Projects sharing features with Magicoder

labelImg is a computer vision labeling tool and image bounding box annotator used to create training datasets for machine learning models. It functions as a desktop utility for drawing rectangular labels on images and saving object coordinates and class names in common machine learning formats. The tool is specifically designed to generate and edit PascalVOC formatted XML files and create image labels in the text-based format required by YOLO object detection pipelines. The software covers object detection annotation and training data preparation, including the ability to manage label catego

Metaseq is a transformer sequence modeling toolkit designed for training, fine-tuning, and deploying sequence-to-sequence models using open pre-trained weights. It provides a comprehensive framework for large language model training, including dedicated tools for sequence dataset processing and a standalone inference server for generating text via API requests. The project features specialized utilities for model quantization to reduce parameter precision to eight bits, which lowers memory usage and increases inference speed. It also includes a checkpoint conversion pipeline to transform mode

Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License

This is a machine learning framework for treating diverse natural language processing tasks as a unified text-to-text problem. It provides a toolkit for pre-training and fine-tuning large-scale transformer models, utilizing a system where both inputs and outputs are formatted as raw text sequences. The framework is distinguished by its distributed training system, which uses mesh-based strategies to scale model weights and training batches across multiple TPU cores. It supports multi-task learning by combining diverse datasets into a single training stream using configurable mixture rates, al

🤗 HF Repo 📄 Paper 📚 6K Data 📚 10K Data

OpenChat is a framework for the training, fine-tuning, and deployment of large language models optimized for conversational and mathematical reasoning tasks. It provides a comprehensive lifecycle for these models, ranging from training pipelines and deployment stacks to a web-based chat interface. The project focuses on enabling high-performance model execution on consumer-grade hardware without the need for enterprise-grade accelerators. It includes a production-ready inference server that implements the OpenAI chat completion protocol and utilizes dynamic request batching to optimize hardwa

This repository contains the code for our paper “ZeroGen: Efficient Zero-shot Learning via Dataset Generation”. Our implementation is built on the source code from dino. Thanks for their work.

Towards Robust Grounded Language Modeling [DEMO](https://huggingface.co/spaces/luohy/SAIL-7B) | [WEB](https://openlsr.org/sail-7b)

New Release We released Adversarial training for both LM pre-training/finetuning and f-divergence.

Official Code Repository for the paper Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-intensive Tasks (NeurIPS 2023).

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

This repo introduces ExpertLLaMA, a solution to produce high-quality, elaborate, expert-like responses by augmenting vanilla instructions with specialized Expert Identity description. This repo contains: - Brief introduction on the method. - 52k Instruction-Following Expert Data generated by…

This repository contains the Unnatural Instructions dataset. Unnatural Instructions is a dataset of instructions automatically generated by a Large Language model. See full details in the paper: "Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor"

This repository contains the source code for reproducing the data curation of MUFFIN (Multi-faceted Instructions).

This repository contains the code and models of the paper "AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation"

This is the official code for the paper Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation) (accepted to EMNLP 2023).

This repository contains the code for our paper “SunGen: Self-Guided High-Quality Data Generation in Efficient Zero-Shot Learning”.

Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements (ACL'24) Chinese Version: [知乎](https://zhuanlan.zhihu.com/p/720237237)

UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon (*Equal Contribution)

Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts)

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

Rethinking Negative Instances for Generative Named Entity Recognition

Inquisitive Parrots for Search A toolkit for end-to-end synthetic data generation using LLMs for IR

Proposed by: - Chia-Yi Su - Collin McMillan

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

This repository provides an overview of all components from the paper OctoPack: Instruction Tuning Code Large Language Models. Link to 5-min video on the paper presented by Niklas Muennighoff.