Showing 13 of 13 projects
Run large language models (LLMs) privately on everyday desktops and laptops without requiring API calls or GPUs.
An industrial deep learning framework supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An LLM acceleration library for Intel XPU (GPU, NPU, CPU) to speed up local inference and finetuning of popular models.
A .NET binding to the TensorFlow C API for running existing machine learning models in C# and F#.
A high-level Deep Learning API for JVM and Android developers, written in Kotlin and inspired by Keras.
A uniform interface to run deep learning models from multiple frameworks like TensorFlow, PyTorch, and Keras in C++ and Python.
Run ONNX transformer pipelines (like Hugging Face) natively in Go for inference and fine-tuning, with support for CPU, GPU, and TPU.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
A pure Go package for running inference with pre-trained Transformer models from Hugging Face, enabling NLP tasks without external languages.
A lightweight Swift library for tensor calculations with TensorFlow-like APIs, enabling ML model inference.
A TensorFlow C API wrapper enabling machine learning in server-side Swift applications.
A type-safe, functional ONNX API and backend for deep learning and classical machine learning in Scala 3.
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