Model Inference
Showing 19 of 19 projects
Run large language models (LLMs) privately on everyday desktops and laptops without requiring API calls or GPUs.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework 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.
Go binding for the MXNet C Predict API to perform inference with pre-trained deep learning models.
A lightweight, high-performance C++ wrapper for TensorFlow that simplifies development with a modern API.
A Node.js interface for running XGBoost models and making predictions.
A Go library using Cgo for blazing fast inference of CatBoost gradient boosting models.
Ruby bindings for XGBoost, enabling machine learning model inference in Ruby applications.
A thread-safe vector database for model inference built on LMDB.
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