microsoft/unilm

Features

• Intelligent Document Processing - Provides a comprehensive framework for extracting information from visually-rich documents by integrating text, layout, and image analysis.
• Language Model Fine-Tuning - Supports large language model fine-tuning to adapt pre-trained models to specific domains and downstream tasks.
• Large Language Models - Offers a complete toolkit for pretraining, instruction tuning, and optimizing transformer-based models for diverse natural language tasks.
• Language Model Training - Provides distributed pre-training pipelines for building large-scale language models from scratch.
• Multimodal AI Systems - Provides a comprehensive framework for multimodal AI development, integrating text, vision, audio, and document layout data.
• Structured Document Extraction - Extracts information from structured documents like forms and receipts by analyzing both textual content and visual layout features.
• Document Question Answering Pipelines - Analyzes the structure and content of web pages to provide accurate answers to natural language queries about the document.
• Unified Frameworks - Provides a research platform for training and fine-tuning unified transformer models across text, vision, audio, and document modalities.
• Training Efficiency - Provides efficient model training workflows through distributed training, memory optimization, and hardware-aware kernels.
• Modular Backbone Architectures - Provides a unified transformer backbone that processes text, vision, and audio inputs through a shared set of weights.
• Multimodal Models - Enables the development of foundational models that align and process cross-modal data including speech, images, and text.
• Document Layout Analysis - Identifies and segments structural elements within document images such as text blocks, figures, and tables.
• Speech Synthesis - Generates natural-sounding human speech from short text prompts using neural codec language models.
• Information Extraction - Processes text and markup from visually-rich documents to identify and pull specific data points.
• Long Context Training Optimizations - Implements dilated attention mechanisms to handle context windows of up to one billion tokens efficiently.
• Model-Driven Text Extraction - Identifies text within images and assigns precise spatial coordinates to enable document-level text recognition.
• Multimodal Layout Analysis - Integrates text, spatial layout, and visual image data into a unified model to extract information and understand visually-rich documents.
• Vision Model Fine-Tuning - Adapts pre-trained transformer models to specific downstream vision tasks like image classification and semantic segmentation.
• Inference Optimization - Reduces memory usage and improves computational efficiency during sequence generation using gated retention mechanisms.
• Language Model Fine-Tuning - Adapts pre-trained document understanding models to specific downstream tasks like question answering or form extraction using task-specific datasets.
• Mixture of Experts - Scales deep learning models using specialized architectures like mixture-of-experts and retentive networks.
• Unified Understanding and Generation Training - Trains neural networks on combined datasets to perform both natural language understanding and text generation within a single architecture.
• Speech Processing - Supports speech and audio processing for automatic speech recognition, voice synthesis, and acoustic representation learning.
• Memory Optimization Techniques - Reduces GPU memory consumption during training using distributed strategies and activation checkpointing.
• Model Optimization Suites - Implements a suite of techniques for accelerating inference and reducing memory usage in large-scale deep learning architectures.
• Model Compression Suites - Reduces model size and computational requirements through self-attention distillation.
• Attention Kernel Fusion - Executes differential attention operations efficiently using hardware-aware kernels to accelerate training and inference.
• Weight Distribution - Provides distributed weight partitioning strategies to handle memory-intensive computations across multiple processors during large-scale model development.
• Multimodal Large Language Models - Integrates visual and textual data into a unified model to enable multimodal understanding and generation tasks across different input modalities.
• Multimodal Training - Trains unified models capable of processing and generating across text, vision, speech, and document modalities.
• Multilingual Text Recognition - Processes visual input using transformer architectures to generate text output for handwritten and printed documents.
• Synthetic Data Generation - Provides synthetic data generation pipelines to create pseudo-test inputs for evaluating and filtering training data.
• Training Data Generation - Supports synthetic training data generation to create large-scale instruction-tuning datasets for model improvement.
• Retentive State Mechanisms - Implements retentive state processing to enable efficient sequence generation and handle long-context data during inference.
• Image Segmentation - Adapts pre-trained vision models to perform pixel-level semantic segmentation for identifying and labeling distinct objects within an image.
