Hugging Face: Hands-On Course
Learn the full Hugging Face ecosystem in this hands-on course. Transformers, datasets, Trainer API, PEFT, RLHF, diffusers, Accelerate, Spaces, and deployment. 50+ hours, 5 real projects.
Course Snapshot
| Course Code | AIM-603 |
| Duration | 50+ hours |
| Modules | 12 |
| Projects | 5 hands-on projects |
| Level | Intermediate to Advanced |
| Format | Self-paced + live sessions |
| Tools | Transformers, Datasets, PEFT, TRL, Diffusers, Accelerate, Spaces |
| Certificate | Yes, upon completion |
| Price | ₹4,999 (early bird ₹3,499) |
Course Overview
The Hugging Face ecosystem has become the backbone of modern machine learning, uniting transformers, datasets, fine-tuning, and deployment under a single, open-source umbrella. This course takes you from zero to confident practitioner across every major library in the stack. You will start with the Transformers library, learning how to load pre-trained models, tokenize text, and solve real NLP tasks like classification, summarization, and question answering. From there you will move to the Datasets library, mastering efficient data loading and processing for datasets that don’t fit in memory. Next, you will train and fine-tune models with the Trainer API, then push into parameter-efficient fine-tuning with PEFT and reinforcement learning from human feedback with TRL. You will generate images and audio with Diffusers, scale training across GPUs with Accelerate, and optimize for production with Optimum. Finally, you will deploy interactive demos to Hugging Face Spaces and leverage advanced Hub features like model cards, discussions, and gated repositories. By the end, you will have a portfolio of five deployed projects and the skills to use Hugging Face as your daily ML workbench.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
ML Engineers
Engineers who want to integrate Hugging Face libraries into production pipelines for NLP, vision, and audio tasks.
Data Scientists
Data scientists transitioning from traditional ML to transformer-based workflows and needing hands-on fine-tuning skills.
AI Developers
Application developers building AI-powered features who need to leverage pre-trained models and deploy them efficiently.
Researchers
Academic and industry researchers who want to reproduce, adapt, and extend state-of-the-art models from the Hub.
This Course Is NOT For You If:
- Absolute beginners with no Python experience — start with a Python and ML foundations course first.
- Those looking for a pure theory course — this is hands-on and project-driven.
- People who only want prompt engineering without touching model internals or training code.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to load and use any pre-trained model from the Hugging Face Hub for text, vision, and audio tasks.
- 2 You will be able to tokenize, process, and manage large datasets efficiently with the Datasets library.
- 3 You will be able to train and fine-tune transformer models using the Trainer API and custom training loops.
- 4 You will be able to apply parameter-efficient fine-tuning (LoRA, QLoRA, IA3) to reduce GPU costs dramatically.
- 5 You will be able to implement RLHF and DPO workflows using TRL to align models with human preferences.
- 6 You will be able to generate images and audio with the Diffusers library and customize diffusion pipelines.
- 7 You will be able to scale training and inference across multiple GPUs with Accelerate and Optimum.
- 8 You will be able to deploy interactive ML demos to Hugging Face Spaces using Gradio and Streamlit.
- 9 You will be able to publish models and datasets to the Hub with proper model cards, licensing, and metadata.
- 10 You will be able to navigate advanced Hub features including gated repos, discussions, and organizations.
Download Free Course Syllabus
Get the complete detailed syllabus with all modules, lessons, and project descriptions delivered to your inbox.
