# mitra-regressor by Autogluon: Open-Weight Model Source: https://savrn.com/models/mitra-regressor Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve mitra-regressor (76M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [mitra-regressor on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/mitra-regressor/gpus) ## SAVRN's Notes on mitra-regressor Two tenths of a gigabyte is the whole memory budget at 16-bit, weights and working set together. The cheapest listed setup is a single MI300X with 192 GB at $1.85 an hour on-demand, but a 76M-parameter tabular regressor has no business holding a card that size alone; it belongs on shared capacity. Its job is predicting a numeric column from the rest of a table, pre-trained by the publisher on synthetic data drawn from random regressors so it works in context. Apache 2.0 leaves you free to embed it in a commercial product and modify it, with the notices kept. Two checks before committing: the file lists no context length and no reported evaluations, so accuracy gets measured on your own tables; and arXiv:2510.21204 is the paper on how the synthetic priors were mixed. The publisher's route runs through AutoGluon, so plan for that dependency. SAVRN Research, 2026-09-18 ## Model Card By Autogluon, published under apache-2.0, revision 5f277aa8f690. Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. ### Architecture Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. ### Usage To use Mitra regressor, install AutoGluon by running: ``` pip install uv uv pip install autogluon.tabular[mitra] ``` A minimal example showing how to perform inference using the Mitra regressor: [Read the full model card (239 words)](https://savrn.com/models/mitra-regressor/card) ## Identity and Version Repository autogluon/mitra-regressor Publisher Autogluon Task Tabular regression Modality Tabular Library Not stated by the source Parameters 76M parameters Languages Not stated by the source Revision 5f277aa8f69042d39d6ac3612aed18bb9279bd95 First published 2025-06-22 Last updated 2025-11-19 ## Files and Weights 4 files, 302.7 MB in total. The weights are 1 file totalling 302.7 MB in safetensors. Weights1 file · 302.7 MB Configuration1 file · 81 B Documentation1 file · 2.8 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 302.7 MB | d8e75c62af0b | | config.json | Configuration | 81 B | — | | README.md | Documentation | 2.8 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 302.7 MB [Download from Autogluon](https://huggingface.co/autogluon/mitra-regressor) Released by Autogluon through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2510.21204](https://savrn.com/papers/mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 302.7 MB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About mitra-regressor ### How much GPU memory does mitra-regressor need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (76M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run mitra-regressor on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use mitra-regressor commercially? Yes. mitra-regressor is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models Model · Tabular regression ### [stocka3](https://savrn.com/models/stocka3) [BAAR](https://savrn.com/model-publishers/aixk) ~425K 파라미터 4-Layer Dynamic GAU 기반 실전 주식 트레이딩 파운데이션 모델입니다. ATR 연동 동적 배리어 라벨링과 고확신 타점 필터링으로 손익비 1.5:1 이상을 타겟팅합니다. 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