bigcode-project/starcoder

Features

• Code Generators - Provides a large language model specifically designed to generate complete function implementations and predict code characters.
• Generative Code Assistants - Functions as a generative code assistant that completes function implementations across multiple languages.
• Conversational Coding Assistants - Provides a framework for training conversational assistants that generate code via natural language chat.
• Generative Code Models - Implements a generative code model capable of synthesizing source code from natural language prompts.
• Model Adaptation Workflows - Implements workflows for adapting base models to specific coding tasks and instruction-following behaviors using specialized datasets.
• Large Language Model Fine-Tuning - Provides capabilities for adapting large language models to specific coding tasks using specialized datasets.
• LLM Fine-Tuning Toolsets - Ships a specialized toolkit for adapting base models to coding tasks and instruction-following behaviors.
• Parameter Efficient Fine-Tuning - Provides parameter-efficient fine-tuning by inserting trainable adapter layers into a frozen base model.
• Causal Language Modeling - Utilizes a transformer architecture for causal language modeling to predict subsequent tokens in a code sequence.
• Source Code Compilers - Implemented as a large language model specifically trained to generate and complete source code.
• Conversational AI Models - Develops conversational AI models capable of generating code and handling multi-turn natural language dialogues.
• Dialogue-Based Fine-Tuning - Trains language models on multi-turn dialogue corpora to create a conversational code-generating assistant.
• Instruction-Tuned Language Models - Tunes language models to follow instructions and align with human needs using adapter layers.
• Model Performance Evaluators - Provides a standardized evaluation harness to measure the accuracy and quality of generated source code outputs.
• Dialogue Dataset Structuring - Converts raw conversational data into structured templates and schemas to prepare models for chat training.
• Dialogue Adaptation - Implements dialogue adaptation to optimize model responses for multi-turn sequential exchanges.
• Dialogue Prompt Templating - Ships a framework for structuring raw text into standardized prompt templates for conversational training.
• Code Generation Benchmarks - Includes a standardized evaluation harness to measure generated code quality via predefined test cases and benchmarks.
• Code Generation Evaluators - Provides a standardized system for measuring the accuracy and quality of source code produced by models.
• Industry Applications - Large language model optimized for programming tasks.
• Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
• Pre-training Research - Foundational models for multilingual code generation and understanding.

Star history