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

Libraries

How to use TerraSense-CASM/TerraSense with Transformers:

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

pipe = pipeline("image-text-to-text", model="TerraSense-CASM/TerraSense")
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("TerraSense-CASM/TerraSense")
model = AutoModelForImageTextToText.from_pretrained("TerraSense-CASM/TerraSense")
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 TerraSense-CASM/TerraSense with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TerraSense-CASM/TerraSense"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TerraSense-CASM/TerraSense",
		"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/TerraSense-CASM/TerraSense

SGLang

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

🌍 TerraSense-Base

A Multimodal Large Language Model for Remote Sensing.

📖 Documentation

For usage instructions, examples, and detailed documentation, please visit:

👉 GitHub Repository

🚀 Quick Start

from transformers import AutoModelForVision2Seq, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = AutoModelForVision2Seq.from_pretrained(
    "TerraSense-CASM/TerraSense-Base",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained("TerraSense-CASM/TerraSense-Base", trust_remote_code=True)

messages = [{"role": "user", "content": [
    {"type": "image", "image": "path/to/image.jpg"},
    {"type": "text", "text": "Describe this remote sensing image."},
]}]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, _ = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, padding=True, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=512)
print(processor.batch_decode(output, skip_special_tokens=True)[0])

📜 License

Apache 2.0

Downloads last month: 4

Safetensors

Model size

770k params

Tensor type

BF16

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