Best AI tools for< Optimize Model Size >

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

Uwear.ai is an AI tool that allows users to easily create stunningly complete photos of AI-generated fashion models in just a few clicks. It turns flat lay photos into studio-quality model visuals, eliminating the need for 3D designs, mannequins, or photos of the product already worn. The platform uses its proprietary AI model to keep the generated clothes as close as possible to the input, helping users sell more by generating realistic human models of different sizes and ethnicities.

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.

IngestAI is a Silicon Valley-based startup that provides a sophisticated toolbox for data preparation and model selection, powered by proprietary AI algorithms. The company's mission is to make AI accessible and affordable for businesses of all sizes. IngestAI's platform offers a turn-key service tailored for AI builders seeking to optimize AI application development. The company identifies the model best-suited for a customer's needs, ensuring it is designed for high performance and reliability. IngestAI utilizes Deepmark AI, its proprietary software solution, to minimize the time required to identify and deploy the most effective AI solutions. IngestAI also provides data preparation services, transforming raw structured and unstructured data into high-quality, AI-ready formats. This service is meticulously designed to ensure that AI models receive the best possible input, leading to unparalleled performance and accuracy. IngestAI goes beyond mere implementation; the company excels in fine-tuning AI models to ensure that they match the unique nuances of a customer's data and specific demands of their industry. IngestAI rigorously evaluates each AI project, not only ensuring its successful launch but its optimal alignment with a customer's business goals.

Provamoda is a virtual fashion photoshoot tool designed for e-commerce businesses. It utilizes AI technology to generate high-quality and accurate images for online stores without the need for traditional photoshoots. Users can upload clothing images, select model types and backgrounds, and receive multiple photos for each piece of clothing. The tool offers cost-effective, time-efficient, and customizable virtual photoshoots, catering to businesses of all sizes. With features like generating videos, bottom and top clothes support, and high-resolution images, Provamoda empowers fashion brands to create professional visuals effortlessly.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

Promptech is an AI teamspace designed to streamline workflows and enhance productivity. It offers a range of features including AI assistants, a collaborative teamspace, and access to large language models (LLMs). Promptech is suitable for businesses of all sizes and can be used for a variety of tasks such as streamlining tasks, enhancing collaboration, and safeguarding IP. It is a valuable resource for technology leaders and provides a cost-effective AI solution for smaller teams and startups.

Market Brew is an AI SEO software platform that offers advanced SEO modeling services backed by a team of search engineers. Trusted by over 60,000 brands since 2006, Market Brew empowers SEO teams to optimize and grow their rankings with precision. The platform utilizes proprietary search engine models to track algorithmic changes, prioritize optimizations, and forecast search engine rankings. Market Brew's SEO software is built with machine learning algorithms and is designed to be user-friendly and scalable for businesses of all sizes.

Gemini AI is a cutting-edge AI and ML solutions provider that focuses on accelerating innovation through artificial intelligence. The company is leading the revolution of artificial intelligence for augmented intelligence, leveraging the power of AI and ML to solve humankind's most challenging problems. Gemini AI specializes in areas such as computer vision, geospatial science, human health, and integrative technologies. Their services include data and sensors analysis, modeling using deep learning techniques, and deployment of predictive models for real-time insights.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

Creatus.AI is an AI-powered platform that provides a range of tools and services to help businesses boost productivity and transform their workplaces. With over 35 AI models and tools, and 90+ business integrations, Creatus.AI offers a comprehensive suite of solutions for businesses of all sizes. The platform's AI-native workspace and autonomous team members enable businesses to automate tasks, improve efficiency, and gain valuable insights from data. Creatus.AI also specializes in custom AI integrations and solutions, helping businesses to tailor AI solutions to their specific needs.

ficc.ai is an AI application that revolutionizes municipal bond pricing by providing real-time, accurate AI technology for informed decisions, portfolio optimization, and compliance. The platform offers a user-friendly web app, direct API access, and integration with existing software or vendors. ficc.ai uses cutting-edge AI models developed in-house by market experts and scientists to deliver highly accurate bond prices based on trade size, ensuring valuable output for trading decisions, investment allocations, and compliance oversight.

