Programming

Hugging Face Transformers

Master state-of-the-art Natural Language Processing (NLP) and generative AI with Hugging Face Transformers. From core Transformer architectures (BERT, GPT, T5, LLaMA, Mistral) and Auto Classes to tokenization, high-level pipelines, dataset engineering with datasets, model fine-tuning with the Trainer API, advanced …

35 hours Duration
15 Modules
Free Access

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

Introduction to Hugging Face Transformers

Welcome to the premier open-source NLP and generative AI ecosystem. Understand what Hugging Face is, why Transformers have revolutionized AI, explore the library's features and architecture, examine the broader ecosystem, install packages, set up your …

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Learning objectives

• Understand what Hugging Face is and its role in democratizing AI
• Explain the breakthrough importance of the Transformer architecture
• Explore key features: pre-trained models, multi-framework support, and reproducibility
• Navigate the Hugging Face ecosystem (Transformers, Datasets, Hub, Spaces)
• Install the transformers library with PyTorch and CUDA support
• Configure your Python development environment for NLP
• Build and execute your first functional Transformers application using pipeline()

Module 2 Locked

Transformer Models

Deep dive into the core Transformer architectural paradigms. Understand Encoder-only, Decoder-only, and Encoder-Decoder architectures. Compare landmark models including BERT, GPT, T5, LLaMA, and Mistral, and learn how to choose the right architecture for your specific …

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Learning objectives

• Understand self-attention and multi-head attention mechanisms
• Analyze Encoder-only models (BERT, RoBERTa) for language understanding
• Analyze Decoder-only models (GPT, LLaMA, Mistral) for generative text
• Analyze Encoder-Decoder models (T5, BART) for sequence-to-sequence translation
• Explore modern open-weights foundation models (LLaMA 3, Mistral 7B)
• Evaluate trade-offs between model size, context length, latency, and cost
• Select the optimal model family for classification, search, or generation

Module 3 Locked

Auto Classes

Write clean, model-agnostic, and future-proof code using Hugging Face Auto Classes. Learn how AutoTokenizer, AutoModel, and task-specific heads (AutoModelForSequenceClassification, AutoModelForCausalLM, AutoModelForQuestionAnswering) automatically instantiate the correct architecture from checkpoint names, explore AutoConfig, and apply best practices.

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Learning objectives

• Understand the design philosophy and power of Auto Classes
• Instantiate tokenizers dynamically with AutoTokenizer.from_pretrained()
• Load raw backbones with AutoModel and task-specific heads with AutoModelFor*
• Configure classification models with AutoModelForSequenceClassification
• Load generative LLMs with AutoModelForCausalLM
• Inspect and adjust architectural hyperparameters using AutoConfig
• Write unified code that switches seamlessly between different models

Module 4 Locked

Tokenizers

Master tokenization, the essential bridge between human language and neural networks. Explore subword algorithms (BPE, WordPiece, SentencePiece), load fast tokenizers, encode text into input_ids, decode tokens back to strings, handle padding, truncation, attention masks, special …

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Learning objectives

• Understand tokenization algorithms: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece
• Load tokenizers using AutoTokenizer.from_pretrained()
• Encode text strings into input_ids and convert token IDs back to text
• Manage sequence lengths with padding="max_length" and truncation=True
• Understand the role and importance of Attention Masks in batches
• Handle model-specific special tokens (BOS, EOS, CLS, SEP, PAD, UNK)
• Execute high-throughput batch tokenization with fast Rust-backed tokenizers

Module 5 Locked

Pipelines

Run inference instantly across standard NLP tasks using Hugging Face Pipelines. Explore ready-to-use pipelines for sentiment analysis, text classification, Named Entity Recognition, question answering, summarization, machine translation, text generation, and fill-mask prediction. Learn pipeline customization …

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Learning objectives

• Understand how the pipeline abstraction orchestrates pre-processing, inference, and post-processing
• Execute sentiment analysis and multi-class classification pipelines
• Extract entities from unstructured text using token-classification (NER) pipelines
• Answer questions over context documents with question-answering pipelines
• Generate concise summaries and translate languages using seq2seq pipelines
• Generate text continuations with text-generation pipelines
• Customize pipelines with specific model checkpoints, batch sizes, and GPU devices

Module 6 Locked

Working with Models

Gain programmatic control over pre-trained models. Load models with from_pretrained(), configure architectures, execute single and batched inference, master generation parameters (temperature, top-k, top-p, beam search), inspect logits and hidden states, and optimize model execution.

