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Open-weight model · Text classification

koelectra-small-v3-nsmc

by Daekeun Kim daekeun-ml/koelectra-small-v3-nsmc

koelectra-small-v3-nsmc is an open-weight model for text classification from Daekeun Kim, released under MIT License. It has 14M parameters and a 512-token context. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 1.1M downloads a month.

It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.

Parameters14M
Context512
Weights113.1 MB
Licensemit
AccessOpen weights
Monthly Downloads1.1M

Runs On

What it takes to serve koelectra-small-v3-nsmc (14M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 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 Oct 13, 2026.

koelectra-small-v3-nsmc on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on koelectra-small-v3-nsmc

The memory line on this page reads 0.0 GB at every precision, and that is the main thing to know about koelectra-small-v3-nsmc. It is a 14M-parameter text classifier with 12 layers, about 113 MB of float32 weights on disk. The cheapest card our Index lists, one MI300X with 192 GB at $1.85 an hour, is far more than it needs, so in our facilities a model this size never gets a GPU to itself. Inputs top out at 512 tokens, which suits short text, not long documents.

MIT terms permit commercial use, modification and redistribution as long as the copyright and permission notices travel with the files. Before committing, check that the nsmc data it was trained on matches the text you plan to classify. Daekeun Kim wrapped it for the SageMaker Inference Toolkit; in your own facility you write the serving layer yourself.

Model Card

By Daekeun Kim, published under mit, revision d1c0233fc013.

Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)

Usage (Amazon SageMaker inference applicable)

It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.

inference_nsmc.py

Read the full model card (351 words)

Configuration

Architecture
ElectraForSequenceClassification
Context length (tokens)
512
Layers
12
Hidden size
256
Feed-forward size
1,024
Attention heads
4
Vocabulary size
35,000
Stored precision
float32
Model type
electra

Identity and Version

Repository
daekeun-ml/koelectra-small-v3-nsmc
Publisher
Daekeun Kim
Task
Text classification
Modality
Text
Library
transformers
Parameters
14M parameters
Languages
ko
Revision
d1c0233fc013ae8bd879d7b61f2bec69aa3af4c9
First published
2022-03-02
Last updated
2025-03-09

Files and Weights

10 files, 113.9 MB in total. The weights are 3 files totalling 113.1 MB in bin, safetensors.

Weights3 files · 113.1 MB
Configuration2 files · 1.0 KB
Tokenizer3 files · 797.5 KB
Documentation1 file · 5.1 KB
Repository1 file · 1.2 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights56.5 MB 012a5926dcfe
pytorch_model.binWeights56.6 MB 756dde30d2f3
training_args.binWeights1.5 KB 6f8238eebcbe
config.jsonConfiguration914 B —
special_tokens_map.jsonConfiguration112 B —
README.mdDocumentation5.1 KB —
.gitattributesRepository1.2 KB —
tokenizer.jsonTokenizer533.7 KB —
tokenizer_config.jsonTokenizer414 B —
vocab.txtTokenizer263.3 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
113.1 MB
Download from Daekeun Kim

Released by Daekeun Kim through its official repository on Hugging Face. Read the license.

Built From

  • Trained on (disclosed) nsmc

Memory Requirements

PrecisionWeights in memory
As published113.1 MB
16-bit0.0 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About koelectra-small-v3-nsmc

How much GPU memory does koelectra-small-v3-nsmc need?

About 0 GB at 16-bit and 0 GB at 4-bit: the weights (14M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run koelectra-small-v3-nsmc 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 koelectra-small-v3-nsmc commercially?

Yes. koelectra-small-v3-nsmc is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is koelectra-small-v3-nsmc's context length?

512 tokens, from the maximum position embeddings in its published configuration.

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