Best AI tools for< Reduce Model Size >

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

OpenAI Strawberry Model is a cutting-edge AI initiative that represents a significant leap in AI capabilities, focusing on enhancing reasoning, problem-solving, and complex task execution. It aims to improve AI's ability to handle mathematical problems, programming tasks, and deep research, including long-term planning and action. The project showcases advancements in AI safety and aims to reduce errors in AI responses by generating high-quality synthetic data for training future models. Strawberry is designed to achieve human-like reasoning and is expected to play a crucial role in the development of OpenAI's next major model, codenamed 'Orion.'

VModel.AI is an AI fashion models generator that revolutionizes on-model photography for fashion retailers. It utilizes artificial intelligence to create high-quality on-model photography without the need for elaborate photoshoots, reducing model photography costs by 90%. The tool helps diversify stores, improve E-commerce engagement, reduce returns, promote diversity and inclusion in fashion, and enhance product offerings.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

AiPlus is an AI tool designed to serve as a cost-efficient model gateway. It offers users a platform to access and utilize various AI models for their projects and tasks. With AiPlus, users can easily integrate AI capabilities into their applications without the need for extensive development or resources. The tool aims to streamline the process of leveraging AI technology, making it accessible to a wider audience.

Pulze.ai is a cloud-based AI-powered customer engagement platform that helps businesses automate their customer service and marketing efforts. It offers a range of features including a chatbot, live chat, email marketing, and social media management. Pulze.ai is designed to help businesses improve their customer satisfaction, increase sales, and reduce costs.

Watermelon is an AI customer service tool that integrates with OpenAI's latest model, GPT-4o. It allows users to build chatbots powered by GPT-4o to automate customer interactions, handle frequently asked questions, and collaborate seamlessly between chatbots and human agents. Watermelon offers features such as chatbot building, customizable chat widgets, statistics tracking, inbox collaboration, and various integrations with APIs and webhooks. The application caters to industries like e-commerce, education, healthcare, and financial services, providing solutions for sales support, lead generation, marketing, HR, and customer service.

Bot Butcher is an AI-powered antispam API for websites that helps web developers combat contact form spam bots using artificial intelligence. It offers a modern alternative to reCAPTCHA, maximizing privacy by classifying messages as spam or not spam with a large language model. The tool is designed for enterprise scalability, vertical SaaS, and website builder apps, providing continuous model improvements and context-aware classification while focusing on privacy.

The AI Lean Canvas Generator is an AI-powered tool designed to help businesses create Lean Canvas models quickly and efficiently. It utilizes artificial intelligence to generate Lean Canvas based on company descriptions, providing a strategic management and entrepreneurial tool for validating business models. The tool streamlines the process of summarizing key aspects of a business model, such as target market, value proposition, revenue streams, cost structure, and key metrics. Developed by Ash Maurya, the Lean Canvas Generator supports the Lean Startup methodology, enabling rapid experimentation and iterative development to reduce risk and uncertainty in the early stages of a business. It is a flexible and adaptable tool that can evolve with the company's business model over time.

SellerPic is an AI image tool designed specifically for e-commerce sellers to enhance their product images effortlessly. It offers AI Fashion Model and AI Product Image features to transform DIY snapshots into professional, studio-quality images in a fraction of the time. SellerPic focuses on boosting sales by providing high-quality product images that captivate the audience and improve conversion rates. Trusted by sellers across various platforms, SellerPic is a game-changer for e-commerce marketing teams, creatives, and store owners.

Anycores is an AI tool designed to optimize the performance of deep neural networks and reduce the cost of running AI models in the cloud. It offers a platform that provides automated solutions for tuning and inference consultation, optimized networks zoo, and platform for reducing AI model cost. Anycores focuses on faster execution, reducing inference time over 10x times, and footprint reduction during model deployment. It is device agnostic, supporting Nvidia, AMD GPUs, Intel, ARM, AMD CPUs, servers, and edge devices. The tool aims to provide highly optimized, low footprint networks tailored to specific deployment scenarios.

VirtuLook Product Photo Generator is an AI-powered tool that revolutionizes product photography by generating high-quality images using cutting-edge AI algorithms. It offers features like fashion model generation, product background generation, and text-based photo generation. The tool helps businesses enhance their online presence, drive sales conversions, and reduce production costs by providing visually appealing product images. Users can easily create lifelike photos of virtual models, experiment with different looks, and visualize clothing creations without the need for physical prototypes or expensive photo shoots.

