Instructions to use monsoon-nlp/dialect-ar-gpt-2021 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monsoon-nlp/dialect-ar-gpt-2021 with Transformers:

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

pipe = pipeline("text-generation", model="monsoon-nlp/dialect-ar-gpt-2021")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/dialect-ar-gpt-2021")
model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/dialect-ar-gpt-2021")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use monsoon-nlp/dialect-ar-gpt-2021 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monsoon-nlp/dialect-ar-gpt-2021"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/dialect-ar-gpt-2021",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monsoon-nlp/dialect-ar-gpt-2021

SGLang

How to use monsoon-nlp/dialect-ar-gpt-2021 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 "monsoon-nlp/dialect-ar-gpt-2021" \
    --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": "monsoon-nlp/dialect-ar-gpt-2021",
		"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 "monsoon-nlp/dialect-ar-gpt-2021" \
        --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": "monsoon-nlp/dialect-ar-gpt-2021",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monsoon-nlp/dialect-ar-gpt-2021 with Docker Model Runner:
```
docker model run hf.co/monsoon-nlp/dialect-ar-gpt-2021
```

monsoon-nlp commited on Feb 1, 2021

Commit

5d73e61

1 Parent(s): 2dfd242

Update README.md

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README.md CHANGED Viewed

@@ -11,11 +11,6 @@ from AUB MIND Lab.
 This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
 and Johns Hopkins University / LREC for 10 epochs.
-Sources
-- https://qspace.qu.edu.qa/handle/10576/15265
-- https://github.com/UBC-NLP/aoc_id
-- https://github.com/ryancotterell/arabic_dialect_annotation
 You can use special tokens to prompt five dialects: `[EGYPTIAN]`, `[GULF]`, `[LEVANTINE]`, `[MAGHREBI]`, or `[MSA]`, followed by a space.
 ```
@@ -34,3 +29,23 @@ Original model: https://huggingface.co/aubmindlab/aragpt2-medium
 I inserted new tokens into the tokenizer, finetuned the model on the dialect samples, and exported the new model.
 Notebook: https://colab.research.google.com/drive/19C0zbkSCt5ncVCa4kY-ik9hSEiJcjI-F

 This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
 and Johns Hopkins University / LREC for 10 epochs.
 You can use special tokens to prompt five dialects: `[EGYPTIAN]`, `[GULF]`, `[LEVANTINE]`, `[MAGHREBI]`, or `[MSA]`, followed by a space.
 ```
 I inserted new tokens into the tokenizer, finetuned the model on the dialect samples, and exported the new model.
 Notebook: https://colab.research.google.com/drive/19C0zbkSCt5ncVCa4kY-ik9hSEiJcjI-F
+## Citations
+AraGPT2 model:
+```
+@misc{antoun2020aragpt2,
+      title={AraGPT2: Pre-Trained Transformer for Arabic Language Generation},
+      author={Wissam Antoun and Fady Baly and Hazem Hajj},
+      year={2020},
+      eprint={2012.15520},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+Dialect data sources:
+- https://qspace.qu.edu.qa/handle/10576/15265
+- https://github.com/UBC-NLP/aoc_id
+- https://github.com/ryancotterell/arabic_dialect_annotation