Model Detail
EXAONE-4.5-33B-GGUF
—EXAONE-4.5-33B-GGUF is a multimodal model with 33B parameters released by LGAI-EXAONE. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under a other license.
EXAONE-4.5-33B-GGUF ships with 33B parameters, distributed as a quantized weight variant for lower-VRAM inference. Distribution is governed by the other license — review the exact terms before commercial deployment.
EXAONE-4.5-33B-GGUF is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting
arXiv:2609.30880v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has properties that such corpora rarely contain: Short histories, frequent zeros, censoring by stock-out
EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
arXiv:2609.04239v2 Announce Type: replace Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. While recent TSFMs achieve strong zero-shot performance through large-scale pretraining,
EXAONE Forecast for Finance
arXiv:2609.04239v1 Announce Type: new Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series (TS) foundation model (TSFM) tailored to financial forecasting. Recent TSFMs achieve strong zero-shot performance through large-scale pretraining. Howe
EXAONE Tabular 1.0 : Technical Report
arXiv:2608.25774v1 Announce Type: new Abstract: EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dataset-specific gradient updates. Pretrained exclusively on a synthetic structural-causal-model (SCM)
EXAONE 4.5 Technical Report
arXiv:2604.08644v1 Announce Type: new Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal
