Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.
-
Updated
Jun 27, 2024 - Python
Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.
CLIP as a service - Embed image and sentences, object recognition, visual reasoning, image classification and reverse image search
Resources of our survey paper "A Systematic Review of AI Deployment on Resource-Constrained Edge Devices: Challenges, Techniques, and Applications"
Streamlining the process for seamless execution of PyCoral in running TensorFlow Lite models on an Edge TPU USB.
Генерация описаний к изображениям с помощью различных архитектур нейронных сетей
Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.
The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.
Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.
The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.
Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.
Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.
Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.
Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.
This repository contains Python code to classify fashion items using a Convolutional Neural Network (CNN) implemented with TensorFlow and Keras. It includes data preprocessing, model building, training, evaluation, and visualization of results.
CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline
Add a description, image, and links to the model-inference topic page so that developers can more easily learn about it.
To associate your repository with the model-inference topic, visit your repo's landing page and select "manage topics."