Instructions to use alakxender/dv-articles-sm-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alakxender/dv-articles-sm-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="alakxender/dv-articles-sm-gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alakxender/dv-articles-sm-gpt2")
model = AutoModelForCausalLM.from_pretrained("alakxender/dv-articles-sm-gpt2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alakxender/dv-articles-sm-gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alakxender/dv-articles-sm-gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alakxender/dv-articles-sm-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alakxender/dv-articles-sm-gpt2

SGLang

How to use alakxender/dv-articles-sm-gpt2 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/dv-articles-sm-gpt2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alakxender/dv-articles-sm-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/dv-articles-sm-gpt2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alakxender/dv-articles-sm-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alakxender/dv-articles-sm-gpt2 with Docker Model Runner:
```
docker model run hf.co/alakxender/dv-articles-sm-gpt2
```

Dhivehi GPT-2

A GPT-2 language model trained on Dhivehi text data for text generation.

Model Details

Architecture: GPT-2
Vocab Size: 32,000 tokens
Context Length: 1024 tokens
Embedding Size: 768
Layers: 12
Attention Heads: 12
Total Parameters: ~124M

Training

The model is trained on Dhivehi text data with the following configuration:

Training Epochs: 3
Batch Size: 16 (4 per device with gradient accumulation of 4)
Learning Rate: 5e-4 with cosine decay
Weight Decay: 0.01
Warmup: 10% of training steps
Mixed Precision Training (FP16)
Early Stopping with patience of 3

Usage

def simple_generate(prompt, model_path):
    from grapp import DhivehiGPT2Generator
    generator = DhivehiGPT2Generator(model_path)
    return generator.generate_text(prompt, max_length=200)[0]

# Example usage:
result = simple_generate("ސުރުޚީ: ރާއްޖޭގެ", "alakxender/dv-articles-sm-gpt2")
print(result)

Downloads last month: 1

Safetensors

Model size

13M params

Tensor type

F16

Model tree for alakxender/dv-articles-sm-gpt2

Base model

openai-community/gpt2

Finetuned

(2181)

this model

Collection including alakxender/dv-articles-sm-gpt2

Language

Collection

Dhivehi Natural Language Processing: Text analysis, translation, sentiment analysis, and language generation tools for Thaana • 27 items • Updated Mar 2