Best AI tools for< Model Quantizer >

Private LLM is a secure, local, and private AI chatbot designed for iOS and macOS devices. It operates offline, ensuring that user data remains on the device, providing a safe and private experience. The application offers a range of features for text generation and language assistance, utilizing state-of-the-art quantization techniques to deliver high-quality on-device AI experiences without compromising privacy. Users can access a variety of open-source LLM models, integrate AI into Siri and Shortcuts, and benefit from AI language services across macOS apps. Private LLM stands out for its superior model performance and commitment to user privacy, making it a smart and secure tool for creative and productive tasks.

Enhans AI Model Generator is an advanced AI tool designed to help users generate AI models efficiently. It utilizes cutting-edge algorithms and machine learning techniques to streamline the model creation process. With Enhans AI Model Generator, users can easily input their data, select the desired parameters, and obtain a customized AI model tailored to their specific needs. The tool is user-friendly and does not require extensive programming knowledge, making it accessible to a wide range of users, from beginners to experts in the field of AI.

The Frontier Model Forum (FMF) is a collaborative effort among leading AI companies to advance AI safety and responsibility. The FMF brings together technical and operational expertise to identify best practices, conduct research, and support the development of AI applications that meet society's most pressing needs. The FMF's core objectives include advancing AI safety research, identifying best practices, collaborating across sectors, and helping AI meet society's greatest challenges.

Role Model AI is a revolutionary multi-dimensional assistant that combines practicality and innovation. It offers four dynamic interfaces for seamless interaction: phone calls for on-the-go assistance, an interactive agent dashboard for detailed task management, lifelike 3D avatars for immersive communication, and an engaging Fortnite world integration for a gaming-inspired experience. Role Model AI adapts to your lifestyle, blending seamlessly into your personal and professional worlds, providing unparalleled convenience and a unique, versatile solution for managing tasks and interactions.

AI Model Agency is a cutting-edge synthetic photography platform that revolutionizes the world of fashion representation by seamlessly blending technology and creativity. The platform offers innovative AI-generated models, personalized recommendations, and influencer collaboration services to empower brands in enhancing their visual content and boosting e-commerce conversions.

Flux LoRA Model Library is an AI tool that provides a platform for finding and using Flux LoRA models suitable for various projects. Users can browse a catalog of popular Flux LoRA models and learn about FLUX models and LoRA (Low-Rank Adaptation) technology. The platform offers resources for fine-tuning models and ensuring responsible use of generated images.

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.'

HUAWEI Cloud Pangu is an AI tool designed for accelerating drug discovery by optimizing drug molecules. It offers features such as Molecule Search, Molecule Optimizer, and Pocket Molecule Design. Users can submit molecules for optimization and view historical optimization results. The tool is based on the MindSpore framework and has been visited over 300,000 times since August 23, 2021.

Sapling is a language model copilot and API for businesses. It provides real-time suggestions to help sales, support, and success teams more efficiently compose personalized responses. Sapling also offers a variety of features to help businesses improve their customer service, including: * Autocomplete Everywhere: Provides deep learning-powered autocomplete suggestions across all messaging platforms, allowing agents to compose replies more quickly. * Sapling Suggest: Retrieves relevant responses from a team response bank and allows agents to respond more quickly to customer inquiries by simply clicking on suggested responses in real time. * Snippet macros: Allow for quick insertion of common responses. * Grammar and language quality improvements: Sapling catches 60% more language quality issues than other spelling and grammar checkers using a machine learning system trained on millions of English sentences. * Enterprise teams can define custom settings for compliance and content governance. * Distribute knowledge: Ensure team knowledge is shared in a snippet library accessible on all your web applications. * Perform blazing fast search on your knowledge library for compliance, upselling, training, and onboarding.

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.

UbiOps is an AI infrastructure platform that helps teams quickly run their AI & ML workloads as reliable and secure microservices. It offers powerful AI model serving and orchestration with unmatched simplicity, speed, and scale. UbiOps allows users to deploy models and functions in minutes, manage AI workloads from a single control plane, integrate easily with tools like PyTorch and TensorFlow, and ensure security and compliance by design. The platform supports hybrid and multi-cloud workload orchestration, rapid adaptive scaling, and modular applications with unique workflow management system.

