HyperCLOVAX-SEED-Text-Instruct-0.5B is a Text-to-Text model with instruction-following capabilities that excels in understanding Korean language and culture. Compared to external competitors of similar scale, it demonstrates improved mathematical performance and a substantial enhancement in Korean language capability. The HyperCLOVAX-SEED-Text-Instruct-0.5B is currently the smallest model released by the HyperCLOVAX, representing a lightweight solution suitable for deployment in resource‑constrained environments such as edge devices. It supports a maximum context length of 4K and functions as a versatile small model applicable to a wide range of tasks. The total cost of a single training run for HyperCLOVAX-SEED-Text-Instruct-0.5B was 4.358K A100 GPU hours (approximately USD 6.537K), which is 39 times lower than the cost of training the `QWEN2.5‑0.5B‑instruct` model. The training dataset for HyperCLOVAX-SEED-Text-Instruct-0.5B consists of diverse sources, including the high‑quality data accumulated during the development of HyperCLOVAX-SEED-Text-Instruct-0.5B. Training was conducted in three main stages: 1. **Pretraining**: Knowledge acquisition using high‑quality data and a high‑performance pretrained model. 2. **Rejection Sampling Fine‑Tuning (RFT)**: Enhancement of multi‑domain knowledge and complex reasoning capabilities. 3. **Supervised Fine‑Tuning (SFT)**: Improvement of instruction‑following proficiency. HyperCLOVAX-SEED-Text-Instruct-0.5B leveraged HyperCLOVA X’s lightweight training process and high‑quality data to achieve significantly lower training costs compared to industry‑leading competitors of similar scale. Excluding the SFT stage, a single pretraining run incurred:

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HyperCLOVAX-SEED-Text-Instruct-1.5B

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HyperCLOVAX-SEED-Think-14B

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HyperCLOVA X SEED 14B Think is a next-generation language model that moves beyond the conventional approach of simply increasing model size to improve performance. It combines HyperCLOVA X’s lightweighting technology for building high-efficiency LLMs with advanced reasoning capabilities. Its development relied on two key technologies: (1) Pruning & Knowledge Distillation, which achieves both compactness and high performance, and (2) a Reinforcement Learning (RL) pipeline, which maximizes reasoning ability. By pruning low-importance parameters and distilling knowledge from a large model into a smaller one, training costs have been significantly reduced. On top of this, the latest RL recipe validated in HyperCLOVA X Think is applied in a multi-stage process: (1) Supervised Fine-Tuning (SFT), (2) Reinforcement Learning with Verifiable Rewards (RLVR), (3) Length Controllability (LC) for reasoning path optimization, and (4) a joint training of Reinforcement Learning from Human Feedback (RLHF) and RLVR. It is a considerable challenge to equip a pruned, knowledge-distilled model with reasoning capabilities, since reductions in training costs and model size often degrade reasoning performance. However, through extensive research experience and persistent trial and error, the HyperCLOVA X team has succeeded in lowering training costs while maintaining reasoning performance comparable to that of larger, resource-intensive models. - **Architecture** : Transformer-based architecture with Peri-Layer Normalization and Maximal Update Parameterization(μP) (Dense Model) - **Parameters** : 14.74B - **Input/Output Format** (Input/Output) : Text / Text - **Context Length** : 32k

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HyperCLOVAX-SEED-Think-32B

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HyperCLOVA X SEED 32B Think is an updated vision-language thinking model that advances the SEED Think 14B line beyond simple scaling, pairing a unified vision-language Transformer backbone with a reasoning-centric training recipe. SEED 32B Think processes text tokens and visual patches within a shared embedding space, supports long-context multimodal understanding up to 128K tokens, and provides an optional “thinking mode” for deep, controllable reasoning. Building on the earlier 14B model, SEED 32B Think further strengthens Korean-centric reasoning and agentic capabilities, improving practical reasoning quality and reliability in real-world use. **Features:** - Reasoning mode with ` ... ` output - Multi-turn conversation support - Image/Video understanding

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HyperCLOVAX-SEED-Vision-Instruct-3B

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HyperCLOVAX-SEED-Vision-Instruct-3B is a model developed by NAVER, built upon its proprietary backbone model and fine-tuned through post-training. It is capable of understanding both text and images, as well as generating text. The model is primarily designed with a focus on lightweight architecture, optimizing computational efficiency. In terms of visual understanding, it can handle visual question answering (VQA), chart and diagram interpretation, and even comprehend content. HyperCLOVAX-SEED-Vision-Instruct-3B aims for a Pareto-optimal balance specifically tuned for the Korean language, and it demonstrates competitive performance using fewer visual tokens compared to other models of similar size in inference scenarios. Particularly, the model shows relative strengths in handling Korean-language inputs and outperforms similarly sized open-source models in related benchmarks. As the first open-source vision-language model in Korea capable of visual understanding, it is expected to significantly contribute to strengthening Korea's sovereign AI capabilities.

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