ASKTOWEB is an AI-powered service that enhances websites by adding AI search buttons to SaaS landing pages, software documentation pages, and other websites. It allows visitors to easily search for information without needing specific keywords, making websites more user-friendly and useful. ASKTOWEB analyzes user questions to improve site content and discover customer needs. The service offers multi-model accuracy verification, direct reference jump links, multilingual chatbot support, effortless attachment with a single line of script, and a simple UI without annoying pop-ups. ASKTOWEB reduces the burden on customer support by acting as a buffer for inquiries about available information on the website.

Directly is an AI-powered platform that offers on-demand and automated customer support solutions. The platform connects organizations with highly qualified experts who can handle customer inquiries efficiently. By leveraging AI and machine learning, Directly automates repetitive questions, improving business continuity and digital transformation. The platform follows a pay-for-performance compensation model and provides global support in multiple languages. Directly aims to enhance customer satisfaction, reduce contact center volume, and save costs for businesses.

Wild Moose is an AI-powered tool designed to streamline incident response and site reliability engineering processes. It offers fast and efficient root cause analysis by automatically gathering and analyzing logs, metrics, and code to pinpoint issues. The tool converts tribal knowledge into custom playbooks, constantly improves performance with a learning system model, and integrates seamlessly with existing observability and alerting tools. Wild Moose helps users quickly identify root causes with real-time production data, reducing downtime and empowering engineers to focus on strategic work.

Clarifai is an AI Workflow Orchestration Platform that helps businesses establish an AI Operating Model and transition from prototype to production efficiently. It offers end-to-end solutions for operationalizing AI, including Retrieval Augmented Generation (RAG), Generative AI, Digital Asset Management, Visual Inspection, Automated Data Labeling, and Content Moderation. Clarifai's platform enables users to build and deploy AI faster, reduce development costs, ensure oversight and security, and unlock AI capabilities across the organization. The platform simplifies data labeling, content moderation, intelligence & surveillance, generative AI, content organization & personalization, and visual inspection. Trusted by top enterprises, Clarifai helps companies overcome challenges in hiring AI talent and misuse of data, ultimately leading to AI success at scale.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

Dynamiq is an operating platform for GenAI applications that enables users to build compliant GenAI applications in their own infrastructure. It offers a comprehensive suite of features including rapid prototyping, testing, deployment, observability, and model fine-tuning. The platform helps streamline the development cycle of AI applications and provides tools for workflow automations, knowledge base management, and collaboration. Dynamiq is designed to optimize productivity, reduce AI adoption costs, and empower organizations to establish AI ahead of schedule.

Slicker is an AI-powered tool designed to recover failed subscription payments and maximize subscription revenue for businesses. It uses a proprietary AI engine to process each failing payment individually, converting past due invoices into revenue. With features like payment recovery on auto-pilot, state-of-the-art machine learning model, lightning-fast setup, in-depth payment analytics, and enterprise-grade security, Slicker offers a comprehensive solution to reduce churn and boost revenue. The tool is fully transparent, allowing users to inspect and review every action taken by the AI engine. Slicker seamlessly integrates with popular billing and payment platforms, making it easy to implement and start seeing results quickly.

FairPlay is a Fairness-as-a-Service solution designed for financial institutions, offering AI-powered tools to assess automated decisioning models quickly. It helps in increasing fairness and profits by optimizing marketing, underwriting, and pricing strategies. The application provides features such as Fairness Optimizer, Second Look, Customer Composition, Redline Status, and Proxy Detection. FairPlay enables users to identify and overcome tradeoffs between performance and disparity, assess geographic fairness, de-bias proxies for protected classes, and tune models to reduce disparities without increasing risk. It offers advantages like increased compliance, speed, and readiness through automation, higher approval rates with no increase in risk, and rigorous Fair Lending analysis for sponsor banks and regulators. However, some disadvantages include the need for data integration, potential bias in AI algorithms, and the requirement for technical expertise to interpret results.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

MNN is a highly efficient and lightweight deep learning framework that supports inference and training of deep learning models. It has industry-leading performance for on-device inference and training. MNN has been integrated into various Alibaba Inc. apps and is used in scenarios like live broadcast, short video capture, search recommendation, and product searching by image. It is also utilized on embedded devices such as IoT. MNN-LLM and MNN-Diffusion are specific runtime solutions developed based on the MNN engine for deploying language models and diffusion models locally on different platforms. The framework is optimized for devices, supports various neural networks, and offers high performance with optimized assembly code and GPU support. MNN is versatile, easy to use, and supports hybrid computing on multiple devices.

