LM-Kit.NET
LM-Kit.NET serves as a comprehensive toolkit tailored for the seamless incorporation of generative AI into .NET applications, fully compatible with Windows, Linux, and macOS systems. This versatile platform empowers your C# and VB.NET projects, facilitating the development and management of dynamic AI agents with ease.
Utilize efficient Small Language Models for on-device inference, which effectively lowers computational demands, minimizes latency, and enhances security by processing information locally. Discover the advantages of Retrieval-Augmented Generation (RAG) that improve both accuracy and relevance, while sophisticated AI agents streamline complex tasks and expedite the development process.
With native SDKs that guarantee smooth integration and optimal performance across various platforms, LM-Kit.NET also offers extensive support for custom AI agent creation and multi-agent orchestration. This toolkit simplifies the stages of prototyping, deployment, and scaling, enabling you to create intelligent, rapid, and secure solutions that are relied upon by industry professionals globally, fostering innovation and efficiency in every project.
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Vertex AI
Completely managed machine learning tools facilitate the rapid construction, deployment, and scaling of ML models tailored for various applications.
Vertex AI Workbench seamlessly integrates with BigQuery Dataproc and Spark, enabling users to create and execute ML models directly within BigQuery using standard SQL queries or spreadsheets; alternatively, datasets can be exported from BigQuery to Vertex AI Workbench for model execution. Additionally, Vertex Data Labeling offers a solution for generating precise labels that enhance data collection accuracy.
Furthermore, the Vertex AI Agent Builder allows developers to craft and launch sophisticated generative AI applications suitable for enterprise needs, supporting both no-code and code-based development. This versatility enables users to build AI agents by using natural language prompts or by connecting to frameworks like LangChain and LlamaIndex, thereby broadening the scope of AI application development.
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DeepEval
DeepEval presents an accessible open-source framework specifically engineered for evaluating and testing large language models, akin to Pytest, but focused on the unique requirements of assessing LLM outputs. It employs state-of-the-art research methodologies to quantify a variety of performance indicators, such as G-Eval, hallucination rates, answer relevance, and RAGAS, all while utilizing LLMs along with other NLP models that can run locally on your machine. This tool's adaptability makes it suitable for projects created through approaches like RAG, fine-tuning, LangChain, or LlamaIndex. By adopting DeepEval, users can effectively investigate optimal hyperparameters to refine their RAG workflows, reduce prompt drift, or seamlessly transition from OpenAI services to managing their own Llama2 model on-premises. Moreover, the framework boasts features for generating synthetic datasets through innovative evolutionary techniques and integrates effortlessly with popular frameworks, establishing itself as a vital resource for the effective benchmarking and optimization of LLM systems. Its all-encompassing approach guarantees that developers can fully harness the capabilities of their LLM applications across a diverse array of scenarios, ultimately paving the way for more robust and reliable language model performance.
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Airtrain
Investigate and assess a diverse selection of both open-source and proprietary models at the same time, which enables the substitution of costly APIs with budget-friendly custom AI alternatives. Customize foundational models to suit your unique requirements by incorporating them with your own private datasets. Notably, smaller fine-tuned models can achieve performance levels similar to GPT-4 while being up to 90% cheaper. With Airtrain's LLM-assisted scoring feature, the evaluation of models becomes more efficient as it employs your task descriptions for streamlined assessments. You have the convenience of deploying your custom models through the Airtrain API, whether in a cloud environment or within your protected infrastructure. Evaluate and compare both open-source and proprietary models across your entire dataset by utilizing tailored attributes for a thorough analysis. Airtrain's robust AI evaluators facilitate scoring based on multiple criteria, creating a fully customized evaluation experience. Identify which model generates outputs that meet the JSON schema specifications needed by your agents and applications. Your dataset undergoes a systematic evaluation across different models, using independent metrics such as length, compression, and coverage, ensuring a comprehensive grasp of model performance. This multifaceted approach not only equips users with the necessary insights to make informed choices about their AI models but also enhances their implementation strategies for greater effectiveness. Ultimately, by leveraging these tools, users can significantly optimize their AI deployment processes.
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