Best AI tools for< Speedup Evaluation >

Career Copilot is an AI-powered hiring tool that helps recruiters and hiring managers find the best candidates for their open positions. The tool uses machine learning to analyze candidate profiles and identify those who are most qualified for the job. Career Copilot also provides a number of features to help recruiters streamline the hiring process, such as candidate screening, interview scheduling, and offer management.

Sereda.ai is an AI-powered platform designed to unleash a team's potential by bringing together all documents and knowledge into one place, conducting employee surveys and satisfaction ratings, facilitating performance reviews, and providing solutions to increase team productivity. The platform offers features such as a knowledge base, employee surveys, performance review tools, interactive learning courses, and an AI assistant for instant answers. Sereda.ai aims to streamline HR processes, improve employee training and evaluation, and enhance overall team productivity.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform collates and analyzes medical research articles, extracting important concepts and identifying causal and associative relationships between them. It organizes this information into a specialized database forming a knowledge graph. Researchers can retrieve causal chains, access specific research articles, and perform tasks like concept analysis, drug repurposing, and target discovery. SOMA enhances literature review by finding relevant articles based on the mechanism of action, saving time on organizing reviews and allowing researchers to focus on their own research. The platform offers freemium access with basic functionality for free indefinitely, with the option to subscribe to advanced features after a 14-day trial period.

CodeParrot is an AI tool designed to speed up frontend development tasks by generating production-ready frontend components from Figma design files using Large Language Models. It helps developers reduce UI development time, improve code quality, and focus on more creative tasks. CodeParrot offers customization options, support for frameworks like React, Vue, and Angular, and integrates seamlessly into various workflows, making it a must-have tool for developers looking to enhance their frontend development process.

TraqCheck is an AI-powered agent for background checks that offers simple, fast, and reliable verifications verified by human experts. It brings inhuman speed and transparency to the verification process, enabling quick hiring decisions at scale. TraqCheck supports various types of checks, including ID, criminal, education, employment history, and more. The platform helps in managing hiring smartly by tracking progress, running analytics, and generating reports on-the-fly. With powerful features like HRMS integration, AI-powered insights, API integration, and more, TraqCheck enhances the hiring process and boosts efficiency and scalability.

Code99 is an AI-powered platform designed to speed up the development process by providing instant boilerplate code generation. It allows users to customize their tech stack, streamline development, and launch projects faster. Ideal for startups, developers, and IT agencies looking to accelerate project timelines and improve productivity. The platform offers features such as authentication, database support, RESTful APIs, data validation, Swagger API documentation, email integration, state management, modern UI, clean code generation, and more. Users can generate production-ready apps in minutes, transform database schema into React or Nest.js apps, and unleash creativity through effortless editing and experimentation. Code99 aims to save time, avoid repetitive tasks, and help users focus on building their business effectively.

DoMore.ai is a personalized AI tools catalog that offers a wide range of AI-powered tools to enhance productivity, creativity, and efficiency. With DoMore.ai, users can access a curated collection of AI tools tailored to their specific needs and preferences. The platform provides detailed descriptions, ratings, and reviews of each tool, making it easy for users to find the right tool for the job. DoMore.ai also offers a personalized recommendation engine that suggests tools based on user preferences and usage patterns. Whether you're a creative professional, a business owner, or a student, DoMore.ai has the tools you need to achieve your goals.

V7 is an AI data engine for computer vision and generative AI. It provides a multimodal automation tool that helps users label data 10x faster, power AI products via API, build AI + human workflows, and reach 99% AI accuracy. V7's platform includes features such as automated annotation, DICOM annotation, dataset management, model management, image annotation, video annotation, document processing, and labeling services.

Osium AI is a cutting-edge AI-powered software designed to accelerate the development of sustainable and high-performance materials and chemicals. The platform leverages proprietary technology developed by experts with 10 years of experience in AI and authors of multiple AI patents. Osium AI offers a comprehensive solution that covers every step of materials and chemicals development cycles, from formulation and characterization to scale-up and manufacturing. The software is flexible, adaptable to various R&D projects, and eliminates trial-and-error approaches, unlocking the full potential of R&D with its advanced functionalities.

AI-SYNT is a digital copy trained on your content. AI-SYNT enables to insert humans, products or characters into generated scenes. Grow your engagement rate up to 4x.

