Transformers documentation
Optimum
Get started
Tutorials
Run inference with pipelinesWrite portable code with AutoClassPreprocess dataFine-tune a pretrained modelTrain with a scriptSet up distributed training with 🤗 AccelerateLoad and train adapters with 🤗 PEFTShare your modelAgents 101Agents, supercharged - Multi-agents, External tools, and moreGeneration with LLMsChatting with Transformers
Task Guides
Natural Language Processing
Audio
Computer Vision
Multimodal
Generation
Prompting
Developer guides
Use fast tokenizers from 🤗 TokenizersRun inference with multilingual modelsUse model-specific APIsShare a custom modelChat templatesTrainerRun training on Amazon SageMakerExport to ONNXExport to TFLiteExport to TorchScriptBenchmarksNotebooks with examplesCommunity resourcesTroubleshootInteroperability with GGUF filesInteroperability with TikToken filesModularity in `transformers`Model Hacking (overwriting a class to your usage)
Quantization Methods
Getting startedbitsandbytesGPTQAWQAQLMQuantoEETQHQQFBGEMM_FP8OptimumTorchAOBitNetcompressed-tensorsContribute new quantization method
Performance and scalability
OverviewLLM inference optimization Instantiate a big modelDebuggingXLA Integration for TensorFlow ModelsOptimize inference using `torch.compile()`
Efficient training techniques
Methods and tools for efficient training on a single GPUMultiple GPUs and parallelismFully Sharded Data ParallelDeepSpeedEfficient training on CPUDistributed CPU trainingTraining on TPU with TensorFlowPyTorch training on Apple siliconCustom hardware for trainingHyperparameter Search using Trainer API
Optimizing inference
Contribute
How to contribute to 🤗 Transformers?How to add a model to 🤗 Transformers?How to add a pipeline to 🤗 Transformers?TestingChecks on a Pull Request
Conceptual guides
PhilosophyGlossaryWhat 🤗 Transformers can doHow 🤗 Transformers solve tasksThe Transformer model familySummary of the tokenizersAttention mechanismsPadding and truncationBERTologyPerplexity of fixed-length modelsPipelines for webserver inferenceModel training anatomyGetting the most out of LLMs
API
Main Classes
Agents and ToolsAuto ClassesBackbonesCallbacksConfigurationData CollatorKeras callbacksLoggingModelsText GenerationONNXOptimizationModel outputsPipelinesProcessorsQuantizationTokenizerTrainerDeepSpeedExecuTorchFeature ExtractorImage Processor
Models
Text models
Vision models
Audio models
Video models
Multimodal models
Reinforcement learning models
Time series models
Graph models
Internal Helpers
You are viewing v4.47.0 version. A newer version v5.8.1 is available.
Optimum
The Optimum library supports quantization for Intel, Furiosa, ONNX Runtime, GPTQ, and lower-level PyTorch quantization functions. Consider using Optimum for quantization if you’re using specific and optimized hardware like Intel CPUs, Furiosa NPUs or a model accelerator like ONNX Runtime.
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