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31 lines
1.7 KiB
Markdown
31 lines
1.7 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Trainer
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[`Trainer`] is a complete training and evaluation loop for Transformers models. You only need a model and dataset to get started.
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<Youtube id="nvBXf7s7vTI"/>
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Underneath, [`Trainer`] handles batching, shuffling, and padding your dataset into tensors. The training loop runs the forward pass, calculates loss, backpropagates gradients, and updates weights. Configure the training run with [`TrainingArguments`] to customize everything from batch size and training duration to distributed strategies, compilation, and more.
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## Next steps
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- Start with the [fine-tuning](./training) tutorial for an introduction to training a large language model with [`Trainer`].
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- Check the [Subclassing Trainer methods](./trainer_customize) guide for examples of how to subclass [`Trainer`] methods.
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- See the [Data collators](./data_collators) guide to learn how to create a data collator for custom batch assembly.
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- See the [Callbacks](./trainer_callbacks) guide to learn how to hook into training events.
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