AutoGluon

Mitra-v2 Classifier is a tabular foundation model from AutoGluon and Amazon for classification tasks. It is pretrained entirely on synthetic datasets sampled from random classifier priors, including a Hybrid SCM prior, rather than on real-world datasets. The model uses a 12-layer 2D Transformer with attention across both rows and columns and incorporates in-context learning. Its recommended recipe fine-tunes 50 steps on a user's table and bags eight model copies for prediction; it can be run through the mitra-finetune package or used as a Mitra model in AutoGluon Tabular. The repository describes it as the second generation of Mitra, with a longer context, more features, and an improved optimizer than Mitra-v1. The weights are distributed on Hugging Face under the Apache-2.0 license and require a CUDA GPU for the documented fine-tuning and eight-fold bagging recipe.

Mitra-v2 Regressor is a tabular foundation model from AutoGluon for regression tasks. It is pretrained entirely on synthetic datasets sampled from mixed random-regressor priors, including a Hybrid SCM prior, and uses in-context learning with a 12-layer 2D Transformer that attends across rows and columns. Regression is formulated as classification over 1,000 target bins: the model predicts a distribution for each row, whose mean provides the point prediction and whose probabilities can also produce quantiles and probabilistic metrics such as CRPS. Using the accompanying mitra-finetune package, the model is fine-tuned for 50 steps and bagged across eight copies on a CUDA GPU. It accepts tabular features and regression targets and returns point predictions or full predictive distributions. The weights are distributed through Hugging Face under the Apache-2.0 License; the package is required because standard AutoGluon expects the scalar head used by Mitra-v1 rather than Mitra-v2's distributional head.

mitra-finetune is a Python package for fine-tuning and running inference with second-generation Mitra v2 tabular foundation-model checkpoints. It supports classification and regression through the `MitraFinetune` API, including `fit`, `predict`, `predict_proba`, and distributional regression outputs. Each fit performs a 50-step full fine-tuning run through AutoGluon's eight-fold bagging wrapper. The package uses capped, optionally class-balanced in-context support for large tables, narrows wide tables with top-K feature selection or truncated-SVD projection, and uses hierarchical label decomposition for classification problems exceeding the checkpoint's native 10-class head. Regression is represented as classification over 1,000 target bins, with predictions returned as point estimates or predictive distributions. The package requires Python 3.11–3.13, a CUDA GPU, AutoGluon with the Mitra extra, and the TabArena execution wrapper. It uses checkpoints hosted on Hugging Face, including `autogluon/mitra-classifier-2` and `autogluon/mitra-regressor-2`; FlashAttention is optional. The project is distributed from its Hugging Face repository and can be installed from a cloned checkout.

AirLLM is an open-source Python library for running large language model inference on GPUs with limited memory. It reduces GPU memory use by loading and processing one model layer at a time instead of holding the entire model in GPU memory; for sparse mixture-of-experts models, it streams individual experts as needed. The project is designed to run large models without requiring quantization, distillation, or pruning, and provides a pip-installable package with support for multiple model families, CPU inference, macOS, and optional model compression and quantization features.

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for numerical computing and large-scale machine learning. It transforms Python and NumPy-style functions through automatic differentiation with `jax.grad`, automatic batching and vectorization with `jax.vmap`, and just-in-time compilation with `jax.jit`. It uses XLA to compile computations for GPUs, TPUs, and other accelerators. JAX supports compiler-driven multi-device execution: `Mesh`, `PartitionSpec`, and `NamedSharding` declare array placement across device meshes, allowing distributed training code to include automatically managed parallel work such as gradient synchronization. Its compilation tools also include ahead-of-time compilation, portable artifact export with `jax.export`, and conversion to TensorFlow SavedModels through `jax2tf` for serving integrations. The project describes itself as a research project rather than an official Google product.