Weather Understanding (VLM) - Answering questions about radar and satellite imagery (RadarQA)
Weather Generation (Sat2Rad) - Generating radar VIL images from satellite IR observations (IR069 + IR107 -> VIL)
Weather Nowcasting - Predicting future radar frames from historical sequences
Multi-Task Weather Processing - Downscaling, interpolation, cross-modal translation on SEVIR data

Thinking / Chain-of-Thought Reasoning: Coming Soon

Files

File	Size	Description
`ema.safetensors`	~55GB	Fine-tuned model weights (EMA)
`ae.safetensors`	~320MB	VAE encoder/decoder
`config.json`	-	Model configuration
`llm_config.json`	-	LLM backbone config
`vit_config.json`	-	Vision encoder config
`tokenizer*`	-	Tokenizer files

Usage

python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Zhouzone/Omni-Weather",
    local_dir="models/Omni-Weather",
    local_dir_use_symlinks=False,
)

See the GitHub repository for full training and inference instructions.

Training Details

Base Model: ByteDance-Seed/BAGEL-7B-MoT
Training Data: SEVIR (sat2rad, multi-task) + RadarQA (understanding)
Training Steps: 20,000
Hardware: 8x GPUs with FSDP

Citation

bibtex

@article{omniweather2025,
  title   = {Omni-Weather: A Unified Multimodal Foundation Model for Weather Understanding and Generation},
  year    = {2025}
}

Acknowledgements

Built upon BAGEL by ByteDance-Seed.

akiwatanabe/Omni-Weather

Omni-Weather

Model Description

Files

Usage

Training Details

Citation

Acknowledgements

akiwatanabe/
Omni-Weather