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Learning objectives

• Download and cache pre-trained models using from_pretrained()
• Inspect and customize model configurations with PretrainedConfig
• Execute forward passes, inspect output logits, and compute loss
• Perform batch inference efficiently with torch.no_grad()
• Tune text generation parameters: max_new_tokens, temperature, top_k, top_p
• Compare greedy search with Beam Search decoding
• Load models in half-precision (float16 / bfloat16) to conserve GPU memory

Module 7 Locked

Datasets

Harness the Hugging Face `datasets` library for efficient data engineering. Load public benchmark datasets from the Hub, create custom datasets from local files (CSV, JSON, Parquet), manage train/validation splits, preprocess and tokenize at scale using …

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Learning objectives

• Understand the memory-mapped Apache Arrow foundation of the datasets library
• Load public datasets instantly using load_dataset("dataset_name")
• Create datasets from local CSV, JSON, Parquet, and text files
• Split datasets into training, validation, and test splits
• Apply fast batch tokenization using dataset.map(batched=True)
• Filter and reformat dataset columns using filter() and remove_columns()
• Set tensor output formats for PyTorch or TensorFlow with set_format()

Module 8 Locked

Fine-Tuning Models

Adapt pre-trained models to domain-specific tasks using the Hugging Face Trainer API. Configure TrainingArguments, use dynamic Data Collators, write custom evaluation metrics with compute_metrics, execute training and validation loops, save checkpoints, reload fine-tuned models, and …

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Learning objectives

• Understand the workflow of transfer learning and supervised fine-tuning in NLP
• Configure hyperparameters with TrainingArguments (learning_rate, epochs, batch_size, evaluation_strategy)
• Use DataCollatorWithPadding for dynamic batch-level sequence padding
• Implement compute_metrics callbacks using the evaluate library
• Train and evaluate models using the Trainer abstraction
• Manage training checkpoints and resume training after interruptions
• Save and reload fine-tuned models for production inference

Module 9 Locked

Text Generation

Master decoding algorithms and generation strategies for generative language models. Compare Greedy Search, Beam Search, Temperature-controlled sampling, Top-k and Top-p (nucleus) sampling. Control repetition with repetition penalties, configure stop sequences, and apply generation best practices.

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Learning objectives

• Understand the autoregressive token generation loop
• Analyze the limitations of Greedy Search and when to avoid it
• Configure Beam Search with num_beams and early_stopping
• Implement Top-k and Top-p (nucleus) stochastic sampling
• Control generation creativity and randomness using temperature
• Eliminate repetitive text loops using repetition_penalty and no_repeat_ngram_size
• Halt generation cleanly with stop strings and eos_token_id

Module 10 Locked

NLP Tasks

Tackle the full spectrum of Natural Language Processing tasks using Transformers. Explore Text Classification, Sentiment Analysis, Named Entity Recognition (NER), Extractive/Abstractive Question Answering, Text Summarization, Machine Translation, Fill-Mask language modeling, Zero-Shot Classification, and Feature Extraction.

