microsoft/PhiCookBook

Features

• Small - Provides a comprehensive guide and framework for integrating small language models into applications for reasoning, coding, and math tasks.
• Small Language Models - Provides a framework for integrating and deploying small language models for reasoning, coding, and math tasks.
• AI Development Workflows - Guides the creation of executable sequences of prompts and code to develop intelligent AI applications.
• Prompt Chaining - Implements prompt chaining to link sequential model calls and data transformations for complex reasoning tasks.
• AI Evaluation Frameworks - Implements a framework to automate the assessment of AI outputs and reasoning quality through quality metrics and behavior testing.
• AI Application Debugging - Provides tools and techniques for inspecting and refining the logic and execution paths of AI-driven applications.
• Notebook-Based Experimentation - Utilizes a notebook-based execution model combining documentation and live code for iterative prompt development.
• Model Hosting Abstractions - Provides interfaces to execute lightweight models across diverse hardware environments and serving platforms.
• Workflow Development Guides - Provides tutorials for developing intelligent applications by chaining prompts and code into executable sequences.
• Notebook Tutorials - Organizes technical guidance into executable notebook cells that combine descriptive prose with live code snippets.
• LLM API Workflow Steps - Builds executable sequences by chaining language models, prompts, and code into intelligent AI workflows.
• LLM Evaluation - Measures the accuracy and reliability of language models using custom metrics and automated benchmarks.
• AI Workflow Pipelines - Integrates testing and evaluation processes into automation pipelines to maintain the quality of AI workflows.
• Multi-Platform Hosting Abstractions - Abstracts interaction with different cloud and local hosting environments to execute model logic across varied hardware.
• Automated Dataset Evaluation - Implements automated scripts to run model outputs against benchmark datasets to calculate accuracy and reliability metrics.
• LLM Performance Evaluators - Calculates quality and performance metrics using datasets to verify the accuracy and reliability of AI workflows.
• Model Behavior Evaluation - Enables qualitative and quantitative analysis of model behavior using interactive playgrounds across various platforms.
• Small Model Serving - Provides instructions for publishing executable AI flows to serving platforms and integrating them into application codebases.
• Prompt Playgrounds - Connects model interfaces to the codebase for rapid iterative testing and refinement of prompt logic.
• AI Application Deployment Platforms - Provides instructions for publishing executable AI flows to serving platforms or application codebases.
• AI Implementation Guides - Offers a technical handbook for the practical integration and deployment of small language models.
• Model Interaction Monitors - Tracks execution flow and model interactions to identify errors and iterate on prompt logic.
• Automated Performance Testing - Automates the execution of performance and quality evaluations using scripts integrated into delivery pipelines.

Star history