Instructions to use ASTRALK/sentinel-lfm-mining-450m-full-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ASTRALK/sentinel-lfm-mining-450m-full-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ASTRALK/sentinel-lfm-mining-450m-full-v2")
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("ASTRALK/sentinel-lfm-mining-450m-full-v2")
model = AutoModelForMultimodalLM.from_pretrained("ASTRALK/sentinel-lfm-mining-450m-full-v2")
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 ASTRALK/sentinel-lfm-mining-450m-full-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ASTRALK/sentinel-lfm-mining-450m-full-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ASTRALK/sentinel-lfm-mining-450m-full-v2",
		"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/ASTRALK/sentinel-lfm-mining-450m-full-v2

SGLang

How to use ASTRALK/sentinel-lfm-mining-450m-full-v2 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 "ASTRALK/sentinel-lfm-mining-450m-full-v2" \
    --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": "ASTRALK/sentinel-lfm-mining-450m-full-v2",
		"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 "ASTRALK/sentinel-lfm-mining-450m-full-v2" \
        --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": "ASTRALK/sentinel-lfm-mining-450m-full-v2",
		"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 ASTRALK/sentinel-lfm-mining-450m-full-v2 with Docker Model Runner:
```
docker model run hf.co/ASTRALK/sentinel-lfm-mining-450m-full-v2
```

sentinel-lfm — LFM2.5-VL-450M FULL fine-tune v2 (improved recipe)

All-layers fine-tune of LiquidAI/LFM2.5-VL-450M with an improved training recipe over ASTRALK/sentinel-lfm-mining-450m-full: (1) all 1965 train patches (no downsampling) + class-weighted loss, (2) validation-based best-checkpoint selection (balanced accuracy), (3) weight decay, longer schedule. Replies {"mine_present": true|false}.

Results on the held-out test split (419 patches, 303 pos / 116 neg)

model	acc	F1	recall	recall_neg	precision
base (zero-shot)	0.7208	0.8326	0.9604	0.0948	0.7348
LoRA fine-tuned	0.7995	0.8645	0.8845	0.5776	0.8454
FULL fine-tuned (v1)	0.8138	0.8754	0.9043	0.5776	0.8483
FULL fine-tuned v2 (this)	0.8353	0.8900	0.9208	0.6121	0.8611

Training

FULL fine-tune, 448718848 params, class-weighted loss
6 epochs, lr=2e-05 (warmup->cosine), wd=0.01
best checkpoint by val balanced-acc: epoch 1 (bal_acc=0.7713)
bf16 autocast + gradient checkpointing, ~25.8 min on H100

Usage

from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image

model = AutoModelForImageTextToText.from_pretrained("ASTRALK/sentinel-lfm-mining-450m-full-v2", dtype="bfloat16", device_map="auto")
proc = AutoProcessor.from_pretrained("ASTRALK/sentinel-lfm-mining-450m-full-v2")
img = load_image("patch.png")
conv = [{"role":"user","content":[
    {"type":"image","image":img},
    {"type":"text","text":'Does this satellite patch show illegal mining? Reply with only this JSON: {"mine_present": true} or {"mine_present": false}'}]}]
inp = proc.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt", return_dict=True, tokenize=True).to(model.device)
print(proc.decode(model.generate(**inp, max_new_tokens=16)[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))

Downloads last month: 10

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for ASTRALK/sentinel-lfm-mining-450m-full-v2

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Finetuned

(25)

this model