1. VAETKI-VL-7B-A1B Highlights

VAETKI-VL-7B-A1B is a vision-language model developed by the NC-AI, designed especially for inference efficienty. VAETKI series adopt a Mixture-of-Experts (MoE) architecture to effectively balance performance and computational cost.

2. Model Overview

VAETKI-VL-7B-A1B has the following features:

Type: Causal (Auto-regressive) Vision Language Models
Architecture: LLM(VAETKI-VL-7B-A1B) + RICE-ViT + FFN
Developed by: NC-AI consortium (with ETRI, Korea University)
Training Stage: Stage 1(FFN), Stage 2(SFT), Stage 3(MPO)
Number of Parameters: 7.25B in total and 1.2B activated
Number of Paramaters (Non-Embedding): 6.8B
Number of Layers: 24
Number of Attention Heads: 12
Number of Experts: 64
Number of Activated Experts: 5
Context Length: 16k tokens
Vocabulary Size: 126k
Languages: Korean, English
License: MIT
Related URLs: https://github.com/wbl-ncai/VAETKI/

For more details, please refer to our Technical Report - to be updated

3. How to Use

See the Quickstart for more details.

4. Training Details

Training Data

- to be updated

Training Procedure

Hardware
- Platform: Naver Cloud MLX Platform
- GPUs: NVIDIA H100 80GB HBM3 × 64
- Interconnect: InfiniBand 400 Gb/s, 6 lanes (4 lanes were used for RDMA-based inter-node communication)
Software: The model architecture configuration, training loop, checkpointing, and distributed optimization logic were implemented based on Megatron-Core v0.14, with selective modifications to accommodate experimental requirements. for On & Off-policy Reinforcement Learning, was implemented using ms-swift 3.11.1 and verl 0.6.1. The implementation includes internal modifications to the original frameworks for research and optimization purposes, and this model does not claim full compatibility with original upstream implementations.
Hyperparameters - to be updated

5. Evaluation Results

- to be updated

6. Limitations

Limitations: This model may produce inaccurate or incomplete outputs, including hallucinated content, particularly for ambiguous prompts or tasks requiring high factual accuracy. It may have limitations in complex multi-step reasoning, precise mathematical computation, and strict correctness in code generation. The model does not have the ability to independently verify information.
(Potential) Biases: The training data may contain social or cultural biases, which can be reflected in the model’s outputs. Despite mitigation efforts, biases related to gender, ethnicity, nationality, or religion may still occur.
Out-of-Scope Use: This model is not designed for use in safety-critical or regulated domains, such as medical, legal, financial, or military applications. It should not be relied upon for decisions where errors could lead to harm.

7. License

This model repository is licensed under the MIT License. The use of VAETKI models is subject to the Model License.

8. Citation

@misc{ncai2025vaetkitechnicalreport,
      title={VAETKI Technical Report}, 
      author={NC-AI Consortium},
      year={2025},
      eprint={xxxx.xxxxx},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/xxxx.xxxxx}, 
}