Refactored the architecture of Co-op Translator to support multiple language and vision model providers, including OpenAI and Azure OpenAI. Introduced abstract base classes, modularized provider-specific configurations, and reorganized utilities for enhanced maintainability. The redesign ensures better separation of concerns and facilitates the integration of new LLM and vision services like Anthropic or Google Cloud Vision. Functional testing confirmed core functionalities remain intact.

Enhanced the disclaimer to inform users that translations are performed by generative AI, ensuring transparency about the source and limitations. Resolved issues with skipped chunks and incomplete document translations by implementing a sequential processing mechanism for markdown files. Introduced a `process_api_requests_sequential` method to ensure reliable and consistent translation by processing markdown files one at a time. Updated the `translate_all_markdown_files` method to utilize this sequential processing approach.

Refactored the project structure to enhance maintainability and facilitate diverse testing scenarios. Introduced separate modules for configurations, image processing, text translation, and utilities. Centralized configuration management using a `Config` class and segregated development and production settings. Added initial testing infrastructure with unit tests for each module and a template for integration tests. Improved documentation by adding detailed docstrings and updating the README.

Step-by-step guide for fine-tuning Phi-3 models with Prompt Flow integration

Comprehensive guide for deploying and managing Phi-3 models in Azure AI Studio

Detailed guide on model evaluation with Microsoft's Responsible AI principles

Implemented GitHub Actions workflow for automated link checking

Added support for 6 languages using Co-op Translator

Resolved Flash Attention issue

Resolved missing QLoRA parameters in the notebook