nlpxucan/WizardLM
WizardLM
WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models.
The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels.
The system covers capabilities in complex instruction execution, mathematical problem solving via chain-of-thought prompting, and the generation of functional source code. It also supports multi-turn conversation management to maintain context across natural dialogues.
Features
- Instruction Evolution - Uses an evolutionary method to automatically rewrite simple instructions into complex tasks to expand training dataset difficulty.
- Synthetic Instruction Evolutions - Implements an evolutionary method to rewrite simple instructions into complex tasks for training high-performance models.
- Chain-of-Thought Prompting - Implements a reasoning process that breaks down complex mathematical and logical problems into intermediate steps.
- Complex Problem Solving - Computes mathematical results and applies reasoning through standard and chain-of-thought prompting.
- Conversational AI - Maintains state and context across multiple dialogue exchanges to perform sophisticated conversational operations.
- Generative Code Models - Functions as a specialized AI model focused on producing functional code from natural language descriptions.
- Synthetic Generators - Provides a framework to transform simple instructions into diverse, difficult synthetic datasets for model training.
- Instruction-Following Models - Executes sophisticated operations across coding and math domains using specialized instruction-evolution training.
- Instruction Tuning - Optimizes pre-trained models through instruction tuning to improve their ability to follow complex user commands.
- Instruction Tuning Frameworks - Provides a framework for enhancing models through evolutionary instruction tuning to handle complex reasoning.
- Large Language Models - Provides a generative large language model capable of executing complex coding, math, and conversational tasks.
- Mathematical Reasoning Training - Employs specialized instructional training and chain-of-thought prompting to solve advanced mathematical problems.
- Natural Language Code Generators - Generates functional and high-quality source code based on natural language descriptions and technical requirements.
- Synthetic Instruction Generators - Automatically evolves simple natural language instructions into sophisticated tasks to create diverse training datasets.
- Domain Specific Models - Provides specialized model adaptations tailored for high-performance coding and mathematical reasoning tasks.
- Synthetic Dataset Generators - Acts as a tool for transforming simple instructions into complex synthetic datasets for training other models.
- Complexity Scaling - Increases training example difficulty through repeated refinement cycles to advance model performance boundaries.
- Instruction Datasets - Transforms basic prompts into high-volume open-domain tasks to improve model instruction-following performance.
- Conversation Management - Tracks information and maintains state across multi-turn interactions to enable natural conversational flow.
- Data Expansion - Uses evolutionary instructions to empower models with complex reasoning.
- Distillation Algorithms - Distills complex instruction-following capabilities using evolved data.
- Foundation Models - Evolutionary instruction-tuned language model.
- Instruction Tuning - Framework for empowering code models through evolutionary instruction tuning.
- LLM Frameworks - Model trained with evolved instructions for complex task following.
- Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
- Reasoning And Math Models - Reinforced instruction tuning for enhanced mathematical reasoning.
