On-device Large Language Models with Keras and Android
Large language models have become increasingly popular in natural language processing tasks such as text classification, translation, and conversational agents. However, running these models on-device presents a challenge due to their size and computational requirements. In this article, we will explore how to build and deploy on-device large language models using Keras and Android.
Building Large Language Models with Keras
Keras is a high-level neural networks API written in Python, which makes it easy to build and train deep learning models. Large language models such as GPT-3 and BERT can be implemented using Keras with pre-trained weights, allowing developers to build powerful language processing capabilities.
Deploying Large Language Models on Android
Once a large language model has been built using Keras, the next step is deploying it on an Android device. This can be achieved by using TensorFlow Lite, a lightweight machine learning library for on-device inference. By converting the Keras model to a TensorFlow Lite model, it can be efficiently run on Android devices, enabling powerful language processing capabilities on-the-go.
Benefits of On-device Large Language Models
Deploying large language models on-device brings several benefits, including improved privacy and reduced latency. Since the models run locally on the device, user data is not sent to external servers, enhancing privacy and security. Additionally, on-device inference results in lower latency, enabling real-time language processing without relying on network connectivity.
Conclusion
On-device large language models offer a promising solution for enabling powerful language processing capabilities on mobile devices. By leveraging Keras for model development and TensorFlow Lite for deployment, developers can build and deploy advanced language models on Android devices, unlocking new possibilities for on-the-go natural language processing.
Overall, on-device large language models with Keras and Android pave the way for more efficient and privacy-focused language processing applications.
Have a burning question? Leave it in the comments below for a chance to get it answered by the TensorFlow team. 👇👇🏻👇🏿👇🏽 👇🏾👇🏼
I am trying to implement this using tflite_flutter but i keep getting "Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency flutter" error. i went ahead to add the dependency in my build.gradle file but the issue still persist. Does the Tensorflow team by chance have any implementation of LLM models in flutter? if yes I'd love a link to the article/video because i've been stuck on this for weeks
Can tensorflow lite be trained directly on microcontroller? meaning instead of training tensorflow on pc then converting to tensorflow lite and upload to microcontroller to run it there i want to directly train tensorflow lite on microcontroller, is it possible? thank you
Can you report the speed, latency or memory occupation of this application running on android?
I am wondering whether I can achieve on-device training, i.e., using local mobile data to fine-tune LLM.
Will this work on the web with tensorflow JS instead of android?
I have a question about YAMNet TensorFlow lite model (Android app). I want to use it with an audio clip as input, Not a live recording.
Can you help in that? Thank you for your help
So, GPT-2 can run on android devices, with a few delay responses, but of course GPT-2 isn't good as GPT-3 or 4.
1) How many years do you think that we need to have gpt-3 on our android phones?
2) What task could improve to have an agent like this in the phone?
3) We could have better prediction in our screen keyboard to the next word?
4) Recollect all chats whereas we are the sender and using it to feed the LLM and therefore setup automatic responses when we are out of our phone?
5) An ultimate advanced-reasoning virtual assistant better than google assistant and siri?
6) There is some security warnings about having an LLM like this in our phone? And if there is,what are the most recommended advices to handle and llm in our phone in the secure way?
7) And finally, what other IA types will be available for our phone ? I mean, speech-recognition, image generation, etc…