Phenaki is a model capable of generating realistic videos from a sequence of textual prompts. It is particularly challenging to generate videos from text due to the computational cost, limited quantities of high-quality text-video data, and variable length of videos. To address these issues, Phenaki introduces a new causal model for learning video representation, which compresses the video to a small representation of discrete tokens. This tokenizer uses causal attention in time, which allows it to work with variable-length videos. To generate video tokens from text, Phenaki uses a bidirectional masked transformer conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to create the actual video. To address data issues, Phenaki demonstrates how joint training on a large corpus of image-text pairs as well as a smaller number of video-text examples can result in generalization beyond what is available in the video datasets. Compared to previous video generation methods, Phenaki can generate arbitrarily long videos conditioned on a sequence of prompts (i.e., time-variable text or a story) in an open domain. To the best of our knowledge, this is the first time a paper studies generating videos from time-variable prompts. In addition, the proposed video encoder-decoder outperforms all per-frame baselines currently used in the literature in terms of spatio-temporal quality and the number of tokens per video.

Artiko.ai is a multi-model AI chat platform that integrates advanced AI models such as ChatGPT, Claude 3, Gemini 1.5, and Mistral AI. It offers a convenient and cost-effective solution for work, business, or study by providing a single chat interface to harness the power of multi-model AI. Users can save time and money while achieving better results through features like text rewriting, data conversation, AI assistants, website chatbot, PDF and document chat, translation, brainstorming, and integration with various tools like Woocommerce, Amazon, Salesforce, and more.

Claude is a large multi-modal model, trained by Google. It is similar to GPT-3, but it is trained on a larger dataset and with more advanced techniques. Claude is capable of generating human-like text, translating languages, answering questions, and writing different kinds of creative content.

SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.

GPT4All is a web-based platform that allows users to access the GPT-4 language model. GPT-4 is a large language model that can be used for a variety of tasks, including text generation, translation, question answering, and code generation. GPT4All makes it easy for users to get started with GPT-4, without having to worry about the technical details of setting up and running the model.

Datagen is a platform that provides synthetic data for computer vision. Synthetic data is artificially generated data that can be used to train machine learning models. Datagen's data is generated using a variety of techniques, including 3D modeling, computer graphics, and machine learning. The company's data is used by a variety of industries, including automotive, security, smart office, fitness, cosmetics, and facial applications.

Text Generator is an AI-powered text generation tool that provides users with accurate, fast, and flexible text generation capabilities. With its advanced large neural networks, Text Generator offers a cost-effective solution for various text-related tasks. The tool's intuitive 'prompt engineering' feature allows users to guide text creation by providing keywords and natural questions, making it adaptable for tasks such as classification and sentiment analysis. Text Generator ensures industry-leading security by never storing personal information on its servers. The tool's continuous training ensures that its AI remains up-to-date with the latest events. Additionally, Text Generator offers a range of features including speech-to-text API, text-to-speech API, and code generation, supporting multiple spoken languages and programming languages. With its one-line migration from OpenAI's text generation hub and a shared embedding for multiple spoken languages, images, and code, Text Generator empowers users with powerful search, fingerprinting, tracking, and classification capabilities.

The AI Lean Canvas Generator is an AI-powered tool designed to help businesses create Lean Canvases quickly and efficiently. It uses artificial intelligence to analyze company descriptions and generate Lean Canvases that summarize key aspects of a business model. The tool aims to streamline the process of creating and validating business models, following the Lean Startup methodology to reduce risk and uncertainty in the early stages of a business. It provides a user-friendly interface for users to input their company information and receive a comprehensive Lean Canvas that includes target market, value proposition, revenue streams, cost structure, and key metrics.

AIModels.fyi is a website that helps users find the best AI model for their startup. The website provides a weekly rundown of the latest AI models and research, and also allows users to search for models by category or keyword. AIModels.fyi is a valuable resource for anyone looking to use AI to solve a problem.

HQQ is a fast and accurate model quantizer that skips the need for calibration data. It's super simple to implement (just a few lines of code for the optimizer). It can crunch through quantizing the Llama2-70B model in only 4 minutes! 🚀

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.

AnyGPT is a unified multimodal language model that utilizes discrete representations for processing various modalities like speech, text, images, and music. It aligns the modalities for intermodal conversions and text processing. AnyInstruct dataset is constructed for generative models. The model proposes a generative training scheme using Next Token Prediction task for training on a Large Language Model (LLM). It aims to compress vast multimodal data on the internet into a single model for emerging capabilities. The tool supports tasks like text-to-image, image captioning, ASR, TTS, text-to-music, and music captioning.

This repo contains a comprehensive paper list of **Model Quantization** for efficient deep learning on AI conferences/journals/arXiv. As a highlight, we categorize the papers in terms of model structures and application scenarios, and label the quantization methods with keywords.

