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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Automation foundation model for tiny devices: 2-bit, 8-29 MB, tool calls, ASR, structured extraction and embeddings on phones, wearables, smart homes, robots, cars and microcontrollers.

openpi holds open-source models and packages for robotics, published by the Physical Intelligence team.

8-27B bf16 (130 tok/s), 262k context, prefix caching. Works with Claude Code, Codex, opencode and pi.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

The models were pre-trained on the ImageNet and ImageNet-21k datasets. We provide the code for fine-tuning the released models in JAX/Flax.

NOTE: this course does not yet exist. It is current being developed by Eureka Labs. Until it is ready I am archiving this repo !!!

CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch.

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

🤗 Datasets is a lightweight library providing two main features:

State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

until forever. Exceptional understanding of French with the flawless accent implies correct exposure to exceptional French and flawless accent. Similarly, great AI Researchers & engineers with excellent problem-skills imply quality knowledge consumed and exposure rich experience.

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).

Make sure to download the checkpoint and place the ckpt-0 directory in checkpoints - see Downloading the weights

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

We recommend running these notebooks on Colab, which provides a hosted runtime with all the dependencies you will need. You can also, run these notebooks locally, either by setting up your own Jupyter environment, or using Colab's instructions for running locally.

A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.

You can also adjust the sweep configuration file if you need to perform a hyperparameter search.

Ivy enables you to convert ML models, tools and libraries between frameworks using ivy.transpile

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

Official implementation of StreamPI, built on openpi , with additional, fully validated support for JAX multi-node distributed training beyond the standard single-node setup, enabling substantially more efficient training without compromising model performance .

Both demos support terminal interaction and an OpenAI-compatible HTTP server.