Instructions to use naturecodeproject/mangrove with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use naturecodeproject/mangrove with Transformers:

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

pipe = pipeline("text-generation", model="naturecodeproject/mangrove")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("naturecodeproject/mangrove")
model = AutoModelForCausalLM.from_pretrained("naturecodeproject/mangrove", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use naturecodeproject/mangrove with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "naturecodeproject/mangrove"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "naturecodeproject/mangrove",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naturecodeproject/mangrove

SGLang

How to use naturecodeproject/mangrove 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 "naturecodeproject/mangrove" \
    --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": "naturecodeproject/mangrove",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "naturecodeproject/mangrove" \
        --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": "naturecodeproject/mangrove",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naturecodeproject/mangrove with Docker Model Runner:
```
docker model run hf.co/naturecodeproject/mangrove
```

mangrove / README.md

hilarl

Update org name to naturecodeproject

a975f1e verified 6 months ago

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metadata

license: apache-2.0
language:
  - en
tags:
  - mangrove
  - blue-carbon
  - climate
  - satellite
  - multimodal
  - environmental-ai
library_name: transformers
pipeline_tag: text-generation
extra_gated_prompt: >-
  NatureCode Mangrove Model is designed for mangrove conservation, carbon
  accounting,  and environmental research. Access requires approval to ensure
  responsible use. Please describe your intended use case.
extra_gated_fields:
  Organization: text
  Use case: text
  I agree to use this model for conservation and research purposes: checkbox

NatureCode Mangrove Model

A multimodal foundation model for mangrove ecosystem analysis, carbon estimation, and conservation planning.

Website: naturecode.ai

Capabilities

Satellite Imagery Analysis: Process 19-band multispectral satellite tiles (Sentinel-2, Landsat, Planet)
Environmental Timeseries: Analyze water quality, temperature, salinity data
Carbon Estimation: Calculate above-ground biomass and blue carbon stocks
Conservation Planning: Generate site assessments and restoration recommendations

Quick Start

from naturecode import NatureCodeMangrove

model = NatureCodeMangrove.from_pretrained("naturecodeproject/mangrove")

response = model.generate(
    text="Analyze this mangrove site",
    image="site_tile.npz",
    coordinates=(4.5, 73.5)
)

Supported Input Types

Type	Formats
Satellite	NPZ (19-band), GeoTIFF, NumPy array
Timeseries	CSV, DataFrame, NetCDF
Text	Natural language queries
Coordinates	(lat, lon) tuples

Model Architecture

Base: Qwen3-1.7B with merged LoRA adapters
Vision Encoder: DINOv2-B/14 with learned projector
Timeseries Encoder: Custom transformer for environmental data
Training: 5-phase curriculum on 50K global mangrove tiles

License

Apache 2.0