Model card
K2-Horizon-MoVA-36B-A4B is a high-efficiency Mixture-of-Experts (MoE) model designed to bridge the gap between mid-sized parameter counts and high-performance reasoning. By utilizing a 36B architecture with an active parameter count of approximately 4B, it offers a highly optimized compute-to-performance ratio. For developers, this means you can achieve sophisticated text generation and logical reasoning capabilities without the massive VRAM overhead typically required by dense 30B+ models. It is particularly well-suited for deployment in resource-constrained environments or edge-cloud hybrid setups where low latency and high throughput are critical. Built under the Apache 2.0 license, it provides a flexible foundation for fine-tuning on domain-specific datasets or integrating into RAG (Retrieval-Augmented Generation) pipelines. Compared to standard dense models, the MoE architecture allows for faster inference speeds, making it a strong candidate for real-time conversational agents and automated content workflows.
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IFM/K2-Horizon-MoVA-36B-A4BInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model IFM/K2-Horizon-MoVA-36B-A4BREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model IFM/K2-Horizon-MoVA-36B-A4B README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('IFM/K2-Horizon-MoVA-36B-A4B')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/IFM/K2-Horizon-MoVA-36B-A4B.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/IFM/K2-Horizon-MoVA-36B-A4B.gitHow to use
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Read the model card and source information.
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Review quality, licensing and usage limits.
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