantization affect model accuracy?
NNCF quantization can lead to a small reduction in model accuracy due to the reduced precision of weights and activations. However, techniques like quantization-aware training (QAT) can help minimize this impact.
Can I use NNCF quantization with any deep learning framework?
NNCF quantization is compatible with popular frameworks like TensorFlow and PyTorch, but it may require additional adjustments for less common frameworks or custom models.
What is the difference between post-training quantization and quantization-aware training (QAT)?
Post-training quantization is applied to a pre-trained model without retraining, while QAT involves retraining the model during the quantization process to better adapt to the reduced precision. QAT typically results in better accuracy for the quantized model.