Instructions to use hilarl/naturecode-dhivehi-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hilarl/naturecode-dhivehi-270m with Transformers:

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

pipe = pipeline("text-generation", model="hilarl/naturecode-dhivehi-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hilarl/naturecode-dhivehi-270m", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hilarl/naturecode-dhivehi-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hilarl/naturecode-dhivehi-270m"
# 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-dhivehi-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hilarl/naturecode-dhivehi-270m

SGLang

How to use hilarl/naturecode-dhivehi-270m 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-dhivehi-270m" \
    --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-dhivehi-270m",
		"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-dhivehi-270m" \
        --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-dhivehi-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hilarl/naturecode-dhivehi-270m with Docker Model Runner:
```
docker model run hf.co/hilarl/naturecode-dhivehi-270m
```

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Naturecode Dhivehi 270m

The first open-source Dhivehi language model optimized for natural conversations.

Model Description

Naturecode Dhivehi 270m is a fine-tuned language model specifically designed for Dhivehi, the official language of the Maldives. Built on Google's FunctionGemma-270m architecture, this model has been trained through a 6-phase curriculum learning approach on authentic Dhivehi text data.

Key Features

Native Dhivehi Support: Trained on authentic Dhivehi text including formal and informal language
Conversational: Optimized for natural dialogue in Dhivehi
Lightweight: 270M parameters - efficient for deployment
Curriculum Trained: Progressive training through 6 phases for robust language understanding

Usage

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-dhivehi-270m")
tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")

prompt = "<start_of_turn>user\nކިހިނެއް؟<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Training Details

Base Model: google/functiongemma-270m-it
Training Method: LoRA fine-tuning with curriculum learning (6 phases)
Training Data: Curated Dhivehi corpus including dictionary entries, formal text, informal conversations, and SFT data

Intended Use

Dhivehi language chatbots and assistants
Dhivehi text generation
Educational applications for Dhivehi language learning
Research on low-resource language models

Limitations

Primarily trained for conversational use; may not excel at technical domains
Performance may vary with highly specialized vocabulary
Should not be used for generating harmful or misleading content

License

Apache 2.0

Citation

@misc{naturecode-dhivehi-270m,
  author = {Naturecode},
  title = {Naturecode Dhivehi 270m},
  year = {2025},
  publisher = {HuggingFace},
}

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