Instructions to use PixelistStudio/prompt-extend with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PixelistStudio/prompt-extend with Transformers:

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

pipe = pipeline("text-generation", model="PixelistStudio/prompt-extend")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PixelistStudio/prompt-extend")
model = AutoModelForCausalLM.from_pretrained("PixelistStudio/prompt-extend")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PixelistStudio/prompt-extend with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PixelistStudio/prompt-extend

SGLang

How to use PixelistStudio/prompt-extend 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 "PixelistStudio/prompt-extend" \
    --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": "PixelistStudio/prompt-extend",
		"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 "PixelistStudio/prompt-extend" \
        --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": "PixelistStudio/prompt-extend",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PixelistStudio/prompt-extend with Docker Model Runner:
```
docker model run hf.co/PixelistStudio/prompt-extend
```

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Prompt Extend

Text generation model for generating suitable style cues given the main idea for a prompt.

It is a GPT-2 model trained on dataset of stable diffusion prompts.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 128
eval_batch_size: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.7436	1.0	12796	2.5429
2.3292	2.0	25592	2.0711
1.9439	3.0	38388	1.8447
1.7059	4.0	51184	1.7325
1.5775	5.0	63980	1.7110

Framework versions

Transformers 4.24.0
Pytorch 1.13.0+cu117
Datasets 2.7.1
Tokenizers 0.13.2

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prompt-extend

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Prompt Extend

Training hyperparameters

Training results

Framework versions

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