Instructions to use praveenseb/product_review_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use praveenseb/product_review_generator with Transformers:

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

pipe = pipeline("text-generation", model="praveenseb/product_review_generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("praveenseb/product_review_generator")
model = AutoModelForCausalLM.from_pretrained("praveenseb/product_review_generator")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use praveenseb/product_review_generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/praveenseb/product_review_generator

SGLang

How to use praveenseb/product_review_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 "praveenseb/product_review_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": "praveenseb/product_review_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 "praveenseb/product_review_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": "praveenseb/product_review_generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use praveenseb/product_review_generator with Docker Model Runner:
```
docker model run hf.co/praveenseb/product_review_generator
```

praveenseb/product_review_generator

This model is a fine-tuned version of distilgpt2 on a sample of amazon_us_reviews dataset. The sample was drawn from 'Apparel_v1_00' subset.

Model description

This model can auto generate review text for apparel products on providing product title, review rating (1-5) and review headline as an input prompt.

The input prompt should be in the format <|BOS|>product_title<|SEP|>product_rating<|SEP|>review_title<|SEP|>. For example, <|BOS|>Columbia Women's Benton Springs Full-Zip Fleece Jacket<|SEP|>5<|SEP|>Awesome jacket!<|SEP|>. You can find the complete code in my GitHub repository.

Intended uses & limitations

This model is only intended to demonstrate the text generation capabilities of transformer-based models. Do not use it commercially or for any real-life purpose . The model is trained specifically on 'Apparel_v1_00' dataset. So, using non-apparel product titles in the input prompt may yield inconsistent results.

Training procedure

Code used for training can found in my GitHub repository.

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 1000, 'decay_rate': 0.95, 'staircase': True, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
training_precision: float32

Training results

Train Loss	Epoch
0.7579	0
0.6720	1

Framework versions

Transformers 4.27.3
TensorFlow 2.11.0
Datasets 2.10.1
Tokenizers 0.13.2

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product_review_generator