Instructions to use deepguess/Qwen2.5-VL-7B-Meteorology with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepguess/Qwen2.5-VL-7B-Meteorology with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="deepguess/Qwen2.5-VL-7B-Meteorology")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("deepguess/Qwen2.5-VL-7B-Meteorology")
model = AutoModelForMultimodalLM.from_pretrained("deepguess/Qwen2.5-VL-7B-Meteorology", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepguess/Qwen2.5-VL-7B-Meteorology with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepguess/Qwen2.5-VL-7B-Meteorology"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepguess/Qwen2.5-VL-7B-Meteorology",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/deepguess/Qwen2.5-VL-7B-Meteorology

SGLang

How to use deepguess/Qwen2.5-VL-7B-Meteorology with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deepguess/Qwen2.5-VL-7B-Meteorology" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepguess/Qwen2.5-VL-7B-Meteorology",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deepguess/Qwen2.5-VL-7B-Meteorology" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepguess/Qwen2.5-VL-7B-Meteorology",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use deepguess/Qwen2.5-VL-7B-Meteorology with Docker Model Runner:
```
docker model run hf.co/deepguess/Qwen2.5-VL-7B-Meteorology
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
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

{
  "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

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

@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 for details.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for deepguess/Qwen2.5-VL-7B-Meteorology

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Finetuned

(1164)

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Quantizations

2 models