#instruction tuning

Dolly, a large language model by Databricks with 12 billion parameters and based on EleutherAI's Pythia-12b, is commercially licensed. Fine-tuned on around 15,000 instruction-response pairs, Dolly excels in instruction adherence. Although not a state-of-the-art model, it targets accessibility and AI democratization. Challenges such as managing complex prompts and factual accuracy remain, with ongoing improvements. Available on Hugging Face, Dolly facilitates straightforward inference and training on diverse GPU configurations.

LLaMA-Pro employs progressive block expansion to enhance AI model performance across multiple benchmarks. This open-source project provides training codes and demos, proving effective in code and math tasks. MetaMath-Mistral-Pro surpasses previous models in GSM8k and MATH tests, marking its significance in the field. Its acceptance at ACL 2024 underlines its contribution to AI research and development.

LESS introduces a method for selecting influential data to enhance targeted instruction tuning, improving model performance. The process includes warmup training, creating a gradient datastore, and selecting data specific to tasks. It utilizes datasets like Flan v2, COT, Dolly, and Open Assistant, with evaluation on MMLU, TydiQA, and BBH. Suitable for refining machine learning model efficiency. Explore detailed implementation and evaluation for performance enhancements.

The platform delivers an AI-driven chatbot tailored for video and image interaction, with updates like instruction tuning enhancing performance across benchmarks such as VideoChat2_phi3 and VideoChat2_HD. It supports long video understanding, diverse tasks, and integrates with systems such as ChatGPT, StableLM, and MOSS, highlighting its continuous development in AI and video comprehension. Contribute to this third-party project and explore its extensive applications without any promotional exaggeration.

OpenFedLLM is an open-source resource for training large language models using federated learning. It offers seven federated learning methods, two methods for instruction tuning and value alignment, and more than 30 metrics for assessing capabilities in tasks such as medical and financial QA, code generation, and math solving. This project includes FedLLM-Bench, a benchmarking tool for evaluating federated LLM training. The platform enables the installation, training script access for instruction tuning and value alignment, and comprehensive evaluation tools. OpenFedLLM aids in maintaining decentralized data privacy while pushing forward in the field of language model training.

The project presents a versatile multimodal model that processes and generates various output types, including text, images, videos, and audio. It utilizes pre-trained models and advanced diffusion technology to enhance semantic understanding and multimodal content generation. Recent updates include the release of code and datasets, supporting further research and development. Developers can customize NExT-GPT with flexible datasets and model frameworks. Instruction tuning strengthens its performance across different tasks, making it a solid foundation for AI research.

The project develops a compact large language model for basic chat functionality using the bert4torch framework, focusing on pre-training and instruction fine-tuning. It ensures efficient memory use and integrates smoothly with transformers. While its primary function is simple chat, updates aim to enhance conversational capabilities using extensive datasets and improved training techniques.

Explore an extensive collection of open-source datasets, models, and tools dedicated to instruction tuning in various NLP tasks. This project offers modified datasets from traditional NLP tasks and those generated by large language models (LLMs), providing multilingual resources for global accessibility. It includes translation tools and processes to improve translation quality in low-resource languages. Additionally, it provides a curated list of academic papers and repositories for exploring instruction-based learning, in-context learning, reasoning, and frameworks, making it a valuable resource for researchers and developers in AI enhancement.

This repository offers a vast collection of instruction tuning and learning resources, including papers and datasets, curated by PennState and OhioState experts. Focused on advancing instruction-based learning, it supports the academic community with surveys, corpora, and a collaborative environment, enhancing AI task efficiency. Explore the latest updates and contribute to improving instruction methodologies.

Bonito, an open-source tool, simplifies the creation of task-specific training datasets from unannotated text to support instruction tuning. It leverages Hugging Face transformers and vllm libraries for seamless synthetic dataset generation. Bonito v1 includes features such as zero-shot task adaptation and supports a diverse range of tasks such as question answering, sentiment analysis, and summarization. Comprehensive documentation and tutorials make it a valuable resource for researchers focusing on model training efficiency.

The flan-alpaca project offers improved problem-solving through the fine-tuning of Vicuna-13B on the Flan dataset, as well as demonstrating FLAN-T5's capability in text-to-audio generation. This approach extends Stanford Alpaca's instruction tuning to various models, with all pre-trained models accessible via HuggingFace. It includes practical tools for interactive demo, benchmarking, data preprocessing, training, and efficient integration. This objective overview highlights the project's focus on accessible, high-performance language model tuning.

Mantis enhances multi-image visual language task processing using LLaMA-3, allowing for efficient handling of text and image inputs simultaneously. By achieving top performance on five key benchmarks with minimal resources, Mantis stands out without extensive pre-training data. It ensures robust single-image capabilities similar to CogVLM and Emu2. Recent updates include Idefics-3 training support and evaluation tools via VLMEvalKit. Explore a variety of models and scripts available on Hugging Face.

SEED-LLaMA advances multimodal AI by improving in-context comprehension and generation. Features include advanced visual embeddings that merge text and visuals, and GPU-optimized memory usage. Key updates are SEED-X release and a new SEED-LLaMA demo. Ongoing innovations support extensive multimedia integrations.

GPT4RoI leverages region-focused tuning to enhance image analysis with improved language model integration, highlighting compatibility with LLaMA-7B models and providing practical user tools like a web demo. It is supported by detailed data from RefCOCO, VCR, and more, ensuring comprehensive visual understanding and alignment with advanced AI technologies.

Explore the features of Otter's latest version in multimodal instruction tuning, focusing on OtterHD-8B and MagnifierBench. Otter introduces techniques such as detailed visual interpretation without using a vision encoder and advanced training methods with Flash-Attention-2 for increased efficiency. Evaluate diverse uses with the MIMIC-IT dataset for integrated video and image processing. Otter provides advanced capabilities for complex visual inputs, serving as a valuable resource for AI visual tasks.

SEED-X, a unified foundation model, serves multimodal AI needs with advanced comprehension and generation, from image design to personal assistance. Features include SEED-Story for story creation and SEED-Data-Edit for precise image editing. Experience enhanced models and fast demos.

Investigate the tuning of language models with leading-edge approaches on publicly accessible datasets. This project provides a unified codebase for training and assessing, featuring modern enhancements like LoRA, QLoRA, and efficient parameter updates. Find further insights and advancements through related research publications. The repository contains datasets, evaluation scripts for key benchmarks, and offers models such as Tülu tailored to diverse datasets, facilitating improved language model outcomes. Engage in fine-tuning for instruction adherence, employing advanced practices and reliable evaluation techniques.