• Dataset Preparation Tools - Provides comprehensive training dataset curation tools for organizing general knowledge, code, and mathematical datasets.
• Distributed Training - Distributes large model training across multiple processors by partitioning model weights to handle memory-intensive computations efficiently.
• Image Classification - Fine-tunes pre-trained vision models to categorize images into predefined classes with high accuracy.
• Document Image Model Pre-training - Learns visual representations from large-scale unlabeled document images using self-supervised techniques.
• Vision Transformer Pre-training - Trains image transformer models using masked image modeling to learn visual representations from large-scale datasets.
• Speech Model Fine-Tuning - Adapts pre-trained audio representations to specific downstream tasks like speaker verification and speech separation.
• Model Performance Benchmarking - Runs standardized benchmarks on mathematical reasoning datasets to measure model accuracy and output quality.
• Model Training Pipelines - Executes instruction-tuning pipelines on large-scale grounded image-text datasets for unified vision-language systems.
• Model Fine-Tuning and Adaptation - Offers comprehensive tools for refining pre-trained models across various domains and task requirements.
• Synthetic Speech Generation - Converts text input into natural-sounding audio using pre-trained models and vocoders.
• Self-Supervised Speech Representations - Trains large-scale self-supervised models on extensive audio datasets to generate robust representations for speech processing.
• Model Deployment Toolkits - Provides toolkits for efficient sequence-to-sequence decoding and model compression for production environments.
• Multimodal Token Interleaving - Implements multimodal data tokenization to align audio, text, and visual inputs into unified sequences for model training.
• Self-Supervised Embedding Trainers - Trains vision models using self-supervised learning to create reusable feature representations.
• Multimodal Document Pre-training - Learns joint representations of text, spatial layout, and visual image features for document understanding tasks.
• Preference Optimization - Refines model outputs using direct preference optimization by comparing responses against feedback.
• Sequence Decoders - Predicts multiple tokens simultaneously during sequence generation to reduce decoding steps.
• Subword Tokenization - Implements subword text tokenization to convert raw text into numerical sequences for transformer architectures.
• Vision-Language Grounding Models - Links text spans such as noun phrases and referring expressions to specific image regions to enable phrase grounding and comprehension.
• Attention Optimization - Implements decoder-decoder architectures to optimize cache usage.
• Foundation Models - Grounding multimodal models to real-world entities.
• Generalization And Learning - Neural codec models for zero-shot in-context learning.
• Large Language Models - Self-supervised pre-training across tasks and modalities.
• Model Architectures - Decoder-decoder architecture for efficient caching.
• Model Quantization Tools - Native 4-bit activations for 1-bit LLM architectures.
• Multimodal Foundation Models - General-purpose foundation model for vision and language tasks.
• Multimodal Models - Framework for transformer-based optical character recognition.
• Sequence To Sequence Models - Unified framework for various sequence-to-sequence generation tasks.
• Vision Language Models - Transformer-based models for aligning perception with linguistic capabilities.
• Vision Models - Document image transformer for self-supervised pre-training.
• Document Processing - LayoutLM-v3 model for document understanding.
• Reading Order Predictors - Analyzes text and spatial layout information within document images to determine the logical sequence in which text lines should be read.
• AI-Generated Captions - Produces descriptive text summaries for images by interpreting visual content and generating corresponding natural language captions.
• Visual Tokenizers - Implements visual data tokenization to convert raw images into discrete tokens using encoder-decoder architectures.
• Sparse Caching Strategies - Implements sparse key-value caching to maintain accuracy while accelerating the processing of long text sequences.
• Attention Mechanisms - Calculates attention scores using a differential mechanism that subtracts two separate attention maps to improve model focus and performance.
• Audio Tokenization - Learns acoustic representations from raw audio data using iterative tokenization for downstream classification tasks.
• Data Preparation - Provides pipelines for multilingual training data preparation, converting raw text and parallel pairs into memory-mapped binary formats.
• Multilingual Extractors - Extends document understanding capabilities to multiple languages by training on cross-lingual datasets to extract key-value pairs from international document formats.
