Instructions to use Rzoro/sd-prompt-generator-gpt-neo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rzoro/sd-prompt-generator-gpt-neo with Transformers:

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

pipe = pipeline("text-generation", model="Rzoro/sd-prompt-generator-gpt-neo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rzoro/sd-prompt-generator-gpt-neo")
model = AutoModelForCausalLM.from_pretrained("Rzoro/sd-prompt-generator-gpt-neo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rzoro/sd-prompt-generator-gpt-neo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rzoro/sd-prompt-generator-gpt-neo

SGLang

How to use Rzoro/sd-prompt-generator-gpt-neo 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 "Rzoro/sd-prompt-generator-gpt-neo" \
    --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": "Rzoro/sd-prompt-generator-gpt-neo",
		"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 "Rzoro/sd-prompt-generator-gpt-neo" \
        --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": "Rzoro/sd-prompt-generator-gpt-neo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rzoro/sd-prompt-generator-gpt-neo with Docker Model Runner:
```
docker model run hf.co/Rzoro/sd-prompt-generator-gpt-neo
```

This model is an experimental model by fine tuning 'EleutherAI/gpt-neo-125m' model on Stable Diffusion AI prompts data. The code that was used to train the model is found here This code NB is a reference used from the following Youtube video.

@software{gpt-neo, author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}}, month = mar, year = 2021, note = {{If you use this software, please cite it using these metadata.}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.5297715}, url = {https://doi.org/10.5281/zenodo.5297715} }

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sd-prompt-generator-gpt-neo

Dataset used to train Rzoro/sd-prompt-generator-gpt-neo