Awesome GitHub RepositoriesCode-Specific Language Models

CodeQwen1.5 is a large language model designed for generating, completing, and analyzing code. It functions as an AI code generator capable of writing programming logic across hundreds of different languages. The model is distinguished by its long-context capabilities, allowing it to process up to one million tokens to reason across entire software repositories. It also operates as a function calling model, utilizing specialized formats to execute complex coding tasks and browser-based automation. The system supports intelligent code completion through fill-in-the-middle capabilities, which

CodeLlama is a family of large language models derived from the Llama 2 architecture and specialized for producing, completing, and refactoring source code across multiple programming languages. It functions as a code generation model capable of synthesizing source code from natural language descriptions. The project includes specific model variants designed for different programming tasks. This includes instruction-tuned models trained to follow complex natural language directions and code infilling models that predict and insert missing code segments into existing files by analyzing surroun

Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software development. It provides specialized model types optimized for general code generation, instruction following, and context-aware infilling. The project includes an instruction-tuned programming model for executing technical tasks via natural language prompts and a code infilling model that predicts missing sections based on surrounding source context. A large context code model is also provided to analyze extensive blocks of source code for improved coherence. The system covers capab

CodeGen est un grand modèle de langage entraîné et un modèle de synthèse de programme conçu pour générer du code source fonctionnel. Il utilise une architecture de réseau neuronal pour synthétiser du code exécutable à partir de descriptions en langage naturel ou d'extraits de code partiels. Le modèle permet la synthèse de programme automatisée et le codage assisté par IA en prédisant et en remplissant les sections manquantes de code au sein d'un programme. Il transforme les descriptions en langage naturel en logique de programmation fonctionnelle pour automatiser la création de code standard et de logique.

Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical reasoning tasks. These models are trained on high-quality programming datasets to interpret natural language prompts and generate functional source code, explain complex logic, repair code defects, and produce technical documentation. The project distinguishes itself through specialized training methodologies that align model behavior with complex programming instructions and mathematical problem-solving. By utilizing chain-of-thought reasoning and instruction-tuned parameter