Instructions to use aubmindlab/aragpt2-mega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aubmindlab/aragpt2-mega with Transformers:

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

pipe = pipeline("text-generation", model="aubmindlab/aragpt2-mega", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aubmindlab/aragpt2-mega", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aubmindlab/aragpt2-mega with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aubmindlab/aragpt2-mega"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aubmindlab/aragpt2-mega",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aubmindlab/aragpt2-mega

SGLang

How to use aubmindlab/aragpt2-mega 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 "aubmindlab/aragpt2-mega" \
    --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": "aubmindlab/aragpt2-mega",
		"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 "aubmindlab/aragpt2-mega" \
        --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": "aubmindlab/aragpt2-mega",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aubmindlab/aragpt2-mega with Docker Model Runner:
```
docker model run hf.co/aubmindlab/aragpt2-mega
```

aragpt2-mega

Commit History

Oct 24, 2024

10:21
Adding `safetensors` variant of this model⁠

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Feb 24, 2024

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Update run_pretraining.py and configuration_aragpt2.py

wissamantoun

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Update README.md and config.json, and add aragpt2-large model to ARAGPT2_PRETRAINED_MODEL_ARCHIVE_LIST

wissamantoun

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Update model configuration and mappings

wissamantoun

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Update config.json with AraGPT2 model and auto mapping

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Oct 30, 2023

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AUB MIND LAB

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Commit History

Oct 24, 2024

Adding `safetensors` variant of this model⁠

Feb 24, 2024

Update run_pretraining.py and configuration_aragpt2.py

Update README.md and config.json, and add aragpt2-large model to ARAGPT2_PRETRAINED_MODEL_ARCHIVE_LIST

Update model configuration and mappings

Update config.json with AraGPT2 model and auto mapping

Oct 30, 2023

Update README.md

Create LICENSE

Nov 15, 2022

Update README.md

Apr 7, 2022

added tensorboard logs

Jul 31, 2021

Update README.md

May 21, 2021

allow flax

Apr 18, 2021

added citation

Jan 13, 2021

Update README.md

Jan 1, 2021

Added model files

initial commit