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huggingface/transformers

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Transformers

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference.

The library features extensive support for model optimization and performance, including techniques like quantization, speculative decoding, and paged memory management for key-value caches. It provides native integration for distributed training across multi-node clusters, as well as flexible APIs for serving models via compatible inference servers. Developers can also utilize built-in utilities for model patching, custom kernel execution, and automated documentation generation to streamline development workflows.

Features

  • API Frameworks - Standardizes the training, fine-tuning, and deployment of models across diverse hardware acceleration backends.
  • Vision Transformers - Processes visual data by partitioning images into sequences of patches compatible with transformer architectures.
  • Hybrid - Coordinates data, pipeline, and tensor parallelism to scale large-scale model training across multi-node clusters.
  • Byte Pair Encodings - Builds vocabularies by iteratively merging frequent character pairs to perform subword tokenization.
  • Chat Template Formatters - Transforms chat histories into the specific token sequences and control structures required by individual models.

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168,632 stars·35,136 forks·Python·Apache-2.0·199 views
  • Qwen2 Language Models - Supports advanced architectural features like group query attention and rotary positional embeddings for specialized model families.
  • Large Model Optimizations - Optimizes memory usage and inference speed through automatic device mapping and half-precision weight support.
  • Checkpoint Resumption - Restores training sessions by reloading optimizer, scheduler, and random number generator states from saved checkpoints.
  • Patterns - Defines standardized patterns for appending tool execution results and function requests to conversation histories.
  • Attention Mechanisms - Exposes a registry-based interface for implementing custom attention mechanisms or modifying existing model behaviors.
  • Batched Inference Mechanisms - Enables efficient inference by processing multiple conversation sequences simultaneously in a single forward pass.
  • Tokenizer Base Interfaces - Maintains a consistent base class for vocabulary management, encoding, and decoding across various tokenization backends.
  • Model Quantization - Reduces memory footprints by storing model weights in lower-precision formats while maintaining performance accuracy.
  • Paged KV Cache Management - Manages key-value cache states using fixed-size blocks to minimize memory fragmentation during inference.
  • Configuration Management - Centralizes hyperparameters and infrastructure settings within a unified class structure for consistent configuration management.
  • Supports - Generates structured function execution requests that allow models to interact directly with host applications.
  • Multimodal Input Handlers - Handles diverse input modalities including audio, video, and images within a unified content processing interface.
  • Training Flow Managers - Automates logging, evaluation, and checkpointing schedules through a flexible callback system during training.
  • Transformers Integration Layers - Extends standard library functionality with specialized loaders for device mapping, quantization, and custom attention backends.
  • Data - Synchronizes model training across multiple GPUs to reduce overall computation time through distributed data strategies.
  • Sequence-to-Sequence Translation Tasks - Facilitates text-to-text translation through integrated model fine-tuning, dataset preprocessing, and streamlined inference pipelines.
  • AI and Agents - A framework that lets you easily use pre-trained transformer models.
  • AI and Machine Learning - State-of-the-art machine learning library for PyTorch and TensorFlow.
  • Computer Vision Frameworks - Library for state-of-the-art machine learning models and architectures.
  • Deep Learning - State-of-the-art models for natural language and multimodal tasks.
  • Hugging Face Ecosystem - Library for downloading and training state-of-the-art pretrained models.
  • Language Model Development - Library for accessing state-of-the-art pretrained NLP models.
  • Language Modeling - Distilled BERT version for faster, lighter inference.
  • Large Language Models - State-of-the-art machine learning library for PyTorch and TensorFlow.
  • Machine Learning - Framework for state-of-the-art machine learning models.
  • Machine Learning Libraries - Framework for state-of-the-art ML models.
  • Memory and Context - Core library for transformer-based sequence modeling and generation.
  • Model Architectures - Library for state-of-the-art natural language processing models.
  • Model Serving and Inference - Framework for defining and using state-of-the-art ML models.
  • Model Training - Access thousands of pretrained models for various modalities.
  • Model Training and Fine-tuning - Pretrained models for text, vision, and audio.
  • Natural Language Processing - State-of-the-art NLP tools for transformer models.
  • Neural Network Frameworks - Large-scale language modeling and transformer-based research.
  • Neural Network Libraries - Ecosystem of pretrained Transformer models for natural language tasks.
  • Pre-trained Language Models - State-of-the-art library for pre-trained NLP models.
  • Reasoning And Planning - Provides foundational support for self-consistency in reasoning tasks.
  • Transformer Implementations - State-of-the-art library for transformer-based natural language processing.
  • Vision Language Models - Cutting-edge multimodal model for image and text understanding.
  • Core Frameworks and Libraries - The de facto standard library for pretrained NLP models.
  • Python NLP Libraries - State-of-the-art library for Transformer-based models.
  • Bert - Listed in the “Bert” section of the Ailia Models awesome list.
  • Named entity recognition - Listed in the “Named entity recognition” section of the Ailia Models awesome list.
  • Neural Natural Language Generation - Listed in the “Neural Natural Language Generation” section of the Awesome Nlg awesome list.
  • Sentiment Analysis - Listed in the “Sentiment analysis” section of the Ailia Models awesome list.
  • Zero shot classification - Listed in the “Zero shot classification” section of the Ailia Models awesome list.
  • Chunked Prefill Mechanisms - Splits long prompt processing across multiple forward passes to prevent blocking other concurrent requests during generation.
  • Document Question Answering Pipelines - Delivers a high-level interface for performing document question answering by routing image and text inputs through specialized inference pipelines.
  • Distributed - Integrates native components to load models directly into distributed training frameworks, utilizing parallelization and optimization techniques.
  • Mixture of Experts - Captures expert routing indices during inference and replays them during training passes to ensure consistent expert paths in mixture-of-experts models.
  • Text Classification - Assigns labels to text sequences for tasks like sentiment analysis or document categorization through pre-trained machine learning models.
  • Generation Continuation Modes - Configures generation to continue from existing chat history rather than initiating a new assistant turn.
  • Edge Model - Exports models into a portable format with ahead-of-time memory planning and hardware-specific operation dispatch for edge device inference.
  • Prompt Lookup Decoding - Proposes candidate tokens by identifying and copying repeating n-grams from input prompts, bypassing the need for an external assistant model.
  • Parallel Loading - Shards tensors during materialization to allow each rank to load only the necessary portion of weight data during parallel training.
  • Asynchronous Batching Execution - Overlaps CPU request preparation with GPU computation using multiple streams and graph-based execution to enhance overall throughput.
  • Memory Efficient Evaluation - Improves evaluation efficiency by offloading accumulated predictions to the CPU and preprocessing logits at the batch level.
  • Byte Level Encodings - Utilizes byte values as a base vocabulary to ensure every input sequence can be tokenized without requiring unknown tokens.

