#Instruction Tuning

This article provides a detailed overview of resources related to Large Language Models, featuring organized academic papers, insights from a Chinese language book for beginners, and analysis of trends in research outputs post-ChatGPT launch. It includes evolutionary insights on GPT-series and LLaMA models, alongside practical prompt design resources and experiments on instruction tuning. Contributors to the research can access updates through the provided links, supporting collaborative progress in LLMs.

An objective overview of instruction tuning methodologies, dataset structures, and model training in large language models. This survey evaluates applications across various domains and considers factors impacting outcomes, offering valuable insights into the challenges and future research directions.

Explore ReplitLM's resources, including guides for training, fine-tuning, and using instruction tuning. Learn how to set up hosted demos and integrate with Hugging Face Transformers. Get insights into MosaicML's LLM Foundry for optimized training. Stay updated with the latest releases and configuration tips. These models support Alpaca-style instruction tuning, offering solutions for varied needs. This repository offers evolving tools and practices for enhancing Replit model performance across multiple programming languages.

Cambrian project explores open-source vision-centric multimodal language models with state-of-the-art capabilities in 8B, 13B, and 34B sizes. It provides comprehensive benchmarks and datasets like Cambrian-10M for instruction tuning, allowing easy adoption and performance comparison with proprietary models such as GPT-4V. The project emphasizes two-stage training techniques for model robustness.

Discover an extensive compilation of surveys on Large Language Models, addressing critical areas such as instruction tuning, human alignment, and multi-modal integrations. Understand challenges like hallucination and compression, with insights into their applications in domains like health, finance, and others. A valuable tool for researchers involved in LLM development.

This repository provides a detailed overview of how to improve large language models for code through instruction tuning. It describes components and datasets that enhance models such as OctoCoder and OctoGeeX with a focus on instruction-based fine-tuning. Explore strategic data approaches, including refined datasets like CommitPackFT, and evaluation methods across different programming languages. Training insights for models like OctoCoder and SantaCoder deliver actionable steps for refining model features, allowing for replication, assessment, and extension of existing models to enhance instructional efficacy in coding.

Access an extensive collection of open-source datasets for instruction tuning, suitable for training both text and multi-modal chat-based large language models (LLMs) like GPT-4, ChatGPT, LLaMA, and Alpaca. This repository includes visual-instruction, text-instruction, and RLHF datasets, offering crucial resources for LLM fine-tuning and development. It provides multilingual and multi-task datasets created from both human and machine sources, which facilitate specific task solutions. Leverage these datasets and a comprehensive codebase to advance LLM research and development.