We’re on a journey to advance and democratize artificial intelligence through open source and open science.

HuggingFace's platform is a resource for developers and researchers working in natural language processing (NLP) and machine learning, offering insights into NLP models, tools, and datasets. Through articles, tutorials, and open-source projects, HuggingFace offers insights into state-of-the-art NLP techniques, transformer architectures, and transfer learning methods. Developers can learn about using pre-trained models, fine-tuning strategies, and deploying NLP applications with HuggingFace's libraries and APIs.

Hugging Face

OpenAI's Codex coding agent can now orchestrate end-to-end machine learning experiments using Hugging Face Skills. The integration enables Codex to fine-tune models, monitor training with Trackio, evaluate checkpoints, generate reports, and deploy models—all through natural language instructions. The tutorial demonstrates fine-tuning a Qwen3-0.6B model on coding problems, with Codex handling dataset validation, hardware selection, job submission, progress tracking, and GGUF conversion for local deployment. The system supports SFT, DPO, and GRPO training methods for models from 0.5B to 7B parameters on Hugging Face's cloud infrastructure.

Codex is Open Sourcing AI models

GOAL: End-to-end Machine Learning experiments