bigcode-project/starcoder
Starcoder
Starcoder is a large language model and associated framework designed to generate, complete, and evaluate source code across multiple programming languages. It functions as a source code model that can produce complete function implementations and predict subsequent characters in a line of code based on provided prompts.
The project provides a specialized toolkit for adapting base models to specific coding tasks and instruction-following behaviors. This includes a conversational code assistant framework for training models to generate code via natural language chat, as well as a parameter-efficient fine-tuning framework that uses adapter layers to minimize computational costs.
The system covers a broad range of capabilities including causal language modeling, multi-turn dialogue training, and data engineering for dialogue dataset formatting. It also includes a standardized evaluation harness to measure the accuracy and quality of generated code outputs through predefined test cases and benchmarks.
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.