Download Free Syllabus →Complete Course Curriculum
12 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Hugging Face Ecosystem Overview
3 hours- The history and mission of Hugging Face as an open-source AI platform
- The core libraries: Transformers, Datasets, Tokenizers, PEFT, TRL, Diffusers, Accelerate, Optimum
- The Model Hub and how open-source model sharing transformed ML
- How Hugging Face fits into the broader ML and AI ecosystem
- Installing the huggingface_hub CLI and Python libraries
- Configuring authentication tokens for Hub access
- Setting up GPU environments locally and in the cloud
- Creating your first Hugging Face account and organization
- Browsing and searching the Model Hub by task, library, and license
- Understanding model cards, tags, and metadata conventions
- Exploring the Datasets Hub and dataset viewer
- Touring Spaces and the types of apps you can deploy
MODULE 2 — Transformers Fundamentals
5 hours- Self-attention, multi-head attention, and positional encoding explained
- Encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5) model families
- How tokenizers convert text into model-ready input IDs
- Loading a model and tokenizer with AutoClass and from_pretrained
- Using pipeline() for zero-setup inference on common tasks
- Supported tasks: text classification, NER, summarization, translation, QA, and more
- Specifying models and revision pins for reproducibility
- Batching inputs and handling long documents with chunking
- Fast vs slow tokenizers and why fast tokenizers matter
- Padding, truncation, and max length strategies
- Encoding, decoding, and special tokens like [CLS] and [SEP]
- Saving and loading custom tokenizers
MODULE 3 — NLP Tasks with Transformers
5 hours- Sentiment analysis, topic classification, and toxicity detection
- Using AutoModelForSequenceClassification for inference
- Handling multi-class and multi-label classification
- Interpreting model logits and confidence scores
- Named entity recognition with AutoModelForTokenClassification
- BIO tagging schemes and entity post-processing
- Aligning tokenizer outputs with word-level labels
- Evaluating NER models with seqeval metrics
- Abstractive summarization with BART and T5 models
- Machine translation across language pairs
- Extractive and generative question answering
- Controlling generation with beam search, sampling, and temperature
MODULE 4 — Datasets Library
4 hours- Using load_dataset to fetch data from the Hub or local files
- Dataset and DatasetDict object structure and methods
- The dataset viewer for quick exploration and filtering
- Handling CSV, JSON, parquet, and custom loading scripts
- Using map() for batched and unbatched transformations
- Tokenizing datasets efficiently with batched mapping
- Filtering, shuffling, and selecting subsets of data
- Caching processed datasets for faster iteration
- Why streaming matters for datasets larger than RAM
- Using streaming=True with iterable datasets
- Combining streaming with training pipelines
- Performance considerations and memory profiling
MODULE 5 — Training with the Trainer API
5 hours- Setting up TrainingArguments for batch size, epochs, and learning rate
- Creating a Trainer with model, args, datasets, and compute_metrics
- Using data collators for dynamic padding and task-specific batching
- Running train(), evaluate(), and predict() workflows
- Integrating the evaluate library for standard metrics
- Computing accuracy, F1, BLEU, ROUGE, and perplexity
- Building custom metric functions for specialized tasks
- Logging metrics to the Hub and external experiment trackers
- Saving checkpoints locally and managing disk space
- Using push_to_hub to share models directly from training
- Creating model cards automatically during training
- Versioning models with commits and tags on the Hub
MODULE 6 — PEFT & Efficient Fine-Tuning
5 hours- The cost of full fine-tuning and when it becomes impractical
- How PEFT methods reduce trainable parameters by orders of magnitude
- Overview of LoRA, QLoRA, IA3, and prefix tuning
- Memory and storage savings compared to full fine-tuning
- Configuring LoraConfig with rank, alpha, and target modules
- Quantizing base models to 4-bit and 8-bit with bitsandbytes
- Training with QLoRA on consumer GPUs
- Merging LoRA adapters back into the base model for deployment
- Saving and loading PEFT adapters separately from base models
- Swapping adapters at runtime for multi-task serving
- Combining multiple adapters and evaluating compatibility
- Pushing adapters to the Hub for community sharing
MODULE 7 — TRL and RLHF
4 hours- The three stages of RLHF: SFT, reward model, and PPO
- Why RLHF improves alignment and reduces harmful outputs
- Collecting and formatting human preference data
- Overview of the TRL library and its trainer classes
- Supervised fine-tuning with SFTTrainer on instruction data
- Training a reward model with RewardTrainer
- Formatting prompt-response pairs for preference learning
- Evaluating reward model quality and calibration
- Running PPOTrainer to optimize a policy against the reward model
- Configuring KL penalties and generation parameters
- Direct Preference Optimization as a simpler alternative to PPO
- Comparing DPO and PPO trade-offs in practice
MODULE 8 — Diffusers Library
4 hours- How diffusion models generate images from noise iteratively
- UNet architecture, schedulers, and the denoising loop
- Stable Diffusion and the ecosystem of open diffusion models
- Loading pipelines with DiffusionPipeline.from_pretrained
- Controlling image quality with num_inference_steps and guidance scale
- Using different schedulers like DDIM, Euler, and DPM-Solver
- Seed management for reproducible image generation
- Batch generation and memory optimization with attention slicing
