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New Release We released Adversarial training for both LM pre-training/finetuning and f-divergence.
The main features of namisan/mt-dnn are: Instruction Tuning, Natural Language Processing.
Projects with overlapping indexed features include: thunlp/ultrachat — UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and… nlpxucan/wizardlm — WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding,… google-research/text-to-text-transfer-transformer — This is a machine learning framework for treating diverse natural language processing tasks as a unified text-to-text… 7compass/sentimental — Simple sentiment analysis with Ruby. abitdodgy/gibran — Gibran is an Elixir natural language processor, and a port of WordsCounted. abadojack/whatlanggo — Natural language detection library for Go.
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
UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros
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
