Text Generation
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Safetensors
Arabic
gpt2
text-generation-inference
Instructions to use aubmindlab/aragpt2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aubmindlab/aragpt2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aubmindlab/aragpt2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aubmindlab/aragpt2-base") model = AutoModelForCausalLM.from_pretrained("aubmindlab/aragpt2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aubmindlab/aragpt2-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aubmindlab/aragpt2-base" # 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-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aubmindlab/aragpt2-base
- SGLang
How to use aubmindlab/aragpt2-base 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-base" \ --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-base", "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-base" \ --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-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aubmindlab/aragpt2-base with Docker Model Runner:
docker model run hf.co/aubmindlab/aragpt2-base
PEFT LoRA adapter compatibility and redistribution note?
#2
by ISLAM-PO - opened
Hello,
I published a small experimental LoRA adapter on top of aragpt2-base:
https://huggingface.co/ISLAM-PO/MasryGPT-Flash-Adapter
adapter_config.json records the base model, and loading works with:
PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained("aubmindlab/aragpt2-base"), adapter_id).
Two questions:
- Is there a recommended tokenizer revision to pin for adapter compatibility?
- The adapter card reproduces the AraGPT2 license notice. Is there anything else you require for redistribution of adapter-only weights?
I kept the adapter experimental and documented it as non-standalone.
Thanks.
- just pin the latest
- feel free to publish nothing else required