Instructions to use flax-community/gpt2-medium-persian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flax-community/gpt2-medium-persian with Transformers:

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

pipe = pipeline("text-generation", model="flax-community/gpt2-medium-persian")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt2-medium-persian")
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt2-medium-persian")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use flax-community/gpt2-medium-persian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flax-community/gpt2-medium-persian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flax-community/gpt2-medium-persian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/flax-community/gpt2-medium-persian

SGLang

How to use flax-community/gpt2-medium-persian 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 "flax-community/gpt2-medium-persian" \
    --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": "flax-community/gpt2-medium-persian",
		"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 "flax-community/gpt2-medium-persian" \
        --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": "flax-community/gpt2-medium-persian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flax-community/gpt2-medium-persian with Docker Model Runner:
```
docker model run hf.co/flax-community/gpt2-medium-persian
```

GPT2 Medium 4 Persian

This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.

Team Members

Dataset

We used Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus.

How To Use

You can use this model directly with a pipeline for text generation.

from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('flax-community/gpt2-medium-persian')
model = GPT2LMHeadModel.from_pretrained('flax-community/gpt2-medium-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100})
generated_text = generator('در یک اتفاق شگفت انگیز، پژوهشگران')

For using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.

Demo

... SOON

Evaluation