    • Star history

    Star history chart for huggingface/transformersStar history chart for huggingface/transformers

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does huggingface/transformers do?

    Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing…

    What are the main features of huggingface/transformers?

    The main features of huggingface/transformers are: API Frameworks, Vision Transformers, Hybrid, Byte Pair Encodings, Chat Template Formatters, Qwen2 Language Models, Large Model Optimizations, Checkpoint Resumption.

    Which projects share features with huggingface/transformers?

    Projects with overlapping indexed features include: unslothai/unsloth — Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and… sgl-project/sglang — Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It… tensorflow/tensorflow — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of… lightning-ai/pytorch-lightning — PyTorch Lightning is a deep learning research framework that provides a structured environment for organizing machine… jax-ml/jax — This project is a high-performance numerical computing library designed for large-scale scientific and machine… huggingface/peft — This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained…

    Projects sharing features with Transformers

    These projects share indexed features with Transformers. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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      Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

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      tensorflow/tensorflow

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      TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

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    • huggingface/pefthuggingface avatar

      huggingface/peft

      21,877View on GitHub↗

      This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained models by training only a small subset of parameters. It functions as a distributed model training system and optimization toolkit, designed to reduce the computational and memory requirements typically associated with full model fine-tuning. The project distinguishes itself through a suite of methods for modular adapter composition, including low-rank matrix decomposition and activation-based scaling. It supports the integration of multiple task-specific adapter modules, allowin

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