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

Libraries

How to use hilarl/naturecode-mangrove-small with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hilarl/naturecode-mangrove-small", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hilarl/naturecode-mangrove-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hilarl/naturecode-mangrove-small

SGLang

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

Docker Model Runner
How to use hilarl/naturecode-mangrove-small with Docker Model Runner:
```
docker model run hf.co/hilarl/naturecode-mangrove-small
```

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Naturecode Mangrove Small

Expert AI model for mangrove ecosystem knowledge with function calling capabilities.

Model Description

Naturecode Mangrove Small is a specialized language model trained on comprehensive mangrove ecosystem data. It combines deep domain knowledge about mangrove ecology, conservation, and climate science with function calling capabilities, making it ideal for building mangrove monitoring and research applications.

Key Features

Domain Expert: Trained on extensive mangrove ecosystem knowledge
Function Calling: Can invoke tools for data retrieval and analysis
Conservation Focus: Understands mangrove importance for climate and biodiversity
Lightweight: 270M parameters for edge deployment

Usage

General Knowledge

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("google/functiongemma-270m-it")
model = PeftModel.from_pretrained(base_model, "hilarl/naturecode-mangrove-small")
tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")

prompt = "<start_of_turn>user\nWhat is a mangrove ecosystem?<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0]))

Function Calling

prompt = """<start_of_turn>user
[mangrove_api]
{"name": "get_species_info", "description": "Get information about a mangrove species", "parameters": {"species_name": "string"}}

Tell me about Rhizophora mangle<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
# Output: <start_function_call>call:get_species_info{species_name:<escape>Rhizophora mangle<escape>}<end_function_call>

Knowledge Domains

Species Information: Mangrove tree species, characteristics, distribution
Ecosystem Services: Carbon sequestration, coastal protection, biodiversity
Conservation: Threats, restoration techniques, protected areas
Climate Science: Blue carbon, sea level rise adaptation
Monitoring: Remote sensing, field survey methods

Training Details

Base Model: google/functiongemma-270m-it
Training Method: Mixed training (40% function calling, 60% general knowledge)
Training Data: Curated mangrove knowledge base + function calling dataset

Intended Use

Mangrove research assistants
Conservation planning tools
Educational applications about coastal ecosystems
Mangrove monitoring system integration
Climate science applications

Limitations

Focused on mangrove ecosystems; general knowledge is limited
Function calling requires proper tool descriptions
Knowledge based on training data cutoff

License

Apache 2.0

Citation

@misc{naturecode-mangrove-small,
  author = {Naturecode},
  title = {Naturecode Mangrove Small},
  year = {2025},
  publisher = {HuggingFace},
}

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