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.

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.

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.

Welcome to the ultimate hub for on-device Large Language Models (LLMs)! This repository is your go-to resource for all things related to LLMs designed for on-device deployment. Whether you're a seasoned researcher, an innovative developer, or an enthusiastic learner, this comprehensive collection of cutting-edge knowledge is your gateway to understanding, leveraging, and contributing to the exciting world of on-device LLMs.

ZhiLight is a highly optimized large language model (LLM) inference engine developed by Zhihu and ModelBest Inc. It accelerates the inference of models like Llama and its variants, especially on PCIe-based GPUs. ZhiLight offers significant performance advantages compared to mainstream open-source inference engines. It supports various features such as custom defined tensor and unified global memory management, optimized fused kernels, support for dynamic batch, flash attention prefill, prefix cache, and different quantization techniques like INT8, SmoothQuant, FP8, AWQ, and GPTQ. ZhiLight is compatible with OpenAI interface and provides high performance on mainstream NVIDIA GPUs with different model sizes and precisions.

DB-GPT-Hub is an experimental project leveraging Large Language Models (LLMs) for Text-to-SQL parsing. It includes stages like data collection, preprocessing, model selection, construction, and fine-tuning of model weights. The project aims to enhance Text-to-SQL capabilities, reduce model training costs, and enable developers to contribute to improving Text-to-SQL accuracy. The ultimate goal is to achieve automated question-answering based on databases, allowing users to execute complex database queries using natural language descriptions. The project has successfully integrated multiple large models and established a comprehensive workflow for data processing, SFT model training, prediction output, and evaluation.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

DashInfer is a C++ runtime tool designed to deliver production-level implementations highly optimized for various hardware architectures, including x86 and ARMv9. It supports Continuous Batching and NUMA-Aware capabilities for CPU, and can fully utilize modern server-grade CPUs to host large language models (LLMs) up to 14B in size. With lightweight architecture, high precision, support for mainstream open-source LLMs, post-training quantization, optimized computation kernels, NUMA-aware design, and multi-language API interfaces, DashInfer provides a versatile solution for efficient inference tasks. It supports x86 CPUs with AVX2 instruction set and ARMv9 CPUs with SVE instruction set, along with various data types like FP32, BF16, and InstantQuant. DashInfer also offers single-NUMA and multi-NUMA architectures for model inference, with detailed performance tests and inference accuracy evaluations available. The tool is supported on mainstream Linux server operating systems and provides documentation and examples for easy integration and usage.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

LLMGA (Multimodal Large Language Model-based Generation Assistant) is a tool that leverages Large Language Models (LLMs) to assist users in image generation and editing. It provides detailed language generation prompts for precise control over Stable Diffusion (SD), resulting in more intricate and precise content in generated images. The tool curates a dataset for prompt refinement, similar image generation, inpainting & outpainting, and visual question answering. It offers a two-stage training scheme to optimize SD alignment and a reference-based restoration network to alleviate texture, brightness, and contrast disparities in image editing. LLMGA shows promising generative capabilities and enables wider applications in an interactive manner.

CodeGeeX4-ALL-9B is an open-source multilingual code generation model based on GLM-4-9B, offering enhanced code generation capabilities. It supports functions like code completion, code interpreter, web search, function call, and repository-level code Q&A. The model has competitive performance on benchmarks like BigCodeBench and NaturalCodeBench, outperforming larger models in terms of speed and performance.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

AQLM is the official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. It includes prequantized AQLM models without PV-Tuning and PV-Tuned models for LLaMA, Mistral, and Mixtral families. The repository provides inference examples, model details, and quantization setups. Users can run prequantized models using Google Colab examples, work with different model families, and install the necessary inference library. The repository also offers detailed instructions for quantization, fine-tuning, and model evaluation. AQLM quantization involves calibrating models for compression, and users can improve model accuracy through finetuning. Additionally, the repository includes information on preparing models for inference and contributing guidelines.

Colossal-AI is a deep learning system for large-scale parallel training. It provides a unified interface to scale sequential code of model training to distributed environments. Colossal-AI supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer.

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