• Reading Order Benchmarks - Provides large-scale datasets of document images paired with ground-truth reading order information to evaluate and train document analysis models.
• Text-to-Image Generators - Creates images containing coherent text by using text prompts and layout guidance.
• Visual Text Renderers - Generates visual output from text inputs using pre-trained models or fine-tuned adapters to render specific text styles and layouts.
• Knowledge Distillation - Transfers knowledge from teacher models to student retrievers to improve performance and efficiency.
• Mathematical Reasoning Training - Trains models on large-scale synthetic instruction datasets to enhance mathematical problem-solving capabilities.
• Long Context Retrieval Testing - Assesses model recall capabilities within long sequences using needle-in-a-haystack and multi-needle retrieval experiments.
• Custom Vision Training - Supports modifying pre-trained vision and language weights to master downstream tasks like visual question answering.
• Biencoder Pipelines - Executes a multi-stage supervised fine-tuning pipeline to develop high-performance biencoder models for information retrieval tasks.
• Model Fine-Tuning - Adapts pre-trained models to specific document understanding objectives like semantic entity recognition and relation extraction using labeled datasets.
• Cross-Lingual Objectives - Trains language models using masked language modeling, translation language modeling, and contrastive learning objectives to improve cross-lingual representation.
• Retrieval Model Pre-training - Compresses input information into a representation bottleneck to create specialized models for dense passage retrieval.
• Ternary Weight Optimizations - Optimizes large language model architectures by using ternary weights to reduce memory footprint and computational requirements.
• Sequence-to-Sequence Tasks - Trains parameter-efficient transformer models to perform tasks like grammatical error correction and abstractive summarization on resource-constrained devices.
• Speech Translation Systems - Translates spoken language by processing audio input and generating text output through sequence-to-sequence models.
• Multilingual Text Processing - Facilitates multilingual text processing by tokenizing and converting raw text into binary formats for large-scale training.
• Multimodal Prompt Adapters - Customizes pre-trained multimodal models for text-intensive image understanding tasks by applying supervised training with task-specific prompts.
• Speech-to-Text Engines - Converts model outputs into text using language models and lexicons to improve transcription accuracy.
• Cross-Lingual Translation Training - Provides cross-lingual transformer encoders to improve the accuracy and scalability of automated translation workflows.
• Referring Expression Generators - Produces descriptive text for specific image regions based on provided visual context using zero-shot or few-shot learning techniques.
• Markdown Converters - Transforms visual document layouts into structured markdown format by capturing both the text content and its original styling.
• Document Classification - Categorizes documents based on their visual structure and content to automate sorting and organization workflows.
• Audio Processing - Applies fine-tuned acoustic models to categorize audio inputs into specific classes based on learned patterns.
• Cross-Modal Representations - Applies iterative word alignment and contrastive loss functions during training to synchronize semantic representations across different languages.
• Custom Diffusion Model Training - Trains two-stage diffusion models on large-scale image-text datasets annotated with character-level segmentation masks and optical character recognition data.
• Vocabulary Builders - Supports incremental vocabulary generation to expand token sets for domain-specific terminology.
• Embedding Generators - Transforms text inputs into high-dimensional vector representations using pre-trained language models to support semantic search and information retrieval tasks.
• Layout Planners - Optimizes models to predict spatial arrangements for text elements within generated images to ensure coherent composition.
• Pre-made Models - Leverages pre-trained model weights to accelerate development of systems for complex document layout analysis.
• Backbone Model Integration - Utilizes established transformer architectures as backbones to initialize and accelerate training of document understanding systems.
• Result Reranking - Optimizes re-ranking models to refine retrieval results by evaluating the relevance between queries and passages more precisely.
• Voice Cloning - Modifies speaker identity or characteristics of audio input while preserving linguistic content.
• Data Input Interfaces - Provides automated input data processing to detect and handle raw text or pre-tokenized dataset structures.
• Audio Feature Extraction - Processes audio input through pre-trained models to generate numerical representations for downstream speech analysis.
• Training Cycles - Executes training cycles and performance testing on document datasets to optimize model accuracy for specialized document understanding tasks.

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