huggingface/notebooks

Features

• Tutorials and Notebooks - Organises executable code examples and explanations into self-contained Jupyter notebooks for interactive learning.
• Diffusion Model Training - Provides notebooks that teach training and fine-tuning of diffusion models for image generation and editing.
• Text-to-Image Generators - Produces visual content from textual descriptions using diffusion models for creative and guided image synthesis.
• Large Language Model Fine-Tuning - Demonstrates adapting large language models to custom datasets using parameter-efficient techniques.
• Model Inference and Serving - Provides notebooks demonstrating how to load trained models and run inference in production-like settings.
• Parameter Efficient Fine-Tuning - Demonstrates adapting large language models by updating only a small subset of parameters to reduce computational cost.
• Model Serving & Deployment - Ships guides for packaging trained models and deploying them as scalable inference endpoints.
• Pretrained Checkpoint Fine-Tuning - Provides notebooks for adjusting pre-existing models on custom datasets by modifying training configurations.
• Transformers Tutorials - Ships guided notebooks for implementing transformer models for NLP tasks like question answering and text generation.
• Hugging Face Implementations - Provides guided notebooks for implementing transformer models for NLP tasks using the Hugging Face ecosystem.
• Machine Learning Tutorials - Provides executable Jupyter notebooks demonstrating how to train, fine-tune, and deploy ML models using the Hugging Face ecosystem.
• Image Inpainting - Fills in missing or selected parts of an image with new content that blends into the surrounding area.
• Distributed Deep Learning - Runs distributed training across multiple GPUs and optimizes compute costs with spot instances for large-scale experiments.
• Image Variation and Mixing - Creates new image variants from a source image, using text prompts to guide the changes.
• Text-Instruction Editors - Modifies existing images by giving natural language instructions to change content or style.
• Image Super Resolution Models - Increases the resolution of input images to produce higher-quality versions with more detail.
• Concept-Specific Adaptations - Includes notebooks for adapting pre-trained diffusion models to specific concepts using a small set of example images.
• Hardware Acceleration - Demonstrates leveraging GPUs and hardware accelerators to speed up model training and inference.
• Multi-GPU Training Utilities - Covers multi-GPU training orchestration with distributed strategies and spot instance optimisation.
• Structural Image Generation - Generates images guided by structural inputs such as depth maps or edge detections.
• Transformer Training Walkthroughs - Provides walkthrough notebooks for loading datasets, configuring models, and running training loops for NLP tasks.
• Model Endpoint Deployments - Packages trained models into scalable inference endpoints for real-time predictions via REST APIs.

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