Notification Harbor is an email marketing platform that uses Large Language Models (LLMs) to help businesses create and send more effective email campaigns. With Notification Harbor, businesses can use LLMs to generate personalized email content, optimize subject lines, and even create entire email campaigns from scratch. Notification Harbor is designed to make email marketing easier and more effective for businesses of all sizes.

Google Gemma is a lightweight, state-of-the-art open language model (LLM) developed by Google. It is part of the same research used in the creation of Google's Gemini models. Gemma models come in two sizes, the 2B and 7B parameter versions, where each has a base (pre-trained) and instruction-tuned modifications. Gemma models are designed to be cross-device compatible and optimized for Google Cloud and NVIDIA GPUs. They are also accessible through Kaggle, Hugging Face, Google Cloud with Vertex AI or GKE. Gemma models can be used for a variety of applications, including text generation, summarization, RAG, and both commercial and research use.

LAHgen is a free unlimited text to image generator that does not require any login. It offers an advanced AI model for creating high-quality images for various creative purposes. Users can input prompts and select from a range of image sizes to generate stunning visuals. The tool is optimized for producing realistic, anime, digital art, and 3D cinematic images with advanced options for customization.

EnergeticAI is an open-source AI library that can be used in Node.js applications. It is optimized for serverless environments and provides fast cold-start, small module size, and pre-trained models. EnergeticAI can be used for a variety of tasks, including building recommendations, classifying text, and performing semantic search.

Valohai is a scalable MLOps platform that enables Continuous Integration/Continuous Deployment (CI/CD) for machine learning and pipeline automation on-premises and across various cloud environments. It helps streamline complex machine learning workflows by offering framework-agnostic ML capabilities, automatic versioning with complete lineage of ML experiments, hybrid and multi-cloud support, scalability and performance optimization, streamlined collaboration among data scientists, IT, and business units, and smart orchestration of ML workloads on any infrastructure. Valohai also provides a knowledge repository for storing and sharing the entire model lifecycle, facilitating cross-functional collaboration, and allowing developers to build with total freedom using any libraries or frameworks.

SambaNova Systems is an AI platform that revolutionizes AI workloads by offering an enterprise-grade full stack platform purpose-built for generative AI. It provides state-of-the-art AI and deep learning capabilities to help customers outcompete their peers. SambaNova delivers the only enterprise-grade full stack platform, from chips to models, designed for generative AI in the enterprise. The platform includes the SN40L Full Stack Platform with 1T+ parameter models, Composition of Experts, and Samba Apps. SambaNova also offers resources to accelerate AI journeys and solutions for various industries like financial services, healthcare, manufacturing, and more.

Lexset is an AI tool that provides synthetic data generation services for computer vision model training. It offers a no-code interface to create unlimited data with advanced camera controls and lighting options. Users can simulate AI-scale environments, composite objects into images, and create custom 3D scenarios. Lexset also provides access to GPU nodes, dedicated support, and feature development assistance. The tool aims to improve object detection accuracy and optimize generalization on high-quality synthetic data.

Contlo is an AI-powered marketing platform that helps businesses create personalized campaigns and automated customer journeys across multiple channels, including email, SMS, WhatsApp, web push, and social media. It uses a brand's own generative AI model to optimize marketing efforts and drive customer engagement. Contlo also offers audience management, data collection, and business insights to help businesses make informed decisions.

cake is a pure Rust implementation of the llama3 LLM distributed inference based on Candle. The project aims to enable running large models on consumer hardware clusters of iOS, macOS, Linux, and Windows devices by sharding transformer blocks. It allows running inferences on models that wouldn't fit in a single device's GPU memory by batching contiguous transformer blocks on the same worker to minimize latency. The tool provides a way to optimize memory and disk space by splitting the model into smaller bundles for workers, ensuring they only have the necessary data. cake supports various OS, architectures, and accelerations, with different statuses for each configuration.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

Efficient Multimodal Large Language Models: A Survey provides a comprehensive review of efficient and lightweight Multimodal Large Language Models (MLLMs), focusing on model size reduction and cost efficiency for edge computing scenarios. The survey covers the timeline of efficient MLLMs, research on efficient structures and strategies, and their applications, while also discussing current limitations and future directions.

