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- sections:
- local: index
title: 🤗 Transformers 简介
- local: quicktour
title: 快速上手
- local: installation
title: 安装
title: 开始使用
- sections:
- local: pipeline_tutorial
title: 使用pipelines进行推理
- local: autoclass_tutorial
title: 使用AutoClass编写可移植的代码
- local: preprocessing
title: 预处理数据
- local: training
title: 微调预训练模型
- local: run_scripts
title: 通过脚本训练模型
- local: accelerate
title: 使用🤗Accelerate进行分布式训练
- local: peft
title: 使用🤗 PEFT加载和训练adapters
- local: model_sharing
title: 分享您的模型
- local: llm_tutorial
title: 使用LLMs进行生成
- local: generation_strategies
title: 生成策略
title: 教程
- sections:
- isExpanded: false
sections:
- local: tasks/asr
title: 自动语音识别
- local: tasks/sequence_classification
title: 文本分类
- local: tasks/token_classification
title: 词元分类
- local: tasks/question_answering
title: 问答
- local: tasks/translation
title: 翻译
- local: tasks/summarization
title: 摘要
- sections:
- local: fast_tokenizers
title: 使用 🤗 Tokenizers 中的分词器
- local: multilingual
title: 使用多语言模型进行推理
- local: create_a_model
title: 使用特定于模型的 API
- local: custom_models
title: 共享自定义模型
- local: chat_templating
title: 聊天模型的模板
- local: serialization
title: 导出为 ONNX
- local: gguf
title: 与 GGUF 格式的互操作性
- local: tiktoken
title: 与 Tiktoken 文件的互操作性
- local: community
title: 社区资源
title: 开发者指南
- sections:
- local: performance
title: 综述
- sections:
- local: fsdp
title: 完全分片数据并行
- local: perf_train_special
title: 在 Apple silicon 芯片上进行 PyTorch 训练
- local: perf_infer_gpu_multi
title: 多GPU推理
- local: perf_train_cpu
title: 在CPU上进行高效训练
- local: perf_hardware
title: 用于训练的定制硬件
- local: hpo_train
title: 使用Trainer API 进行超参数搜索
title: 高效训练技术
- local: big_models
title: 实例化大模型
- local: debugging
title: 问题定位及解决
- local: perf_torch_compile
title: 使用 `torch.compile()` 优化推理
title: 性能和可扩展性
- sections:
- local: contributing
title: 如何为 🤗 Transformers 做贡献?
- local: add_new_pipeline
title: 如何将流水线添加到 🤗 Transformers
title: 贡献
- sections:
- local: philosophy
title: Transformers的设计理念
- local: task_summary
title: 🤗Transformers能做什么
- local: tokenizer_summary
title: 分词器的摘要
- local: attention
title: 注意力机制
- local: bertology
title: 基于BERT进行的相关研究
title: 概念指南
- sections:
- sections:
- local: main_classes/callback
title: Callbacks
- local: main_classes/configuration
title: Configuration
- local: main_classes/data_collator
title: Data Collator
- local: main_classes/logging
title: Logging
- local: main_classes/model
title: 模型
- local: main_classes/text_generation
title: 文本生成
- local: main_classes/optimizer_schedules
title: Optimization
- local: main_classes/output
title: 模型输出
- local: main_classes/pipelines
title: Pipelines
- local: main_classes/processors
title: Processors
- local: main_classes/quantization
title: Quantization
- local: main_classes/tokenizer
title: Tokenizer
- local: main_classes/trainer
title: Trainer
- local: main_classes/deepspeed
title: DeepSpeed集成
- local: main_classes/feature_extractor
title: Feature Extractor
- local: main_classes/image_processor
title: Image Processor
title: 主要类
- sections:
- local: internal/modeling_utils
title: 自定义层和工具
- local: internal/pipelines_utils
title: pipelines工具
- local: internal/tokenization_utils
title: Tokenizers工具
- local: internal/trainer_utils
title: 训练器工具
- local: internal/generation_utils
title: 生成工具
- local: internal/image_processing_utils
title: 图像处理工具
- local: internal/audio_utils
title: 音频处理工具
- local: internal/file_utils
title: 通用工具
- local: internal/time_series_utils
title: 时序数据工具
- sections:
- local: model_doc/bert
title: BERT
title: 内部辅助工具
title: 应用程序接口 (API)