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.

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.

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

SUQL (Structured and Unstructured Query Language) is a tool that augments SQL with free text primitives for building chatbots that can interact with relational data sources containing both structured and unstructured information. It seamlessly integrates retrieval models, large language models (LLMs), and traditional SQL to provide a clean interface for hybrid data access. SUQL supports optimizations to minimize expensive LLM calls, scalability to large databases with PostgreSQL, and general SQL operations like JOINs and GROUP BYs.

TTS Generation WebUI is a comprehensive tool that provides a user-friendly interface for text-to-speech and voice cloning tasks. It integrates various AI models such as Bark, MusicGen, AudioGen, Tortoise, RVC, Vocos, Demucs, SeamlessM4T, and MAGNeT. The tool offers one-click installers, Google Colab demo, videos for guidance, and extra voices for Bark. Users can generate audio outputs, manage models, caches, and system space for AI projects. The project is open-source and emphasizes ethical and responsible use of AI technology.

FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) is a tool designed for uniform quantization and lookup table quantization of weights in lower-precision intervals. It offers flexibility in mapping intervals to arbitrary values through a lookup table. FLUTE supports various quantization formats such as int4, int3, int2, fp4, fp3, fp2, nf4, nf3, nf2, and even custom tables. The tool also introduces new quantization algorithms like Learned Normal Float (NFL) for improved performance and calibration data learning. FLUTE provides benchmarks, model zoo, and integration with frameworks like vLLM and HuggingFace for easy deployment and usage.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

GPTQModel is an easy-to-use LLM quantization and inference toolkit based on the GPTQ algorithm. It provides support for weight-only quantization and offers features such as dynamic per layer/module flexible quantization, sharding support, and auto-heal quantization errors. The toolkit aims to ensure inference compatibility with HF Transformers, vLLM, and SGLang. It offers various model supports, faster quant inference, better quality quants, and security features like hash check of model weights. GPTQModel also focuses on faster quantization, improved quant quality as measured by PPL, and backports bug fixes from AutoGPTQ.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

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.

Llama3.java is a practical Llama 3 inference tool implemented in a single Java file. It serves as the successor of llama2.java and is designed for testing and tuning compiler optimizations and features on the JVM, especially for the Graal compiler. The tool features a GGUF format parser, Llama 3 tokenizer, Grouped-Query Attention inference, support for Q8_0 and Q4_0 quantizations, fast matrix-vector multiplication routines using Java's Vector API, and a simple CLI with 'chat' and 'instruct' modes. Users can download quantized .gguf files from huggingface.co for model usage and can also manually quantize to pure 'Q4_0'. The tool requires Java 21+ and supports running from source or building a JAR file for execution. Performance benchmarks show varying tokens/s rates for different models and implementations on different hardware setups.

Java & Python programming assistant for AI model inference.

Expert in applying mental models across various domains.

Discover A More Robust Business: Craft tailored value proposition statements, develop a comprehensive business model canvas, conduct detailed PESTLE analysis, and gain strategic insights on enhancing business model elements like scalability, cost structure, and market competition strategies. (v1.18)

A software architect for C4 Model generation.

Let's get started by telling me about your business: What do you offer? Who do you serve? ------------------------------------------------------- Need help Prompt Engineering? Reach out on LinkedIn: StephenHnilica

Business Model Canvas Creator - Build and evaluate your business model

Discover if your group, relationship or organization uses specific methods to recruit and maintain control over people

Generates Environmental impact assessment templates based on specific global locations and parameters.

Expert in Marian NMT models and training

Un aiuto a costruire il Business Model Canvas della tua iniziativa

Expert in ML model optimization and tuning!

Business model expert, create detailed reports based on business ideas.

是一个专门用于建立数学模型并撰写python代码的gpt.

Assists with ROMS and COAWST modeling in Linux environments.

Consultor experto en Business Model Canvas para emprendedores

Expert on AI Model NFT Marketplace, offering insights on blockchain tech and NFTs.

Expertly Crafting Your GPT From Concept to Masterpiece

Super Prompter is an AI model designed to create high-quality prompts for chatbots. It thinks like a human in crafting prompts, leveraging various methods like the role method, knowledge level method, and emotion method. This AI model has the capability to generate prompts for any given scenario

Model Vibe Picture Creator: Unleash Your Imagination! 🎨📸 Generates detailed, cool prompts for stylized images, perfect for AI tools like DALL-E 3. 🔥👾