It seems you haven’t provided a specific topic or question for the introduction. Please share the topic or question you’d like me to create an introduction for, and I’ll be happy to assist you!
GPTQ Quantization in Keras
What is GPTQ? GPTQ ("Generative Pre-Training Quantization") is a post-training, weight-only quantization method that uses a second-order approximation of the loss (via a Hessian estimate) to minimize ...
📚 Read more at Keras Developer guides🔎 Find similar documents
Writing Quantization-Compatible Layers in Keras
What are quantization-compatible layers? Keras lets you optimize models via post-training quantization (PTQ) by calling the layer.quantize(...) or model.quantize(...) APIs. Keras exposes an extensible...
📚 Read more at Keras Developer guides🔎 Find similar documents
8-bit Integer Quantization in Keras
What is INT8 quantization? Quantization lowers the numerical precision of weights and activations to reduce memory use and often speed up inference, at the cost of a small accuracy drop. Moving from f...
📚 Read more at Keras Developer guides🔎 Find similar documents
INT4 Quantization in Keras
What is INT4 quantization? Quantization lowers the numerical precision of weights and activations to reduce memory use and often speed up inference, at the cost of a small accuracy drop. INT4 post-tra...
📚 Read more at Keras Developer guides🔎 Find similar documents
Quantization in Keras
Introduction Modern large models are often memory- and bandwidth-bound : most inference time is spent moving tensors between memory and compute units rather than doing math. Quantization reduces the n...
📚 Read more at Keras Developer guides🔎 Find similar documents
Orbax Checkpointing in Keras
Introduction Orbax is the default checkpointing library recommended for JAX ecosystem users. It is a high-level checkpointing library which provides functionality for both checkpoint management and co...
📚 Read more at Keras Developer guides🔎 Find similar documents
How to use Keras with NNX backend
Workflow 1: The Classic Keras Experience (model.fit) Create a Keras Model Create Dummy Data Compile and Fit Verify a change As you can see, your existing Keras code works out-of-the-box, giving you a ...
📚 Read more at Keras Developer guides🔎 Find similar documents
Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
📚 Read more at Keras Developer guides🔎 Find similar documents
Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
📚 Read more at Keras Developer guides🔎 Find similar documents
Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
📚 Read more at Keras Developer guides🔎 Find similar documents
Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
📚 Read more at Keras Developer guides🔎 Find similar documents
Getting Started with KerasHub
Installation and Setup To begin, let's install keras-hub. The library is available on PyPI, so we can simply install it with pip. Keras 3 was built to work on top of TensorFlow, Jax, and Torch backend...
📚 Read more at Keras Developer guides🔎 Find similar documents