Best AI tools for< Optimize Llms >

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.

Tensoic AI is an AI tool designed for custom Large Language Models (LLMs) fine-tuning and inference. It offers ultra-fast fine-tuning and inference capabilities for enterprise-grade LLMs, with a focus on use case-specific tasks. The tool is efficient, cost-effective, and easy to use, enabling users to outperform general-purpose LLMs using synthetic data. Tensoic AI generates small, powerful models that can run on consumer-grade hardware, making it ideal for a wide range of applications.

Freeplay is a tool that helps product teams experiment, test, monitor, and optimize AI features for customers. It provides a single pane of glass for the entire team, lightweight developer SDKs for Python, Node, and Java, and deployment options to meet compliance needs. Freeplay also offers best practices for the entire AI development lifecycle.

Cohere is the leading AI platform for enterprise, offering generative AI, search and discovery, and advanced retrieval solutions. Their models are designed to enhance the global workforce, empowering businesses to thrive in the AI era. With features like Cohere Command, Cohere Embed, and Cohere Rerank, the platform enables the development of scalable and efficient AI-powered applications. Cohere focuses on optimizing enterprise data through language-based models, supporting over 100 languages for enhanced accuracy and efficiency.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

Cohere is the leading AI platform for enterprise, offering products optimized for generative AI, search and discovery, and advanced retrieval. Their models are designed to enhance the global workforce, enabling businesses to thrive in the AI era. Cohere provides Command R+, Cohere Command, Cohere Embed, and Cohere Rerank for building efficient AI-powered applications. The platform also offers deployment options for enterprise-grade AI on any cloud or on-premises, along with developer resources like Playground, LLM University, and Developer Docs.

Arcee AI is a platform that offers a cost-effective, secure, end-to-end solution for building and deploying Small Language Models (SLMs). It allows users to merge and train custom language models by leveraging open source models and their own data. The platform is known for its Model Merging technique, which combines the power of pre-trained Large Language Models (LLMs) with user-specific data to create high-performing models across various industries.

Lunary is an AI developer platform designed to bring AI applications to production. It offers a comprehensive set of tools to manage, improve, and protect LLM apps. With features like Logs, Metrics, Prompts, Evaluations, and Threads, Lunary empowers users to monitor and optimize their AI agents effectively. The platform supports tasks such as tracing errors, labeling data for fine-tuning, optimizing costs, running benchmarks, and testing open-source models. Lunary also facilitates collaboration with non-technical teammates through features like A/B testing, versioning, and clean source-code management.

deepset is an AI platform that offers enterprise-level products and solutions for AI teams. It provides deepset Cloud, a platform built with Haystack, enabling fast and accurate prototyping, building, and launching of advanced AI applications. The platform streamlines the AI application development lifecycle, offering processes, tools, and expertise to move from prototype to production efficiently. With deepset Cloud, users can optimize solution accuracy, performance, and cost, and deploy AI applications at any scale with one click. The platform also allows users to explore new models and configurations without limits, extending their team with access to world-class AI engineers for guidance and support.

Arthur is an industry-leading MLOps platform that simplifies deployment, monitoring, and management of traditional and generative AI models. It ensures scalability, security, compliance, and efficient enterprise use. Arthur's turnkey solutions enable companies to integrate the latest generative AI technologies into their operations, making informed, data-driven decisions. The platform offers open-source evaluation products, model-agnostic monitoring, deployment with leading data science tools, and model risk management capabilities. It emphasizes collaboration, security, and compliance with industry standards.

Gradient is an AI automation platform designed specifically for enterprise AI purposes. It offers a seamless way to automate manual workflows with minimal effort, providing business intuition and industry expertise. The platform ensures unmatched compliance with various regulations and prioritizes privacy and security. Gradient's Agent Foundry enables users to automate tasks, integrate data, and optimize workflows efficiently, making it a valuable tool for modern enterprises.

OdiaGenAI is a collaborative initiative focused on conducting research on Generative AI and Large Language Models (LLM) for the Odia Language. The project aims to leverage AI technology to develop Generative AI and LLM-based solutions for the overall development of Odisha and the Odia language through collaboration among Odia technologists. The initiative offers pre-trained models, codes, and datasets for non-commercial and research purposes, with a focus on building language models for Indic languages like Odia and Bengali.

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Unify is an AI tool that offers a unified platform for accessing and comparing various Language Models (LLMs) from different providers. It allows users to combine models for faster, cheaper, and better responses, optimizing for quality, speed, and cost-efficiency. Unify simplifies the complex task of selecting the best LLM by providing transparent benchmarks, personalized routing, and performance optimization tools.

Composio is an integration platform for AI Agents and LLMs that allows users to access over 150 tools with just one line of code. It offers seamless integrations, managed authentication, a repository of tools, and powerful RPA tools to streamline and optimize the connection and interaction between AI Agents/LLMs and various APIs/services. Composio simplifies JSON structures, improves variable names, and enhances error handling to increase reliability by 30%. The platform is SOC Type II compliant, ensuring maximum security of user data.

GPTBots.ai is a powerful no-code platform for creating AI-driven business applications. It seamlessly integrates large language models with organizational data, services, and workflows to empower AI bots in driving business growth. The platform allows users to build and train AI bots without coding experience, access best-practice AI bot templates, optimize and customize AI knowledge base, and adapt to various scenarios with intelligent agent bots. GPTBots supports diverse input types, offers versatile language models, enables seamless chatbot-human handoff, and provides robust API and SDK for embedding capabilities into products. Trusted by over 100k companies worldwide, GPTBots helps enterprises enhance customer service, leads generation, SEO writing, data analysis, and more.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

Dhenu Agri LLMs - KissanAI is an AI-powered application designed to assist farmers in optimizing their agricultural practices. The platform leverages artificial intelligence to provide farmers with valuable insights and recommendations for improving crop yield and overall farm productivity. By analyzing data such as weather patterns, soil quality, and crop health, KissanAI helps farmers make informed decisions to enhance their agricultural output. With user-friendly interfaces and intuitive features, this tool aims to empower farmers with cutting-edge technology to drive sustainable farming practices.

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

TensorRT-LLM is an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM contains components to create Python and C++ runtimes that execute those TensorRT engines. It also includes a backend for integration with the NVIDIA Triton Inference Server; a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using Tensor Parallelism and/or Pipeline Parallelism).

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

veScale is a PyTorch Native LLM Training Framework. It provides a set of tools and components to facilitate the training of large language models (LLMs) using PyTorch. veScale includes features such as 4D parallelism, fast checkpointing, and a CUDA event monitor. It is designed to be scalable and efficient, and it can be used to train LLMs on a variety of hardware platforms.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

Safeguards Shield is a security and alignment toolkit designed to detect unwanted inputs and LLM outputs. It provides tools to optimize RAG pipelines for accuracy and ensure trustworthy AI needs are met. The SDK aims to make LLMs accurate and secure, unlocking value faster by unifying a set of tools.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

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.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project facilitates sharing and exchanging technologies related to large model semantic cache through open-source collaboration.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

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。

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

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