This repository contains the training code of LLM-QAT for large language models. The work investigates quantization-aware training for LLMs, including quantizing weights, activations, and the KV cache. Experiments were conducted on LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. Significant improvements were observed when quantizing weight, activations, and kv cache to 4-bit, 8-bit, and 4-bit, respectively.

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.

LLM-Blender is a framework for ensembling large language models (LLMs) to achieve superior performance. It consists of two modules: PairRanker and GenFuser. PairRanker uses pairwise comparisons to distinguish between candidate outputs, while GenFuser merges the top-ranked candidates to create an improved output. LLM-Blender has been shown to significantly surpass the best LLMs and baseline ensembling methods across various metrics on the MixInstruct benchmark dataset.

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.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

Awesome Text2SQL is a curated repository containing tutorials and resources for Large Language Models, Text2SQL, Text2DSL, Text2API, Text2Vis, and more. It provides guidelines on converting natural language questions into structured SQL queries, with a focus on NL2SQL. The repository includes information on various models, datasets, evaluation metrics, fine-tuning methods, libraries, and practice projects related to Text2SQL. It serves as a comprehensive resource for individuals interested in working with Text2SQL and related technologies.

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.

GLake is an acceleration library and utilities designed to optimize GPU memory management and IO transmission for AI large model training and inference. It addresses challenges such as GPU memory bottleneck and IO transmission bottleneck by providing efficient memory pooling, sharing, and tiering, as well as multi-path acceleration for CPU-GPU transmission. GLake is easy to use, open for extension, and focuses on improving training throughput, saving inference memory, and accelerating IO transmission. It offers features like memory fragmentation reduction, memory deduplication, and built-in security mechanisms for troubleshooting GPU memory issues.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework consists of MDP formulation, RICO algorithm with rollout and update stages, and reward normalization strategies to stabilize training. RAGEN aims to optimize reasoning and action strategies for LLM agents operating in complex environments.

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.

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.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

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.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework enables LLMs to reason and interact with the environment, optimizing entire trajectories for long-horizon reasoning while maintaining computational efficiency.

This repository implements speculative sampling for large language model (LLM) decoding, utilizing two models - a target model and an approximation model. The approximation model generates token guesses, corrected by the target model, resulting in improved efficiency. It includes implementations of Google's and Deepmind's versions of speculative sampling, supporting models like llama-7B and llama-1B. The tool is designed for fast inference from transformers via speculative decoding.

This GPT generates the best possible LLM-agents for your system prompts. You can also specify the model size, like 3B, 33B, 70B, etc.

Java & Python programming assistant for AI model inference.

I'm Back Propagation, here to help you understand and apply back propagation techniques to your AI models.

Expert in refining custom GPT model instructions.

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Expert in ML model optimization and tuning!

Assists with ROMS and COAWST modeling in Linux environments.

Consultor experto en Business Model Canvas para emprendedores

Guía paso a paso para la creación y mejora de modelos de negocio usando la metodología Business Model Canvas.

An AI GPT model designed to assist with Shell/Bash programming, providing real-time code suggestions, debugging tips, and script optimization for efficient command-line operations.

I help developers understand and use APIs, referencing a YAML model.

专门优化和翻译不同LLM的提示词的专家，基于用户选择和LLM特性。

Neural Network Master V2.0

专注于IP和公私域商业模式，提供流量和变现的一站式解决方案。

CUDA and Triton expert with concise, accurate answers.

A detailed expert trained on all 5,588 pages of Apple CoreData, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

Your friend and helper in the database world!

Expert in SQL queries, and Tutoring.

A rocket scientist aiding amateur rocketeers.

Providing the most comprehensive, cutting-edge, and detailed technical guidance on the latest international CAE simulation technology（HyperMesh、THESEUS-FE、ANSA、STAR-CCM+、Amesim、Ncode、Adams、Abaqus）