Hermae Solutions offers an AI Assistant for Enterprise Design Systems, providing onboarding acceleration, contractor efficiency, design system adoption support, knowledge distribution, and various AI documentation and Storybook assistants. The platform enables users to train custom AI assistants, embed them into documentation sites, and communicate instantly with the knowledge base. Hermae's process simplifies efficiency improvements by gathering information sources, processing data for AI supplementation, customizing integration, and supporting integration success. The AI assistant helps reduce engineering costs and increase development efficiency across the board.

Promptmate.io is an AI-powered app builder that allows users to create customized applications based on leading AI systems. With Promptmate, users can combine different AI systems, add external data, and automate processes to streamline their workflows. The platform offers a range of features, including pre-built app templates, bulk processing, and data extenders, making it easy for users to build and deploy AI-powered applications without the need for coding.

ONNX Runtime is a production-grade AI engine designed to accelerate machine learning training and inferencing in various technology stacks. It supports multiple languages and platforms, optimizing performance for CPU, GPU, and NPU hardware. ONNX Runtime powers AI in Microsoft products and is widely used in cloud, edge, web, and mobile applications. It also enables large model training and on-device training, offering state-of-the-art models for tasks like image synthesis and text generation.

ioni.ai is an AI application that offers ChatGPT-4 solution for customer support. It is a smart chatbot based on the latest AI technology, designed to handle general inquiries, complex questions, and user-specific requests. The application streamlines workflow with immediate responses, brings CSAT scores to a new level, and ensures human-in-the-loop verification for quality control. With self-learning capabilities, ioni.ai constantly improves its responses and provides accurate solutions to customer inquiries.

Checkr is an employee background screening platform designed for companies to streamline their hiring process by providing services such as criminal background checks, employment verification, driving record checks, drug testing, and more. The platform offers solutions tailored to various industries and company sizes, with a focus on compliance, speed, and accuracy in background checks.

Tolgee is an AI-powered localization tool that offers in-context translation, AI translation, and collaboration features for developers and businesses. It helps users translate their apps to any language efficiently, saving developers' time and ensuring accurate translations. With Tolgee, users can modify texts directly in their apps, extract context, and leverage AI translations to go global. The platform integrates with popular frameworks and technologies, making localization a smooth and hassle-free process.

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SADESIGN RETOUCH PANEL is a smart Photoshop Plugin with more than 600 powerful functions, fully integrated with automatic features such as mass color correction, automatic skinning, acne removal, face slimming, leg lengthening, makeup, and more. It includes valuable resource libraries and eliminates the need for additional software. The tool offers advanced technology for automated photo editing, making it a go-to solution for designers and photographers.

Automateed is an all-in-one AI eBook creator that helps you create unique and professional-quality eBooks in minutes. With Automateed, you can generate unique book content, design beautiful eBook covers, and even get marketing tasks done for you. It's the perfect tool for authors, marketers, and anyone who wants to create high-quality eBooks quickly and easily.

Inkdrop is an AI-powered tool that helps users visualize their cloud infrastructure by automatically generating interactive diagrams of cloud resources and dependencies. It provides a comprehensive overview of infrastructure, aids in understanding complex resource relationships, and seamlessly integrates with CI pipeline for documentation updates.

Official PyTorch implementation of Wanda (Pruning by Weights and Activations), a simple and effective pruning approach for large language models. The pruning approach removes weights on a per-output basis, by the product of weight magnitudes and input activation norms. The repository provides support for various features such as LLaMA-2, ablation study on OBS weight update, zero-shot evaluation, and speedup evaluation. Users can replicate main results from the paper using provided bash commands. The tool aims to enhance the efficiency and performance of language models through structured and unstructured sparsity techniques.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

TriForce is a training-free tool designed to accelerate long sequence generation. It supports long-context Llama models and offers both on-chip and offloading capabilities. Users can achieve a 2.2x speedup on a single A100 GPU. TriForce also provides options for offloading with tensor parallelism or without it, catering to different hardware configurations. The tool includes a baseline for comparison and is optimized for performance on RTX 4090 GPUs. Users can cite the associated paper if they find TriForce useful for their projects.

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.

