---
license: other
license_name: qwen2.5-vl
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/LICENSE
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- vision
- image-text-to-text
- weather
- meteorology
- climate
- qwen2.5-vl
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
---

# Weather Analysis Vision-Language Model (Qwen2.5-VL-7B)

A specialized vision-language model for meteorological image analysis, fine-tuned from Qwen2.5-VL-7B-Instruct.

## Model Details

- **Architecture**: Qwen2.5-VL (Vision-Language Model)
- **Parameters**: 7.6B
- **Base Model**: [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
  - Rank (r): 32
  - Alpha: 32
  - Target modules: ['v_proj', 'down_proj', 'gate_proj', 'k_proj', 'up_proj', 'q_proj', 'o_proj']
- **Training Data**: Specialized weather and meteorological imagery dataset
- **Checkpoint**: checkpoint-7000

## Training Statistics

```json
{
  "global_step": 7000,
  "epoch": 2.911837350180693,
  "total_flos": 4.786937654858951e+18,
  "train_loss": ".751"
}
```

## Image Preprocessing Note

This model was trained with images preprocessed to 448x448 resolution. While Qwen2.5-VL supports dynamic resolution:
- Best performance may be achieved with 448x448 images
- The model will still work well with other resolutions
- Native support for images from 56x56 to 3584x3584


## Quick Start

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from PIL import Image
import torch

# Load model and processor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "qwen25-vl-weather-7b",
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("qwen25-vl-weather-7b")

# Prepare your weather image
image = Image.open("weather_image.jpg")

# Create a prompt
prompt = "Analyze this weather image and describe the meteorological conditions."

# Format the message
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": prompt}
        ]
    }
]

# Process the input
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
    text=[text],
    images=[image],
    padding=True,
    return_tensors="pt"
).to(model.device)

# Generate response
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print(output_text)
```

## Intended Use

This model is designed for:
- **Weather Analysis**: Interpreting meteorological imagery and data
- **Educational Applications**: Teaching weather concepts
- **Research Support**: Assisting in weather data analysis
- **Operational Meteorology**: Supporting weather forecasting workflows

## Capabilities

The model excels at analyzing:
- **Radar Imagery**: Reflectivity, velocity, dual-polarization products
- **Satellite Data**: Visible, infrared, water vapor imagery
- **Surface Charts**: Weather maps, station plots, frontal analysis
- **Upper Air Data**: Soundings, constant pressure charts
- **Model Output**: Forecast charts, ensemble data
- **Observational Data**: Surface observations, meteograms

## Example Prompts

Professional Analysis:
- "Analyze the radar reflectivity patterns and identify any supercell characteristics."
- "What does this water vapor imagery reveal about the jet stream position?"
- "Describe the atmospheric stability based on this sounding."

Educational:
- "Explain this weather pattern in simple terms."
- "What safety precautions should people take given these conditions?"

## Limitations

- Specialized for meteorological imagery; may not perform well on general images
- Best with standard meteorological data formats and visualizations
- Responses reflect training data biases toward certain weather phenomena

## Hardware Requirements

- **Minimum VRAM**: 16GB (with 8-bit quantization)
- **Recommended VRAM**: 24GB+ (for full precision)
- **Optimal Performance**: NVIDIA A100/H100 or RTX 4090/3090

## Citation

```bibtex
@misc{weather-qwen25vl-2025,
  title={Weather Analysis Vision-Language Model based on Qwen2.5-VL-7B},
  author={Deepguess},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/qwen25-vl-weather-7b}
}
```

## Acknowledgments

- Base model: Qwen team for Qwen2.5-VL
- Training framework: Unsloth for efficient fine-tuning
- Dataset: Custom curated weather imagery dataset

## License

This model follows the license terms of Qwen2.5-VL. See the [license file](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/LICENSE) for details.