Instructions to use HamidRezaAttar/gpt2-product-description-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HamidRezaAttar/gpt2-product-description-generator with Transformers:

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

pipe = pipeline("text-generation", model="HamidRezaAttar/gpt2-product-description-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HamidRezaAttar/gpt2-product-description-generator")
model = AutoModelForCausalLM.from_pretrained("HamidRezaAttar/gpt2-product-description-generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HamidRezaAttar/gpt2-product-description-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HamidRezaAttar/gpt2-product-description-generator

SGLang

How to use HamidRezaAttar/gpt2-product-description-generator 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 "HamidRezaAttar/gpt2-product-description-generator" \
    --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": "HamidRezaAttar/gpt2-product-description-generator",
		"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 "HamidRezaAttar/gpt2-product-description-generator" \
        --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": "HamidRezaAttar/gpt2-product-description-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HamidRezaAttar/gpt2-product-description-generator with Docker Model Runner:
```
docker model run hf.co/HamidRezaAttar/gpt2-product-description-generator
```

GPT2-Home

This model is fine-tuned using GPT-2 on amazon home products metadata. It can generate descriptions for your home products by getting a text prompt. https://github.com/HamidRezaAttar/GPT2-Home

Model description

GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.

Live Demo

For testing model with special configuration, please visit Demo

Blog Post

For more detailed information about project development please refer to my blog post.

How to use

For best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my GitHub

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

>>> from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
>>> tokenizer = AutoTokenizer.from_pretrained("HamidRezaAttar/gpt2-product-description-generator")
>>> model = AutoModelForCausalLM.from_pretrained("HamidRezaAttar/gpt2-product-description-generator")
>>> generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100})
>>> generated_text = generator("This bed is very comfortable.")

Citation info

@misc{GPT2-Home,
  author = {HamidReza Fatollah Zadeh Attar},
  title = {GPT2-Home the English home product description generator},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/HamidRezaAttar/GPT2-Home}},
}

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Paper • 1706.03762 • Published Jun 12, 2017 • 123