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

Libraries

How to use Mostafathy/finetuned_electra_for_arabic_summarization with Transformers:

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

pipe = pipeline("text-generation", model="Mostafathy/finetuned_electra_for_arabic_summarization")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mostafathy/finetuned_electra_for_arabic_summarization")
model = AutoModelForCausalLM.from_pretrained("Mostafathy/finetuned_electra_for_arabic_summarization", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mostafathy/finetuned_electra_for_arabic_summarization with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mostafathy/finetuned_electra_for_arabic_summarization

SGLang

How to use Mostafathy/finetuned_electra_for_arabic_summarization 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 "Mostafathy/finetuned_electra_for_arabic_summarization" \
    --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": "Mostafathy/finetuned_electra_for_arabic_summarization",
		"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 "Mostafathy/finetuned_electra_for_arabic_summarization" \
        --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": "Mostafathy/finetuned_electra_for_arabic_summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

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Check out the documentation for more information.

Trained Electra Model

This is a trained Electra model for text summarization.

Model Details

Model Name: Trained Electra Model
Description: This model was fine-tuned on a dataset for text summarization tasks.
Tags: electra, text summarization, natural language processing

Usage

Load the model using the Hugging Face Transformers library.
Use the model for text summarization tasks.

Additional Information

For more details and usage examples, refer to the Hugging Face Model Hub page.

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Safetensors

Model size

13.5M params

Tensor type

F32