Showing 221-240 of 351 projects
Examples for using ONNX Runtime for machine learning inferencing.
SmoothQuant is an efficient post-training quantization tool for large language models, enabling accurate and fast inference.
PaddleSlim is an open-source library for deep model compression and architecture search.
A TensorFlow/Keras implementation of the Stable Diffusion AI model for text-to-image generation.
Infer.NET is a C# framework for running Bayesian inference in graphical models, useful for machine learning.
An AI-powered text-to-image generation model for creating high-quality images from text prompts.
A pre-trained Chinese language model for text generation, useful for AI-powered coding and content creation.
An ensemble sampling toolkit for affine-invariant Markov Chain Monte Carlo (MCMC) in Python.
High-quality implementations of standard and state-of-the-art methods for Bayesian and probabilistic machine learning.
Spearmint is a Bayesian optimization codebase for AI and machine learning experiments.
An efficient C++ implementation of the RWKV language model for fast CPU inference on various bit-width quantizations.
A high-performance text-to-3D generation model for building immersive 3D experiences with AI tools.
Official repository for work on micro-budget training of large-scale diffusion models for AI coding tools.
A fast and user-friendly runtime for running transformer models like BERT, GPT-2, and others on CPU and GPU.
An official implementation of DanceGRPO, a visual generation model that leverages GRPO techniques.
Official repository for the 'Big Transfer (BiT): General Visual Representation Learning' paper, focused on transfer learning for computer vision.
A repository for training large language models (LLMs) using QLoRA and FSDP techniques.
A reference implementation of MLPerf inference benchmarks for machine learning performance testing.
LiteRT is Google's on-device framework for high-performance ML & GenAI deployment on edge platforms.
Plugin to integrate Learning to Rank (machine learning for better relevance) with Elasticsearch
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