Mitra-v2 Classifier

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.

AutoGluon is an open-source Python machine-learning library for automated modeling of tabular data, time series, and other data types. It trains end-to-end predictive models by selecting and combining suitable algorithms, including classic machine-learning models, ensembles, and foundation models; its tabular API can fit a predictor from a training file and generate test-set predictions with a few lines of code. The library supports Python 3.10–3.13 on Linux, macOS, and Windows, with optional GPU support, and is licensed under Apache 2.0.

Needle 2 is an open-source, 45-million-parameter foundation model for tool calling, device use, and structured extraction on small devices. Its weights are compressed into a single 14 MB binary and integrated with its inference engine; the repository reports that a full session uses about 28 MB of RAM and performs inference without network access after setup. The model converts text and tool descriptions into schema-constrained JSON calls using a byte-level grammar, attaches a learned confidence score for threshold-based escalation, and can retrieve the top five tools from a larger catalogue. It uses a 256-token sliding window with tools pinned as key-value sinks to bound memory, and its Simple Attention Network combines a Hadamard MLP, grouped-query attention, engram key-value memory, and multi-lane hyper-connections. The Python package supports inference, LoRA fine-tuning, and checkpoint export; the inference engine is fetched from Hugging Face and cached, while the training stack is an optional installation.

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.