# huggingface/course

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3,715 stars · 1,254 forks · MDX · apache-2.0

## Links

- GitHub: https://github.com/huggingface/course
- Homepage: https://huggingface.co/course
- awesome-repositories: https://awesome-repositories.com/repository/huggingface-course.md

## Topics

`deep-learning` `hacktoberfest` `nlp` `transformers`

## Description

This project is an educational course and learning curriculum for implementing and fine-tuning transformer models using the Hugging Face ecosystem. It serves as a structured guide and technical walkthrough for processing multimodal data, adapting pre-trained neural networks, and deploying models.

The material includes a guide for managing, versioning, and distributing model weights and datasets through a centralized asset hub. It also provides a practical tutorial on adapting models to specific datasets using parameter-efficient methods and an implementation guide for solving natural language processing tasks using tokenizers.

The course covers a broad range of machine learning capabilities, including dataset management and curation, multimodal data processing for speech and computer vision, and the deployment of models via inference endpoints and interactive demonstrations.

## Tags

### Artificial Intelligence & ML

- [Machine Learning Guides](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-guides.md) — Serves as a structured educational resource for processing multimodal data and deploying machine learning models.
- [Model Hub Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-hub-integrations.md) — Guides the management, versioning, and distribution of model weights and datasets via the Hugging Face Hub.
- [Transformer Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-architectures.md) — Implements deep learning models based on attention mechanisms for sequence processing across text, image, and audio.
- [Dataset Curation](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets/dataset-curation.md) — Provides methods for preparing and processing raw data into formats compatible with model training.
- [Fine-Tuning Tutorials](https://awesome-repositories.com/f/artificial-intelligence-ml/fine-tuning-tutorials.md) — Offers practical guides and examples for adapting pre-trained models to specific datasets.
- [Large Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-fine-tuning.md) — Covers parameter-efficient fine-tuning and reinforcement learning to adapt large language models. ([source](https://huggingface.co/))
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Walks through the process of fine-tuning and aligning models with specific objectives. ([source](https://huggingface.co/course/bn/chapter1/1))
- [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Provides practical tutorials on adapting pre-trained models to specific datasets. ([source](https://huggingface.co/course/en/chapter1/1))
- [Model Asset Collaboration](https://awesome-repositories.com/f/artificial-intelligence-ml/model-asset-collaboration.md) — Store and share models, datasets, and applications in a public repository to enable collaborative development. ([source](https://huggingface.co/))
- [Multimodal Data Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-data-processing.md) — Extends transformer capabilities to process non-text data including speech and computer vision. ([source](https://huggingface.co/course/bn/chapter1/1))
- [Multimodal Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-processing.md) — Extends transformer architectures to process multimodal data, including speech and computer vision. ([source](https://huggingface.co/course/de/chapter1/1))
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Guides the implementation of transformer models to solve complex natural language processing tasks. ([source](https://huggingface.co/course/bn/chapter1/1))
- [Text Tokenization](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing/text-tokenization.md) — Provides a technical walkthrough for segmenting raw text into tokens to make it compatible with neural networks.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Provides a practical tutorial on updating small subsets of model weights to adapt networks to specific tasks.
- [Vocabulary Mappings](https://awesome-repositories.com/f/artificial-intelligence-ml/subword-tokenization-methods/vocabulary-mappings.md) — Explains how to map text tokens to integer indices using a predefined dictionary for tensor compatibility.
- [Text Dataset Preparation](https://awesome-repositories.com/f/artificial-intelligence-ml/text-dataset-preparation.md) — Provides methods for cleaning and formatting large-scale text corpora for language model consumption. ([source](https://huggingface.co/course/en/chapter1/1))
- [Training Dataset Management](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-management.md) — Provides tools and techniques for processing and curating high-quality datasets for model refinement. ([source](https://huggingface.co/course/chapter1/1))
- [Transformer Language Models](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-language-models.md) — Implements natural language processing tasks using transformer-based language models. ([source](https://huggingface.co/course/chapter1/1))
- [Transformer Models](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-models.md) — Provides guidance on using pre-trained transformer architectures to solve natural language processing tasks.
- [Machine Learning Demo Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-demo-platforms.md) — Teaches how to build and share interactive browser-based demonstrations of machine learning models. ([source](https://huggingface.co/course/es/chapter1/1))
- [Model Deployment Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/deployment-pipelines-and-endpoints/model-deployment-pipelines.md) — Provides toolchains for optimizing and serving machine learning models through deployment pipelines.
- [Model Inference APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/inference-servers-and-runtimes/model-inference-apis.md) — Provides guidance on exposing neural networks via standardized HTTP endpoints for remote prediction.
- [Model Interactive Demos](https://awesome-repositories.com/f/artificial-intelligence-ml/model-interactive-demos.md) — Provides a guide for creating interactive applications to demonstrate model performance to a public audience. ([source](https://huggingface.co/course/en/chapter1/1))
- [Model Weight Management](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management.md) — Implements utilities for the secure storage and retrieval of pre-trained model weights. ([source](https://huggingface.co/))
- [Self-Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/self-attention-mechanisms.md) — Teaches the use of self-attention mechanisms to capture long-range dependencies within sequential data.

### Part of an Awesome List

- [Model Fine-Tuning](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/model-fine-tuning.md) — Details the process of optimizing pre-trained models on task-specific datasets.
- [Implementation Guides](https://awesome-repositories.com/f/awesome-lists/ai/nlp-tasks/implementation-guides.md) — Provides technical walkthroughs for using tokenizers and pre-trained models to solve NLP tasks.
- [Pre-trained Language Models](https://awesome-repositories.com/f/awesome-lists/ai/pre-trained-language-models.md) — Guides the implementation of natural language processing tasks using pre-trained transformer models. ([source](https://huggingface.co/course/de/chapter1/1))
- [Model Optimization and Deployment](https://awesome-repositories.com/f/awesome-lists/ai/model-optimization-and-deployment.md) — Includes techniques for compressing and optimizing models to ensure efficiency in production. ([source](https://huggingface.co/course/bn/chapter1/1))
- [Multimodal Architectures](https://awesome-repositories.com/f/awesome-lists/ai/multimodal-architectures.md) — Demonstrates how to adapt transformer architectures to integrate visual and audio data for complex reasoning.
- [Model Discovery](https://awesome-repositories.com/f/awesome-lists/ai/pre-trained-language-models/model-discovery.md) — Explains how to search for pre-trained models using criteria like task type, language, and licensing. ([source](https://huggingface.co/models))

### DevOps & Infrastructure

- [Model Asset Hubs](https://awesome-repositories.com/f/devops-infrastructure/version-control-management/version-control/asset-distribution-repositories/model-asset-hubs.md) — Teaches how to manage, version, and distribute model weights and datasets through a centralized asset hub.
- [Model Endpoint Deployment](https://awesome-repositories.com/f/devops-infrastructure/cloud-deployment/model-endpoint-deployment.md) — Provides instructions on hosting models as reachable API endpoints on optimized infrastructure. ([source](https://huggingface.co/))
- [Model Deployments](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-deployment/model-deployments.md) — Covers the deployment of models to showcase their specific functional capabilities. ([source](https://huggingface.co/course/chapter1/1))

### Education & Learning Resources

- [Educational Courses](https://awesome-repositories.com/f/education-learning-resources/educational-courses.md) — Provides a structured learning curriculum for implementing and fine-tuning transformer models.
- [Hugging Face Implementations](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education/llm-engineering-guides/transformer-model-tutorials/hugging-face-implementations.md) — Includes tutorials for implementing models and managing assets within the Hugging Face ecosystem.