MPT-7B Verified Customer Reviews (2026)

Gemini platform is an enterprise artificial intelligence development and machine learning platform from Google Cloud that provides the tools and infrastructure required to build, customize, deploy, and govern AI models and autonomous agents. The platform represents the evolution of Vertex AI and combines generative AI development, predictive machine learning, agent orchestration, model training, and enterprise governance within a unified environment. Its Model Garden provides access to more than 200 foundation models, including Google's Gemini family, third-party language models, and open-source alternatives that can be evaluated, customized, and deployed for different applications. Agent Studio provides a low-code workspace for designing AI agents, experimenting with prompts, testing model behavior, and prototyping intelligent applications through visual development tools. The Agent Development Kit offers a model-agnostic framework for building sophisticated agents capable of reasoning, using external tools, coordinating specialized subagents, and executing multistep workflows. Google Antigravity extends the platform's agentic capabilities with a development environment for managing AI coding agents, generating application code, and coordinating automated software engineering tasks. Gemini platform also provides custom machine learning training with support for different frameworks, configurable training jobs, hyperparameter optimization, and deployment of trained models for online or batch predictions. Its MLOps capabilities include automated pipelines, model evaluation, Model Registry, Feature Store, and monitoring tools that help engineering teams manage the machine learning lifecycle and identify performance degradation.

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.

Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.