course documentation
Tokenizers, check!
0. Setup
1. Transformer models
IntroductionNatural Language Processing and Large Language ModelsTransformers, what can they do?How do Transformers work?How 🤗 Transformers solve tasksTransformer ArchitecturesQuick quizInference with LLMsBias and limitationsSummaryCertification exam
2. Using 🤗 Transformers
IntroductionBehind the pipelineModelsTokenizersHandling multiple sequencesPutting it all togetherBasic usage completed!Optimized Inference DeploymentEnd-of-chapter quiz
3. Fine-tuning a pretrained model
IntroductionProcessing the dataFine-tuning a model with the Trainer APIA full training loopUnderstanding Learning CurvesFine-tuning, Check!End-of-chapter quiz
4. Sharing models and tokenizers
The Hugging Face HubUsing pretrained modelsSharing pretrained modelsBuilding a model cardPart 1 completed!End-of-chapter quiz
5. The 🤗 Datasets library
IntroductionWhat if my dataset isn't on the Hub?Time to slice and diceBig data? 🤗 Datasets to the rescue!Creating your own datasetSemantic search with FAISS🤗 Datasets, check!End-of-chapter quiz
6. The 🤗 Tokenizers library
IntroductionTraining a new tokenizer from an old oneFast tokenizers' special powersFast tokenizers in the QA pipelineNormalization and pre-tokenizationByte-Pair Encoding tokenizationWordPiece tokenizationUnigram tokenizationBuilding a tokenizer, block by blockTokenizers, check!End-of-chapter quiz
7. Classical NLP tasks
IntroductionToken classificationFine-tuning a masked language modelTranslationSummarizationTraining a causal language model from scratchQuestion answeringMastering LLMsEnd-of-chapter quiz
8. How to ask for help
IntroductionWhat to do when you get an errorAsking for help on the forumsDebugging the training pipelineHow to write a good issuePart 2 completed!End-of-chapter quiz
9. Building and sharing demos
Introduction to GradioBuilding your first demoUnderstanding the Interface classSharing demos with othersIntegrations with the Hugging Face HubAdvanced Interface featuresIntroduction to BlocksGradio, check!End-of-chapter quiz
10. Curate high-quality datasets
Introduction to ArgillaSet up your Argilla instanceLoad your dataset to ArgillaAnnotate your datasetUse your annotated datasetArgilla, check!End-of-chapter quiz
11. Fine-tune Large Language Models
IntroductionChat TemplatesFine-Tuning with SFTTrainerLoRA (Low-Rank Adaptation)EvaluationConclusionExam Time!
12. Build Reasoning Models new
IntroductionReinforcement Learning on LLMsThe Aha Moment in the DeepSeek R1 PaperAdvanced Understanding of GRPO in DeepSeekMathImplementing GRPO in TRLPractical Exercise to Fine-tune a model with GRPOPractical Exercise with UnslothComing soon...
Course Events
Tokenizers, check!
Great job finishing this chapter!
After this deep dive into tokenizers, you should:
- Be able to train a new tokenizer using an old one as a template
- Understand how to use offsets to map tokens’ positions to their original span of text
- Know the differences between BPE, WordPiece, and Unigram
- Be able to mix and match the blocks provided by the 🤗 Tokenizers library to build your own tokenizer
- Be able to use that tokenizer inside the 🤗 Transformers library