facebookresearch/codellamaArchived
Codellama
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 capabilities for automated code completion, predictive snippet generation, and large scale code analysis. It supports the execution of specific coding tasks and the answering of technical questions through a natural language interface.
Features
- AI-Powered Code Generation - Automatically generates functions, classes, and full scripts based on natural language descriptions.
- Contextual Code Infilling - Provides a specialized model capable of predicting and inserting missing code sections based on surrounding source context.
- Predictive Code Completions - Predicts the natural continuation of code snippets based on prefixes to automate repetitive typing and logic.
- Code-Specific Language Models - Provides a large language model based on Llama 2 trained specifically for programming and software development.
- Code Infilling Models - Provides a specialized model type for inserting missing code based on surrounding source context.
- Fill-in-the-Middle Training Objectives - Uses fill-in-the-middle training to enable the model to predict missing tokens from bidirectional context.
- Instruction Fine-tuning - Refines the base model using curated programming task datasets to improve zero-shot instruction following.
- Instruction-Following Models - Answers complex technical questions and performs refactoring tasks via a natural language interface.
- Instruction-Tuned Language Models - Offers a version of the model optimized for following natural language prompts for technical tasks.
- Natural Language Code Generators - Translates natural-language problem descriptions and technical instructions into executable source code.
- Intelligent Code Infilling - Fills gaps or missing logic within existing codebases by analyzing the surrounding file context.
- Large Context Code Models - Provides a model designed to analyze extensive blocks of source code for improved coherence.
- Llama Architectures - Utilizes a transformer-based decoder with rotary positional embeddings and group-query attention.
- Position Embedding Scaling - Extends the maximum token processing limit by adjusting and scaling positional embeddings.
- Causal Language Modeling - Implements a causal decoder architecture that predicts the next token by masking future context.
- Large-Scale Codebase Analysis - Processes extensive codebases to maintain coherence when generating or modifying software logic.
- Code Generation and Distillation - Provides open foundation models for code generation.
- Code Generation Models - Foundation models specifically fine-tuned for code generation and debugging.
- Data Expansion - Foundation models for code generation and instruction following.
- Foundation Models - State-of-the-art model optimized for code generation.
- Large Language Models - Official Meta repository for code-specialized Llama models.
- Llama Model Ecosystem - Official repository for Llama-based code generation and programming models.
