How to use from
SGLangUse 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 "saltacc/RandomPrompt-v1" \
--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": "saltacc/RandomPrompt-v1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
RandomPrompt-v1
A fine tuned GPT-neo 125M
The purpose of this model is to autocomplete or generate danbooru-like prompts for generating images in Stable Diffusion derivatives that use danbooru tags for text conditioning.
Usage
THE HOSTED INTERFACE DOES NOT WORK, USE THE HUGGINGFACE SPACE
Autocompletion
Type in a few tags, and it will generate a completion of the prompt
Generation
Type in nothing, and it will generate a prompt
Training
Trained on 400k tags from danbooru posts for 600k steps, or around 0.25 epochs
https://wandb.ai/saltacc/RandomPrompt/runs/2v2arf0u?workspace=user-saltacc
I plan on doing further runs on better hardware to try to get more accurate prompt completion
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "saltacc/RandomPrompt-v1" \ --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": "saltacc/RandomPrompt-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'