Google announced new tooling for their TensorFlow Lite deep-learning framework that reduces the size of models and latency of inference. The tool converts a trained model's weights from floating ...

As part of the TensorFlow Lite library, the team has also released an on-device conversational model and a demo application with an example of a natural language application.

The Model Maker supports models available on the TensorFlow hub such as the EfficientNet-Lite models. In addition, it supports image classification and text classification.

After this, all weights and variable data are quantized, and the model is significantly smaller compared to the original TensorFlow Lite model. Full integer quantization. The integer quantization ...

TensorFlow Lite is a slimmed-down version of Google’s TensorFlow framework for training machine learning models. It’s a set of tools used by developers to run TensorFlow models on mobile ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

Given how mature TensorFlow Lite is compared to, say, the Gen AI model du jour (probably Mistral AI’s Codestral, which was released the day I wrote this), it’s worth looking at how TensorFlow ...

Google is rebranding TensorFlow Lite to LiteRT (as in “lite runtime”). This lets you deploy ML and AI models on Android, iOS, and embedded devices. Basically, for on-device AI at the Edge. Google ...

Google today announced TensorFlow Lite Model Maker, a tool that adapts state-of-the-art machine learning models to custom data sets using a technique known as transfer learning.It wraps machine ...

If models can run on device, at the edge, they can avoid the cloud and internet all-together. ... TensorFlow lite drives home the point that Google cares about the nexus of AI and mobile devices.