
PyTorch-Ignite
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
The AI community building the future through collaboration on models, datasets, and applications.

Hugging Face is a leading collaboration platform for the machine learning community, offering tools and resources to build, share, and deploy AI models. It provides a central hub for accessing and hosting Git-based models, datasets, and Spaces. The platform’s open-source stack includes Transformers, Diffusers, and Accelerate, designed to optimize ML workflows. Hugging Face allows users to train and deploy models using Inference Endpoints and Spaces, which can be upgraded with custom on-demand hardware. The ecosystem supports diverse modalities like text, image, video, audio, and 3D, enabling users to build comprehensive ML portfolios. Enterprise solutions provide advanced security, access controls, and dedicated support, catering to teams building AI applications.
Hugging Face is a leading collaboration platform for the machine learning community, offering tools and resources to build, share, and deploy AI models.
Explore all tools that specialize in git-based repository. This domain focus ensures Hugging Face delivers optimized results for this specific requirement.
Explore all tools that specialize in using transformers library. This domain focus ensures Hugging Face delivers optimized results for this specific requirement.
Explore all tools that specialize in inference endpoints. This domain focus ensures Hugging Face delivers optimized results for this specific requirement.
Secure production solution to deploy ML models on dedicated and autoscaling infrastructure directly from the HF Hub.
Platform for sharing ML applications and demos with on-demand hardware upgrades.
Collection of state-of-the-art AI models for PyTorch, providing pre-trained models for various NLP tasks.
Access and share datasets for any ML tasks, offering a collaborative environment for dataset management.
Tool for training PyTorch models with multi-GPU, TPU, and mixed-precision support, optimizing training performance.
Enables efficient fine-tuning of large language models (LLMs) by only training a small subset of the model parameters.
Sign up for a Hugging Face account.
Explore the Hugging Face Hub to discover pre-trained models and datasets.
Install the Hugging Face Transformers library using pip.
Load a pre-trained model and tokenizer using the Transformers library.
Fine-tune the model on a custom dataset.
Deploy the model using Inference Endpoints or Spaces.
Integrate the model into an application using the Hugging Face API.
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"Highly regarded for its extensive model library, ease of use, and strong community support."
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High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

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