Hugging Face

Certified course on building and deploying AI agents.

Integrates timm models with Hugging Face Transformers ecosystem.

Unified frontend layer for multiple AI inference backends.

Multilingual similarity model with static embeddings for faster performance.

Efficient English retrieval model using static embeddings.

Build and use AI agents with ethical grounding.

Advanced AI model using Pythonic function calling framework.

Pythonic function calling framework for AI tool interaction.

Interactive tool for exploring and analyzing datasets on Hugging Face.

Generates domain-specific synthetic datasets for AI model fine-tuning.

A library enabling agentic capabilities for language models.

State-of-the-art encoder-only model with improved performance.

Fine-tuned instruction variant for interactive AI applications.

Fine-tuned variant on synthetic data for AI applications.

Base model for downstream fine-tuning in AI tasks.

A small, efficient vision language model for AI applications.

Optimizes file uploads with byte-level analysis for AI data.

Improves storage efficiency using content-defined chunking for AI models.

Enhances text generation with early-exit self-speculative decoding.

Interactive SQL editor for browser-based data exploration.

Search text columns in datasets instantly and efficiently.

Seamless image assembly with customizable grid layouts.

Integrates Hugging Face models into PyCharm for AI development.

Accelerates LLM inference using any assistant model's tokenizer.

Zero-configuration inference microservices for open model deployment.