Instructions to use alakxender/gemma-3-270m-dhivehi-content-gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alakxender/gemma-3-270m-dhivehi-content-gen with Transformers:

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

pipe = pipeline("text-generation", model="alakxender/gemma-3-270m-dhivehi-content-gen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alakxender/gemma-3-270m-dhivehi-content-gen")
model = AutoModelForCausalLM.from_pretrained("alakxender/gemma-3-270m-dhivehi-content-gen", 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 alakxender/gemma-3-270m-dhivehi-content-gen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alakxender/gemma-3-270m-dhivehi-content-gen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alakxender/gemma-3-270m-dhivehi-content-gen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alakxender/gemma-3-270m-dhivehi-content-gen

SGLang

How to use alakxender/gemma-3-270m-dhivehi-content-gen 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 "alakxender/gemma-3-270m-dhivehi-content-gen" \
    --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": "alakxender/gemma-3-270m-dhivehi-content-gen",
		"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 "alakxender/gemma-3-270m-dhivehi-content-gen" \
        --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": "alakxender/gemma-3-270m-dhivehi-content-gen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alakxender/gemma-3-270m-dhivehi-content-gen with Docker Model Runner:
```
docker model run hf.co/alakxender/gemma-3-270m-dhivehi-content-gen
```

Gemma-3-270M Dhivehi — Text Generation Model

Compact Dhivehi (ދިވެހި) text generation model based on google/gemma-3-270m, designed to generate creative and coherent Dhivehi content based on prompts, titles, or instructions.

Note: This model is specifically tuned for text generation tasks and provides natural, flowing Dhivehi text outputs for content creation.

Model details

Base: google/gemma-3-270m-it
Language: Dhivehi
Style: Creative, natural, coherent
Output format: Flowing Dhivehi text
Supported tasks:
- Random Dhivehi text generation

Intended use

To further downstream tasks

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

# Load model
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

# Load model
model_path = "alakxender/gemma-3-270m-dhivehi-text-gen"
model = AutoModelForCausalLM.from_pretrained(
    model_path, 
    torch_dtype="auto", 
    device_map="auto", 
    attn_implementation="eager"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Generate content
title_or_prompt = "ދިވެހިރާއްޖެއަކީ އިންޑިޔާ ކަނޑުގައި އޮންނަ ޖަޒީރާ ޤައުމެކެވެ"

# Create the prompt format used during training
prompt = f"Create a dhivehi article for the following topic: {title_or_prompt}"

# Create chat format message for content generation (matching training format)
messages = [
    {"role": "system", "content": "You are a helpful assistant that can generate dhivehi articles based on a given topic."},
    {"role": "user", "content": prompt}
]

# Apply chat template
formatted_prompt = pipe.tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True
)

# Generation parameters
gen_kwargs = {
    "max_new_tokens": 256,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "do_sample": True,
    "disable_compile": True,
    "pad_token_id": tokenizer.eos_token_id
}

# Generate content
outputs = pipe(formatted_prompt, **gen_kwargs)

# Extract generated content (remove the prompt)
generated_content = outputs[0]['generated_text'][len(formatted_prompt):].strip()

print(f"Generated content: {generated_content}")

Generation Parameters

max_new_tokens: Controls the length of generated text (64-512 recommended)
temperature: Controls randomness (0.1-1.0, higher = more creative)
do_sample: Boolean flag to enable/disable sampling

Limitations

Generated content may not always be factually accurate
Quality depends on the clarity and specificity of input prompts
Context window limitations for very long inputs
The model is specifically trained for text generation tasks in Dhivehi

Downloads last month: 9

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for alakxender/gemma-3-270m-dhivehi-content-gen

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

(1149)

this model

alakxender
/

gemma-3-270m-dhivehi-content-gen