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An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
An open-source AI agent that brings the power of Gemini directly into your terminal.
Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
A Python SDK for Vertex AI, a fully managed, end-to-end platform for data science and machine learning.
Models and examples built with TensorFlow
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
Evaluation and Tracking for LLM Experiments and AI Agents
An open protocol enabling communication and interoperability between opaque agentic applications.
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Examples and guides for using the OpenAI API
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
Google Research
A framework for few-shot evaluation of language models.
Task-based datasets, preprocessing, and evaluation for sequence models.
Stable Diffusion web UI
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.
Model analysis tools for TensorFlow
Arena-Hard-Auto: An automatic LLM benchmark.
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/