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Learning objectives

• Implement sentence-level and document-level text classification
• Extract granular domain entities (people, products, dates) with token classification
• Build extractive question-answering systems over reference documentation
• Summarize long articles into concise executive summaries
• Translate text across languages using sequence-to-sequence models
• Predict masked tokens using bidirectional language models (Fill-Mask)
• Perform Zero-Shot Classification on new categories without re-training
• Extract contextual semantic embeddings for downstream clustering and retrieval

Module 11 Locked

Model Hub

Collaborate and distribute models using the Hugging Face Model Hub. Download and search models, upload fine-tuned checkpoints with push_to_hub(), create comprehensive Model Cards, manage Git-based model versioning, configure private repositories, access gated foundation models (LLaMA), …

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Learning objectives

• Navigate and search the Hugging Face Model Hub for pre-trained weights
• Authenticate sessions using huggingface_hub.login() and CLI tokens
• Push models, tokenizers, and configs to the Hub using push_to_hub()
• Author thorough Model Cards detailing training parameters, metrics, and limitations
• Manage Git-based revisions, branches, and commit hashes for models
• Manage private team repositories and configure organization access controls
• Apply for access and download weights for gated models like LLaMA

Module 12 Locked

Deployment

Transition Transformer models into production services. Master model serialization with save_pretrained(), export architectures to ONNX for runtime acceleration, build high-concurrency inference microservices using FastAPI, implement 8-bit and 4-bit quantization, and adhere to production deployment standards.

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Learning objectives

• Save and package fine-tuned models locally using save_pretrained()
• Export models to the open ONNX standard using optimum and onnxruntime
• Construct high-performance async REST API endpoints with FastAPI
• Apply quantization (8-bit, 4-bit) to shrink memory footprint and improve latency
• Implement request batching and warmup inference in production servers
• Containerize Transformers services using Docker
• Apply best practices for latency, throughput, and zero-downtime rolling deployments

Module 13 Locked

Performance Optimization

Maximize throughput and slash latency across Transformer workloads. Learn GPU inference optimization, batch inference strategies, Mixed Precision execution (FP16 / BF16), KV-caching mechanics for autoregressive models, 4-bit/8-bit quantization with bitsandbytes, memory profiling, and performance benchmarking.

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Learning objectives

• Profile latency and VRAM bottlenecks in Transformer pipelines
• Accelerate inference with FlashAttention and SDPA (Scaled Dot-Product Attention)
• Understand Key-Value (KV) caching to eliminate redundant attention computation
• Load massive foundation models in 4-bit (NF4) and 8-bit using bitsandbytes
• Optimize inference throughput with dynamic batching
• Leverage half-precision (torch.float16 and torch.bfloat16) on modern GPUs
• Benchmark tokens-per-second and time-to-first-token (TTFT) metrics

Module 14 Locked

Hugging Face Ecosystem

Harness the complete ecosystem of modern Hugging Face libraries. Master `accelerate` for distributed scaling without code rewrites, `peft` for Parameter-Efficient Fine-Tuning (LoRA / QLoRA), `safetensors` for secure zero-copy model storage, `trl` for alignment and reinforcement …

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Learning objectives

• Understand how specialized Hugging Face libraries work in concert with Transformers
• Train across multi-GPU and DeepSpeed setups using the Accelerate library
• Fine-tune massive models efficiently with PEFT (Parameter-Efficient Fine-Tuning) using LoRA
• Store and load model weights safely with safetensors to prevent code injection attacks
• Explore model alignment techniques (SFT, DPO, PPO) with TRL (Transformer Reinforcement Learning)
• Compute standardized benchmark metrics with the evaluate library
• Explore multimodal extensions with the Diffusers library

Module 15 Locked

Project Structure & Best Practices

Organize, maintain, and ship production-ready Transformers projects. Implement clean repository layouts, manage environment dependencies and secrets, configure structured logging and error handling, enforce model versioning, execute inference testing, follow deployment checklists, and troubleshoot common pitfalls.

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Learning objectives

• Architect modular enterprise Transformers repositories (data, models, training, serving)
• Manage virtual environments, PyTorch/CUDA compatibility, and dependencies with poetry/pip
• Secure sensitive API tokens and credentials using environment variables
• Implement structured logging and telemetry for model inputs, latency, and outputs
• Implement defensive error handling for out-of-vocabulary tokens and sequence length overflows
• Ensure model reproducibility with fixed seeds and explicit checkpoint revisions
• Execute pre-deployment checklists before promoting models to production

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