- Image-to-image generation and inpainting pipelines
- ControlNet for structural conditioning with edge and pose maps
- LoRA fine-tuning for custom styles and subjects
- Text-to-video and audio generation pipelines
MODULE 9 — Accelerate & Optimum
4 hours- Why Accelerate simplifies distributed training without code rewrites
- Configuring single-GPU, multi-GPU, and TPU launchers
- Using accelerate config and the accelerate CLI
- Mixed precision training with fp16 and bf16
- DataParallel, DistributedDataParallel, and FSDP explained
- Gradient accumulation and gradient checkpointing for memory savings
- DeepSpeed integration for ZeRO optimization stages
- Monitoring distributed training with torch.distributed utilities
- Exporting models to ONNX and TorchScript with Optimum
- Optimizing for Intel CPUs and Habana accelerators
- Quantization and pruning workflows
- Benchmarking optimized models against baseline inference
MODULE 10 — Spaces & Deployment
4 hours- Creating interactive ML demos with Gradio Interface and Blocks
- Connecting Spaces to Hub models for live inference
- Managing dependencies with requirements.txt and environment files
- Free and paid hardware options on Spaces
- Building data dashboards with Streamlit on Spaces
- Using Docker Spaces for full custom environments
- Persistent storage and secrets management on Spaces
- Embedding Spaces in external websites and applications
- Choosing between Spaces, Inference Endpoints, and self-hosting
- Setting up Inference Endpoints for production API serving
- Monitoring latency, throughput, and cost in production
- Implementing caching and batching for inference efficiency
MODULE 11 — Advanced Hub Features
3 hours- Writing effective model cards with intended use and limitations
- Using YAML metadata for tags, language, and task classification
- Gated repositories and approval workflows for sensitive models
- Licensing models with permissive and restrictive licenses
- Creating and managing organizations on the Hub
- Team permissions and repository access control
- Using discussions and pull requests for collaborative model development
- Versioning datasets and models with refs and tags
- Using the huggingface_hub Python library for programmatic access
- Automating model uploads, downloads, and metadata updates
- Webhooks and CI/CD integration for model lifecycle management
- Rate limits and best practices for API usage
MODULE 12 — Capstone Projects
4 hours- Selecting a project that combines multiple libraries from the course
- Designing data pipelines, training, and deployment architecture
- Setting up reproducible environments and version control
- Defining success metrics and evaluation criteria
- Implementing the full pipeline from data to deployment
- Debugging common training and inference issues
- Iterating on model quality with evaluation-driven development
- Documenting the project with a comprehensive model card
- Deploying the final project to Hugging Face Spaces
- Writing a project README and demo walkthrough
- Sharing on social media and the Hugging Face community
- Gathering feedback and planning next improvements
Real-World Projects & Portfolio Outcomes
5 portfolio-worthy projects that prove your skills to employers.
Multi-Task NLP Hub
Build a single Hugging Face Space that serves a text classifier, a summarizer, and a question answering model, letting users switch between tasks in one interface.
Custom Dataset Trainer
Load a domain-specific dataset from the Hub, tokenize it efficiently, fine-tune a transformer model with the Trainer API, and push the result to your Hub account.
QLoRA Fine-Tuning on a Budget
Fine-tune a 7B parameter model on a consumer GPU using QLoRA, demonstrating memory-efficient training without sacrificing quality.
Stable Diffusion Style Studio
Create a Diffusers-powered Space that generates images in a custom style using a LoRA fine-tuned on a small set of reference images.
End-to-End RLHF Pipeline
Implement a complete RLHF or DPO pipeline: supervised fine-tuning, preference data collection, reward modeling, and policy optimization using TRL.
Tools, Technologies & Models Covered
LLM Models Covered
| BERT | Text classification, NER, embeddings | |
|---|---|---|
| T5 | Text-to-text generation, summarization, translation | |
| Llama 3 | Meta | Decoder-only LLM for generation and chat |
| Mistral | Mistral AI | Efficient open LLM for fine-tuning |
| Stable Diffusion XL | Stability AI | Text-to-image generation with Diffusers |
| Whisper | OpenAI | Speech recognition and transcription |
| BART | Summarization and sequence-to-sequence tasks | |
| Phi-3 | Microsoft | Small language models for efficient inference |
Tools & Frameworks Covered
| Transformers | Core model loading and inference | Deep |
|---|---|---|
| Datasets | Data loading and processing | Deep |
| Tokenizers | Text encoding and decoding | Intermediate |
| Trainer API | Training loop abstraction | Deep |
| PEFT | Parameter-efficient fine-tuning | Deep |
| TRL | RLHF and preference alignment | Intermediate |
| Diffusers | Diffusion model pipelines | Intermediate |
| Accelerate | Distributed training | Intermediate |
| Optimum | Inference optimization | Intermediate |
| Evaluate | Metrics computation | Intermediate |
| Gradio | Interactive ML demos | Intermediate |
| huggingface_hub | Hub API and CLI | Deep |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Ananya Sharma
Senior ML Engineer & Hugging Face Community Contributor
10+ years in machine learning and NLP
30+ deployed models on the Hugging Face Hub
4,000+ engineers trained across corporate and public cohorts
Learning Hugging Face is not about memorizing APIs — it is about understanding the ecosystem deeply enough to build, ship, and share real ML systems.