LayerSkip is an implementation enabling early exit inference and self-speculative decoding. It provides a code base for running models trained using the LayerSkip recipe, offering speedup through self-speculative decoding. The tool integrates with Hugging Face transformers and provides checkpoints for various LLMs. Users can generate tokens, benchmark on datasets, evaluate tasks, and sweep over hyperparameters to optimize inference speed. The tool also includes correctness verification scripts and Docker setup instructions. Additionally, other implementations like gpt-fast and Native HuggingFace are available. Training implementation is a work-in-progress, and contributions are welcome under the CC BY-NC license.

T-MAC is a kernel library that directly supports mixed-precision matrix multiplication without the need for dequantization by utilizing lookup tables. It aims to boost low-bit LLM inference on CPUs by offering support for various low-bit models. T-MAC achieves significant speedup compared to SOTA CPU low-bit framework (llama.cpp) and can even perform well on lower-end devices like Raspberry Pi 5. The tool demonstrates superior performance over existing low-bit GEMM kernels on CPU, reduces power consumption, and provides energy savings. It achieves comparable performance to CUDA GPU on certain tasks while delivering considerable power and energy savings. T-MAC's method involves using lookup tables to support mpGEMM and employs key techniques like precomputing partial sums, shift and accumulate operations, and utilizing tbl/pshuf instructions for fast table lookup.

SeerAttention is a novel trainable sparse attention mechanism that learns intrinsic sparsity patterns directly from LLMs through self-distillation at post-training time. It achieves faster inference while maintaining accuracy for long-context prefilling. The tool offers features such as trainable sparse attention, block-level sparsity, self-distillation, efficient kernel, and easy integration with existing transformer architectures. Users can quickly start using SeerAttention for inference with AttnGate Adapter and training attention gates with self-distillation. The tool provides efficient evaluation methods and encourages contributions from the community.

LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

AirLLM is a tool that optimizes inference memory usage, enabling large language models to run on low-end GPUs without quantization, distillation, or pruning. It supports models like Llama3.1 on 8GB VRAM. The tool offers model compression for up to 3x inference speedup with minimal accuracy loss. Users can specify compression levels, profiling modes, and other configurations when initializing models. AirLLM also supports prefetching and disk space management. It provides examples and notebooks for easy implementation and usage.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Neural Speed is an innovative library designed to support the efficient inference of large language models (LLMs) on Intel platforms through the state-of-the-art (SOTA) low-bit quantization powered by Intel Neural Compressor. The work is inspired by llama.cpp and further optimized for Intel platforms with our innovations in NeurIPS' 2023

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

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.

PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.

QServe is a serving system designed for efficient and accurate Large Language Models (LLM) on GPUs with W4A8KV4 quantization. It achieves higher throughput compared to leading industry solutions, allowing users to achieve A100-level throughput on cheaper L40S GPUs. The system introduces the QoQ quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache, addressing runtime overhead challenges. QServe improves serving throughput for various LLM models by implementing compute-aware weight reordering, register-level parallelism, and fused attention memory-bound techniques.

Marlin is a highly optimized FP16xINT4 matmul kernel designed for large language model (LLM) inference, offering close to ideal speedups up to batchsizes of 16-32 tokens. It is suitable for larger-scale serving, speculative decoding, and advanced multi-inference schemes like CoT-Majority. Marlin achieves optimal performance by utilizing various techniques and optimizations to fully leverage GPU resources, ensuring efficient computation and memory management.

LLM-Drop is an official implementation of the paper 'What Matters in Transformers? Not All Attention is Needed'. The tool investigates redundancy in transformer-based Large Language Models (LLMs) by analyzing the architecture of Blocks, Attention layers, and MLP layers. It reveals that dropping certain Attention layers can enhance computational and memory efficiency without compromising performance. The tool provides a pipeline for Block Drop and Layer Drop based on LLaMA-Factory, and implements quantization using AutoAWQ and AutoGPTQ.

MoBA (Mixture of Block Attention) is an innovative approach for long-context language models, enabling efficient processing of long sequences by dividing the full context into blocks and introducing a parameter-less gating mechanism. It allows seamless transitions between full and sparse attention modes, enhancing efficiency without compromising performance. MoBA has been deployed to support long-context requests and demonstrates significant advancements in efficient attention computation for large language models.

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