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🎩 Models | 📚 Dataset | 🚀 Quick Start | 👀 Demo | 📝 Citation | 🙏 Acknowledgements
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 capa
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
Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to improve their instruction-following capabilities. It provides a pipeline for aligning pretrained models with human intentions through a supervised fine-tuning workflow. The system utilizes a synthetic data generator that uses a seed set of tasks to prompt a model to create new instructional data. It includes an instruction dataset curator to remove redundant or low-quality entries, maintaining dataset diversity through a filtered task pool. The framework covers the full alignme
This is the repo for the Code Alpaca project, which aims to build and share an instruction-following LLaMA model for code generation. This repo is fully based on Stanford Alpaca ,and only changes the data used for training. Training approach is the same.
The main features of sahil280114/codealpaca are: Code Generation and Distillation, Data Expansion, Instruction Tuning.
Projects with overlapping indexed features include: yizhongw/self-instruct — Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to… facebookresearch/codellama — Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software… ise-uiuc/magicoder — 🎩 Models | 📚 Dataset | 🚀 Quick Start | 👀 Demo | 📝 Citation | 🙏 … nlpxucan/wizardlm — WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding,… bigscience-workshop/promptsource — Toolkit for creating, sharing and using natural language prompts. codefuse-ai/mftcoder — High Accuracy and efficiency multi-task fine-tuning framework for Code LLMs. This work has been accepted by KDD 2024.