Course Team & Curriculum Design
Our instructor team includes two additional Hugging Face contributors who co-developed the curriculum and lead live coding sessions, plus three teaching assistants who provide 1:1 support during project weeks.
Learning Path & Prerequisites
Prerequisites
- Intermediate Python programming (functions, classes, and basic data processing)
- Familiarity with PyTorch or willingness to learn basics quickly
- Understanding of core ML concepts: training, evaluation, overfitting
- Basic knowledge of NLP tasks like classification and generation
- A Google Colab or local GPU environment for hands-on exercises
Recommended Learning Paths
ML Engineer building production NLP systems
- Complete Modules 1-5 for core Transformers and Trainer API fluency
- Focus on Module 6 (PEFT) to reduce training costs in your job
- Use Module 9 (Accelerate) to scale to multi-GPU training
- Deploy with Module 10 (Spaces) for internal demos and stakeholder buy-in
Researcher reproducing and extending SOTA models
- Master Modules 1-4 for loading and processing research datasets
- Deep-dive Modules 6-7 for PEFT and RLHF research workflows
- Use Module 11 (Advanced Hub) for collaboration and reproducibility
- Complete the capstone to publish a reproducible research artifact
Application developer adding AI features
- Start with Modules 1-3 for inference-only workflows
- Skim Modules 4-5 to understand training for custom use cases
- Focus heavily on Module 10 (Spaces) for rapid prototyping
- Build the Multi-Task NLP Hub project as a template for your app
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹4,999 | All 12 modules, 5 projects, community access, certificate |
|---|---|---|
| Early Bird | ₹3,499 | Same as Self-Paced, limited time pricing |
| Cohort + Mentorship | ₹12,999 | Live sessions, 1:1 mentorship, project reviews, priority support |
What Is Included
- Lifetime access to all 12 modules and future updates
- 5 hands-on projects with detailed walkthroughs
- Downloadable code templates and cheat sheets
- Community Discord with instructors and peers
- Certificate of completion recognized by partner companies
- Direct feedback on your deployed Hugging Face Spaces
30-day money-back guarantee — if you complete the first three modules and don’t feel this course delivers, get a full refund, no questions asked.
Frequently Asked Questions
Do I need a GPU to take this course?
You can complete most exercises using Google Colab’s free GPU tier. For the QLoRA and RLHF modules, a Colab Pro or local GPU with 8GB+ VRAM is recommended but not strictly required for following along with the provided notebooks.
How is this different from the official Hugging Face course?
The official course is excellent for beginners. This course goes deeper into PEFT, RLHF, Diffusers, Accelerate, and production deployment, with five structured projects and instructor feedback that the free course does not provide.
Will I learn about vision and audio models too?
Yes. While the course emphasizes NLP, Modules 8 and 10 cover Diffusers for image generation and Whisper for speech, giving you cross-modal exposure to the Hugging Face ecosystem.
Is the certificate recognized by employers?
The certificate demonstrates hands-on competency with five deployed projects on your Hugging Face profile. Many hiring managers value a public portfolio of working models more than any certificate, and this course helps you build exactly that.
How long does it take to complete the course?
At 50+ hours of content, most learners complete the course in 6-10 weeks at 5-8 hours per week. The self-paced format lets you go faster or slower as your schedule allows.
What if I get stuck on a project?
Every project includes a detailed walkthrough, and the community Discord has teaching assistants who respond within 24 hours. Cohort students also get 1:1 project reviews with instructors.
Are the code templates reusable for my own projects?
Absolutely. All training scripts, PEFT configs, Gradio templates, and deployment scripts are MIT-licensed and designed as starting points for your real-world work.
Does the course cover the latest models like Llama 3 and Mistral?
Yes. The curriculum is updated every quarter to include the latest open-source models. The lastUpdated field on this page reflects the most recent revision.
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