# huggingface/notebooks

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4,468 stars · 1,800 forks · Jupyter Notebook · apache-2.0

## Links

- GitHub: https://github.com/huggingface/notebooks
- awesome-repositories: https://awesome-repositories.com/repository/huggingface-notebooks.md

## Description

This is a collection of Jupyter notebooks that serve as educational guides for training, fine-tuning, and deploying machine learning models within the Hugging Face ecosystem. The notebooks cover the full lifecycle of model development, from loading and configuring pre-trained transformers to packaging trained models for real-time inference via scalable endpoints.

The notebooks demonstrate a range of capabilities including diffusion model training and fine-tuning for image generation and editing, transformer model adaptation for natural language processing tasks, and parameter-efficient fine-tuning techniques that reduce computational cost. They also cover multi-GPU training orchestration, hardware accelerator utilisation, and the deployment of models as production inference endpoints.

Beyond core training workflows, the collection includes guides for image generation tasks such as text-to-image synthesis, inpainting, super-resolution, and instruction-based editing. Additional notebooks cover robot policy training from demonstration data and long-form question answering systems using retrieval-augmented approaches. The repository also provides tooling for converting static documentation into executable notebooks for interactive learning.

## Tags

### Part of an Awesome List

- [Tutorials and Notebooks](https://awesome-repositories.com/f/awesome-lists/learning/tutorials-and-notebooks.md) — Organises executable code examples and explanations into self-contained Jupyter notebooks for interactive learning.
- [Model Serving & Deployment](https://awesome-repositories.com/f/awesome-lists/ai/model-serving-deployment.md) — Ships guides for packaging trained models and deploying them as scalable inference endpoints. ([source](https://github.com/huggingface/notebooks/blob/main/README.md))
- [Pretrained Checkpoint Fine-Tuning](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/pretrained-checkpoint-fine-tuning.md) — Provides notebooks for adjusting pre-existing models on custom datasets by modifying training configurations. ([source](https://github.com/huggingface/notebooks/blob/main/README.md))
- [Transformer Training Walkthroughs](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/transformer-training-walkthroughs.md) — Provides walkthrough notebooks for loading datasets, configuring models, and running training loops for NLP tasks. ([source](https://github.com/huggingface/notebooks/blob/main/README.md))

### Artificial Intelligence & ML

- [Diffusion Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/diffusion-models/diffusion-model-training.md) — Provides notebooks that teach training and fine-tuning of diffusion models for image generation and editing.
- [Text-to-Image Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-image-generators.md) — Produces visual content from textual descriptions using diffusion models for creative and guided image synthesis. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Large Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-fine-tuning.md) — Demonstrates adapting large language models to custom datasets using parameter-efficient techniques.
- [Model Inference and Serving](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving.md) — Provides notebooks demonstrating how to load trained models and run inference in production-like settings. ([source](https://github.com/huggingface/notebooks#readme))
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Demonstrates adapting large language models by updating only a small subset of parameters to reduce computational cost.
- [Image Inpainting](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-architectures/image-inpainting.md) — Fills in missing or selected parts of an image with new content that blends into the surrounding area. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Distributed Deep Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-deep-learning.md) — Runs distributed training across multiple GPUs and optimizes compute costs with spot instances for large-scale experiments.
- [Image Variation and Mixing](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/image-editing/image-variation-and-mixing.md) — Creates new image variants from a source image, using text prompts to guide the changes. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Text-Instruction Editors](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/image-editing/text-instruction-editors.md) — Modifies existing images by giving natural language instructions to change content or style. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Image Super Resolution Models](https://awesome-repositories.com/f/artificial-intelligence-ml/image-super-resolution-models.md) — Increases the resolution of input images to produce higher-quality versions with more detail. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Concept-Specific Adaptations](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-fine-tuning/partial-layer-fine-tunings/lora-fine-tuning-pipelines/diffusion-model-adaptations/concept-specific-adaptations.md) — Includes notebooks for adapting pre-trained diffusion models to specific concepts using a small set of example images. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))
- [Hardware Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/hardware-acceleration.md) — Demonstrates leveraging GPUs and hardware accelerators to speed up model training and inference.
- [Multi-GPU Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-gpu-training-utilities.md) — Covers multi-GPU training orchestration with distributed strategies and spot instance optimisation.
- [Structural Image Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/structural-image-generation.md) — Generates images guided by structural inputs such as depth maps or edge detections. ([source](https://github.com/huggingface/notebooks/tree/main/diffusers))

### DevOps & Infrastructure

- [Transformers Tutorials](https://awesome-repositories.com/f/devops-infrastructure/model-conversion/hugging-face/transformers-tutorials.md) — Ships guided notebooks for implementing transformer models for NLP tasks like question answering and text generation.
- [Model Endpoint Deployments](https://awesome-repositories.com/f/devops-infrastructure/serverless-deployment/model-endpoint-deployments.md) — Packages trained models into scalable inference endpoints for real-time predictions via REST APIs.

### Education & Learning Resources

- [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) — Provides guided notebooks for implementing transformer models for NLP tasks using the Hugging Face ecosystem.
- [Machine Learning Tutorials](https://awesome-repositories.com/f/education-learning-resources/machine-learning-tutorials.md) — Provides executable Jupyter notebooks demonstrating how to train, fine-tune, and deploy ML models using the Hugging Face ecosystem.