NeoZhangJianyu/pr_test_log
Save the log of testing of llama.cpp SYCL PRs
Save the log of testing of llama.cpp SYCL PRs
Get up and running with Llama 3.3, Phi 4, Gemma 2, and other large language models.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Port of Facebook's LLaMA model in C/C++
This is samples for Inte Neural Compressor
Demo TF Model
Darknet on DPCPP for Intel GPU
Download the files in URL folder
User cases for Profiler of PyTorch on XPU and CUDA
Help user use Intel GPU.
This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise
This repo contains documents of the OPEA project
Containerization and cloud native suite for OPEA
Evaluation, benchmark, and scorecard, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination
BigDL: Distributed Deep Learning Library for Apache Spark
GenAI Studio is a low code platform to enable users to construct, evaluate, and benchmark GenAI applications. The platform also provide capability to export developed application as a ready-to-deploy package for immediate enterprise integration.
Step-by-step Deep Leaning Tutorials on Apache Spark using BigDL
GenAI components at micro-service level; GenAI service composer to create mega-service
Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.
Samples for Intel® oneAPI Toolkits
AI Starter Kit for traffic camera object detection using Intel® Extension for Pytorch
Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.
Efficient neural speech synthesis
heterogeneity-aware-lowering-and-optimization