Best AI tools for< Quantize Onnx Model >

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop. It is designed to work with models from PyTorch, TorchFX, TensorFlow, ONNX, and OpenVINO™. NNCF offers samples demonstrating compression algorithms for various use cases and models, with the ability to add different compression algorithms easily. It supports GPU-accelerated layers, distributed training, and seamless combination of pruning, sparsity, and quantization algorithms. NNCF allows exporting compressed models to ONNX or TensorFlow formats for use with OpenVINO™ toolkit, and supports Accuracy-Aware model training pipelines via Adaptive Compression Level Training and Early Exit Training.

This repository provides a demonstration of running a native Large Language Model (LLM) on Android devices. It supports various models such as Qwen2.5-Instruct, MiniCPM-DPO/SFT, Yuan2.0, Gemma2-it, StableLM2-Chat/Zephyr, and Phi3.5-mini-instruct. The demo models are optimized for extreme execution speed after being converted from HuggingFace or ModelScope. Users can download the demo models from the provided drive link, place them in the assets folder, and follow specific instructions for decompression and model export. The repository also includes information on quantization methods and performance benchmarks for different models on various devices.

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

Nexa SDK is a comprehensive toolkit supporting ONNX and GGML models for text generation, image generation, vision-language models (VLM), and text-to-speech (TTS) capabilities. It offers an OpenAI-compatible API server with JSON schema mode and streaming support, along with a user-friendly Streamlit UI. Users can run Nexa SDK on any device with Python environment, with GPU acceleration supported. The toolkit provides model support, conversion engine, inference engine for various tasks, and differentiating features from other tools.

Intel® Neural Compressor is an open-source Python library that supports popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet. It provides key features, typical examples, and open collaborations, including support for a wide range of Intel hardware, validation of popular LLMs, and collaboration with cloud marketplaces, software platforms, and open AI ecosystems.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Custom AI prompt generator node for ComfyUI. With this node, you can use text generation models to generate prompts. Before using, text generation model has to be trained with prompt dataset.

Automatic is an Image Diffusion implementation with advanced features. It supports multiple diffusion models, built-in control for text, image, batch, and video processing, and is compatible with various platforms and backends. The tool offers optimized processing with the latest torch developments, built-in support for torch.compile, and multiple compile backends. It also features platform-specific autodetection, queue management, enterprise-level logging, and a built-in installer with automatic updates and dependency management. Automatic is mobile compatible and provides a main interface using StandardUI and ModernUI.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

Efficient Transformers Library provides reimplemented blocks of Large Language Models (LLMs) to make models functional and highly performant on Qualcomm Cloud AI 100. It includes graph transformations, handling for under-flows and overflows, patcher modules, exporter module, sample applications, and unit test templates. The library supports seamless inference on pre-trained LLMs with documentation for model optimization and deployment. Contributions and suggestions are welcome, with a focus on testing changes for model support and common utilities.

RTranslator is an almost open-source, free, and offline real-time translation app for Android. It offers Conversation mode for multi-user translations, WalkieTalkie mode for quick conversations, and Text translation mode. It uses Meta's NLLB for translation and OpenAi's Whisper for speech recognition, ensuring privacy. The app is optimized for performance and supports multiple languages. It is ad-free and donation-supported.

nncase is a neural network compiler for AI accelerators that supports multiple inputs and outputs, static memory allocation, operators fusion and optimizations, float and quantized uint8 inference, post quantization from float model with calibration dataset, and flat model with zero copy loading. It can be installed via pip and supports TFLite, Caffe, and ONNX ops. Users can compile nncase from source using Ninja or make. The tool is suitable for tasks like image classification, object detection, image segmentation, pose estimation, and more.

EZKL is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). It enables the following workflow: 1. Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow. 2. Export the final graph of operations as an .onnx file and some sample inputs to a .json file. 3. Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as: > "I ran this publicly available neural network on some private data and it produced this output" > "I ran my private neural network on some public data and it produced this output" > "I correctly ran this publicly available neural network on some public data and it produced this output" In the backend we use the collaboratively-developed Halo2 as a proof system. The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.