microsoft/PhiCookBook
PhiCookBook
PhiCookBook is a technical guide and implementation framework for integrating small language models into applications. It provides instructions for deploying these lightweight models to perform reasoning, coding, and math tasks across various hardware environments and serving platforms.
The project functions as a tutorial for developing intelligent AI applications by chaining prompts and code into executable sequences. It includes a framework for evaluating model behavior and calculating quality metrics to verify the accuracy and reliability of these workflows.
The repository covers a broad range of AI development capabilities, including workflow development, application debugging, and performance evaluation. It also provides methods for automating pipeline testing and integrating evaluation processes into continuous delivery workflows.
The content is organized as a series of notebooks that combine descriptive prose with live code cells for immediate testing and iteration.
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.
