Instructions to use deepguess/Isobar-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepguess/Isobar-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="deepguess/Isobar-1")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("deepguess/Isobar-1")
model = AutoModelForImageTextToText.from_pretrained("deepguess/Isobar-1")
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

vLLM

How to use deepguess/Isobar-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepguess/Isobar-1"
# 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/Isobar-1",
		"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/Isobar-1

SGLang

How to use deepguess/Isobar-1 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/Isobar-1" \
    --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/Isobar-1",
		"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/Isobar-1" \
        --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/Isobar-1",
		"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/Isobar-1 with Docker Model Runner:
```
docker model run hf.co/deepguess/Isobar-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Isobar-1

Isobar-1 is a weather-specialized vision-language model for radar, sounding, satellite, and forecast-map interpretation.

This release is the merged full model version of a two-stage fine-tune built on top of Qwen/Qwen3.5-27B.

What This Model Is

Base model: Qwen/Qwen3.5-27B
Release family: Isobar
Release: Isobar-1
Model type: merged multimodal causal LM / image-text model
Intended use: weather image interpretation, technical meteorology QA, operational weather analysis assistance

Training Lineage

Isobar-1 was produced by merging a stage-2 adapter into the base model.

Training stages:

Broad weather VLM adaptation on deepguess/weather-vlm
Curated refinement on deepguess/weather-analysis-sft

The stage-2 refinement was trained from the stage-1 weather checkpoint rather than from the raw base model, so this release reflects both stages together.

Intended Use

Good use cases:

Interpreting radar imagery
Interpreting soundings and hodographs
Explaining severe-weather setups
Reading forecast maps and model visualizations
Technical weather question answering grounded in images

Not the target for this release:

General-purpose agent/tool calling
Fully autonomous forecast operations
Non-weather multimodal tasks

A later Isobar-1-Agent style release is the better place for dedicated tool-use behavior.

Limitations

This model can still be wrong on edge cases, ambiguous imagery, and unusual regional setups.
It is tuned for weather analysis, not guaranteed forecast verification skill.
It should not be treated as a standalone warning or life-safety authority.
Image quality, missing context, bad timestamps, and incomplete annotation can still degrade output quality.

Loading

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "deepguess/Isobar-1"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

Attribution

This model is based on Qwen/Qwen3.5-27B.

The release name Isobar-1 is a downstream branding name for this fine-tuned derivative. It is not an official Qwen release.

License

This release is provided under Apache-2.0, consistent with the upstream base model license. See the upstream model card and license terms for the base model as well.

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for deepguess/Isobar-1

Base model

Qwen/Qwen3.5-27B

Finetuned

(300)

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Quantizations

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Isobar-1