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LLMLingua-2 Prompt Compression Demo

Dependencies: gradio, llmlingua, python-dotenv

Installation

  • Install Python
  • Create and activate a virtual environment and install the requirements:
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
  • Create a .env file, e.g.:
LLM_ENDPOINT=https://api.openai.com/v1  # Optional. If not provided, only compression will be possible
LLM_TOKEN=token_1234
LLM_LIST=gpt-4o-mini, gpt-3.5-turbo     # Optional. If not provided, a list of models will be fetched from the API
FLAG_PASSWORD=very_secret               # Optional. If not provided, /flagged and /logs endpoints are disabled

Running

source venv/bin/activate
uvicorn src.app:app --host 0.0.0.0 --port 80 --log-level warning

The demo is now reachable under http://localhost

OR run the demo from a docker container:

docker pull ghcr.io/cornzz/llmlingua-demo:main
docker run -d -e LLM_ENDPOINT=https://api.openai.com/v1 -e LLM_TOKEN=token_1234 -e LLM_LIST="gpt-4o-mini, gpt-3.5-turbo" -e FLAG_PASSWORD=very_secret -p 8000:8000 ghcr.io/cornzz/llmlingua-demo:main

The demo is now reachable under http://localhost:8000

Note

If you are not on a linux/amd64 compatible platform, add --platform linux/amd64 to the docker pull command to force download the image. Note that performance will be worse than if you follow the above instructions. MPS is not supported in docker containers.

Development

source venv/bin/activate
uvicorn src.app:app --reload --log-level warning

The demo is now reachable under http://localhost:8000

Inspecting flagged data and logs

Navigate to /flagged or /logs and enter the password set in .env

Caches

  • The compression model is cached in ~/.cache/huggingface, the cache location can be set via HF_HUB_CACHE.
  • The tokenizer vocabulary is cached in the operating systems' temporary file directory and can be set via TIKTOKEN_CACHE_DIR.