Explore AI Resources

Framework for building LLM-powered applications using composable primitives.

Run LLaMA-class models efficiently on consumer hardware.

Simple local LLM runtime with a clean developer experience.

High-throughput, memory-efficient LLM serving engine.

Unified API interface for 100+ LLM providers.

Constrained generation and structured output for LLMs.

Programming framework for self-improving LLM pipelines.

State-of-the-art pretrained models for NLP, vision, and audio.

Dense vector representations for sentences and paragraphs.

Industrial-strength NLP library for Python.

Classic Python toolkit for symbolic NLP and linguistics.

Structured text generation with regex and JSON schema constraints.

Real-time object detection system.

Open-source implementation of CLIP models.

Meta's foundation model for zero-shot image segmentation.

The world's largest computer vision library.

600+ pretrained vision models for PyTorch.

Vector database for similarity search and RAG.

Managed vector database for AI applications.

Open-source vector database with built-in ML model integration.

The AI-native open-source embedding database.

Cloud-native vector database built for billion-scale similarity search.

Platform for managing the ML lifecycle.

Experiment tracking and model observability.

Data version control for ML projects.

Framework for building and deploying ML services.

Distributed computing framework for AI and Python workloads.

Streamlined fine-tuning of LLMs with PEFT and full-param support.

Parameter-efficient fine-tuning methods by Hugging Face.

Transformer Reinforcement Learning for RLHF and alignment.

Deep learning optimization library for large-scale training.

Multi-agent conversation framework by Microsoft.

Framework for orchestrating role-playing AI agents.

Build stateful, graph-based LLM agent workflows.

Minimal agent library by Hugging Face.

The standard data manipulation library for Python.

Blazing fast DataFrame library written in Rust.

SQL-based transformation tool for analytics engineering.

Evaluation framework for RAG pipelines.

Unified framework for evaluating language models on benchmarks.

Test and evaluate LLM outputs with CI/CD integration.