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

Libraries

How to use aubmindlab/aragpt2-medium with Transformers:

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

pipe = pipeline("text-generation", model="aubmindlab/aragpt2-medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aubmindlab/aragpt2-medium")
model = AutoModelForCausalLM.from_pretrained("aubmindlab/aragpt2-medium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aubmindlab/aragpt2-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aubmindlab/aragpt2-medium"
# 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-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

wissamantoun commited on Jul 31, 2021

Commit

ca727f5

1 Parent(s): bcfac93

Update README.md

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README.md +4 -20

README.md CHANGED Viewed

@@ -13,6 +13,8 @@ widget:
 # Arabic GPT2
 You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
@@ -38,7 +40,7 @@ from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
 from arabert.preprocess import ArabertPreprocessor
-MODEL_NAME='aragpt2-medium'
 arabert_prep = ArabertPreprocessor(model_name=MODEL_NAME)
 text=""
@@ -73,25 +75,7 @@ python create_pretraining_data.py
  Finetuning:
  ```bash
- python3 run_pretraining.py \\
- --input_file="gs://<GS_BUCKET>/pretraining_data/*" \\
- --output_dir="gs://<GS_BUCKET>/pretraining_model/" \\
- --config_file="config/small_hparams.json" \\
- --batch_size=128 \\
- --eval_batch_size=8 \\
- --num_train_steps= \\
- --num_warmup_steps= \\
- --learning_rate= \\
- --save_checkpoints_steps= \\
- --max_seq_length=1024 \\
- --max_eval_steps= \\
- --optimizer="lamb" \\
- --iterations_per_loop=5000 \\
- --keep_checkpoint_max=10 \\
- --use_tpu=True \\
- --tpu_name=<TPU NAME> \\
- --do_train=True \\
- --do_eval=False
  ```
 # Model Sizes

 # Arabic GPT2
+<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
 You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
 from arabert.preprocess import ArabertPreprocessor
+MODEL_NAME='aubmindlab/aragpt2-medium'
 arabert_prep = ArabertPreprocessor(model_name=MODEL_NAME)
 text=""
  Finetuning:
  ```bash
+ python3 run_pretraining.py \\\n --input_file="gs://<GS_BUCKET>/pretraining_data/*" \\\n --output_dir="gs://<GS_BUCKET>/pretraining_model/" \\\n --config_file="config/small_hparams.json" \\\n --batch_size=128 \\\n --eval_batch_size=8 \\\n --num_train_steps= \\\n --num_warmup_steps= \\\n --learning_rate= \\\n --save_checkpoints_steps= \\\n --max_seq_length=1024 \\\n --max_eval_steps= \\\n --optimizer="lamb" \\\n --iterations_per_loop=5000 \\\n --keep_checkpoint_max=10 \\\n --use_tpu=True \\\n --tpu_name=<TPU NAME> \\\n --do_train=True \\\n --do_eval=False
  ```
 # Model Sizes