Open-weight model · Tabular classification
DigitalEduTransformers
by SnowFlash383935 SnowFlash383935/DigitalEduTransformers
Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох. Пример вывода: [False, True]
Parameters101M
Context—
Weights403.6 MB
Licensegpl-3.0
AccessOpen weights
Monthly Downloads41
Runs On
What it takes to serve DigitalEduTransformers (101M 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 (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x 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, read Sep 18, 2026.
Model Card
By SnowFlash383935, published under gpl-3.0, revision 50af5254e577.
Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох. Пример вывода: [False, True]
Read SnowFlash383935's full model card
Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох.
Параметры
- Пол
- Имеет ли фото
- Привязан ли телефон
- Количество подписчиков
- Семейное положение
- Главное в жизни
- Главное в людях
Пример
from transformers import AutoModel
model = AutoModel.from_pretrained("SnowFlash383935/DigitalEduTransformers", trust_remote_code=True)
model([
[1, 1, 1, 150, 2, 0, 0],
[0, 1, 1, 294, 0, 0, 0],
])
Пример вывода: [False, True]
Configuration
- Architecture
- FleshkaTabularTransformer
- Stored precision
- float32
- Model type
- transformer
Identity and Version
- Repository
- SnowFlash383935/DigitalEduTransformers
- Publisher
- SnowFlash383935
- Task
- Tabular classification
- Modality
- Tabular
- Library
- transformers
- Parameters
- 101M parameters
- Languages
- Not stated by the source
- Revision
- 50af5254e5779ed0ad63d0a5fa7ab0b82dac5447
- First published
- 2025-02-23
- Last updated
- 2025-05-17
Files and Weights
6 files, 403.6 MB in total. The weights are 1 file totalling 403.6 MB in safetensors.
Weights1 file · 403.6 MB
Configuration3 files · 3.4 KB
Documentation1 file · 781 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 403.6 MB | 88185ed9064a |
| config.json | Configuration | 742 B | — |
| model.py | Configuration | 1.9 KB | — |
| model_config.py | Configuration | 818 B | — |
| README.md | Documentation | 781 B | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- gpl-3.0
- Access
- Open weights, no gate
- Download size
- 403.6 MB
Download from SnowFlash383935
Released by SnowFlash383935 through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 403.6 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About DigitalEduTransformers
How much GPU memory does DigitalEduTransformers need?
About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (101M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run DigitalEduTransformers 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.
What license is DigitalEduTransformers released under?
gpl-3.0, as its publisher declares it. Read the license text before commercial use.
Similar Models
RT-PluRel is a Relational Transformer checkpoint pair for in-context / few-shot entity prediction over multi-table relational databases (no per-task gradient training). Same architecture and file layout as stanford-star/rt-j — drop-in replacement. Architecture: ~85.6M blocks, dmodel 512, 8 heads, dff 2048 · text columns embedded with all-MiniLM-L12-v2 (dtext 384). Each folder contains model.safetensors (weights) and config.json (dims + text-embedding model). The paper/ subdirectory preserves the earlier RT-PluRel release:.pt checkpoints (12 blocks, dmodel 256, dff 1024) pretrained on synthetic relational databases generated by PluRel, plus the continued-pretraining and fine-tuned RelBench…
Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Mitra-v2 classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers, including the new Hybrid SCM prior. It is the second generation of the Mitra classifier (autogluon/mitra-classifier), pre-trained with a 10x longer context, three times as many features, and an improved optimizer. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The regression model is at autogluon/mitra-regressor-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a…
Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Model is avaliable at https://huggingface.co/yzhuang/MetaTree 1. Install metatreelib: 2. Use MetaTree on your datasets to generate a decision tree model We show a complete example of using MetaTree at notebook If you have any questions related to the code or the paper, feel free to reach out to us at [email protected]. If you find our paper and code useful, please cite us