README_en.md

# Varco Arena
Varco Arena conducts tournaments between models to be compared for each test set command, ranking models accurately at an affordable price. This is more accurate and cost-effective than rating win rates by comparing against reference outputs.

For more information, the followings may help understanding how it works.
* [Paper](https://huggingface.co/papers/2411.01281)
* [Blog Post (KR)](https://ncsoft.github.io/ncresearch/12cc62c1ea0d981971a8923401e8fe6a0f18563d)


## Quickstart
### Running Web Demo locally (streamlit, Recommended!)
```bash
git clone [THIS_REPO]
# install requirements below. we recommend miniforge to manage environment
cd streamlit_app_local
bash run.sh
```
For more details, see `[THIS_REPO]/streamlit_app_local/README.md`

### CLI use
* located at
  * `varco_arena/`
* debug configurations for vscode at
  * `varco_arena/.vscode`
```bash
## gpt-4o-mini as a judge
python main.py -i "./some/dirpath/to/jsonl/files" -o SOME_REL_PATH_TO_CREATE -m tournament -e "gpt-4o-mini"
## vllm-openai served LLM as a judge
python main.py -i "./some/dirpath/to/jsonl/files" -o SOME_REL_PATH_TO_CREATE -e SOME_MODEL_NAME_SERVED -m tournament -u "http://url_to/your/vllm_openai_server:someport"

# dbg lines
## openai api judge dbg
python main.py -i "rsc/inputs_for_dbg/dbg_400_error_inputs/" -o SOME_WANTED_TARGET_DIR -e gpt-4o-mini
## other testing lines
python main.py -i "rsc/inputs_for_dbg/[SOME_DIRECTORY]/" -o SOME_WANTED_TARGET_DIR -e gpt-4o-mini
## dummy judge dbg (checking errors without api requests)
python main.py -i "rsc/inputs_for_dbg/dbg_400_error_inputs/" -o SOME_WANTED_TARGET_DIR -e debug
```

## Requirements
We tested this on `python = 3.11.9` env:  `requirements.txt`
```
openai>=1.17.0
munch
pandas
numpy
tqdm>=4.48.0
plotly
scikit-learn
kaleido
tiktoken>=0.7.0
pyyaml
transformers
streamlit>=1.40.2
openpyxl
fire==0.6.0
git+https://github.com/shobrook/openlimit.git#egg=openlimit # do not install this by pypi

# Linux
uvloop
# Windows
winloop
```

#### Argument
- -i, --input : directory path which contains input jsonlines files (llm outputs)
- -o, --output_dir : directory where results to be put
- -e, --evaluation : judge model specification (e.g. "gpt-4o-2024-05-13", "gpt-4o-mini", \[vllm-served-model-name\])
- -k, --openai_api_key : OpenAI API Key
- -u, --openai_url: URL to openai_styled_llm_server (requested by openai sdk)

#### advanced
- -j, --n_jobs : n jobs to be put to `asyncio.semaphore(n=)`
- -p, --evalprompt : [see the directory](./varco_arena/prompts/*.yaml)
- -lr, --limit_requests : vLLM OpenAI server request limit (default: 7,680)
- -lt, --limit_tokens : vLLM OpenAI server token limit (default: 15,728,640)

#### Input Data Format
[input jsonl guides](./streamlit_app_local/guide_mds/input_jsonls_en.md)


## Contributing & Customizing
#### Do this after git clone and installation
```bash
pip install pre-commit
pre-commit install
```
#### before commit
```bash
bash precommit.sh # black formatter will reformat the codes
```

## FAQ
* I want to apply my custom judge prompt to run Varco Arena
  * [`./varco_arena/prompts/`](./varco_arena/prompts/__init__.py) defines the prompts with `yaml` file and the class objects for those. Edit those as your need.
* I want tailored judge prompts for each line of the test set row (i.e. ~100th row - `prompt1`, 101st~ - `prompt2`)
  * You could see `load_prompt` at the above link receives `promptname` + `task` as a parameters to load the prompt. The function is called at [`./varco_arena/manager.py:async_run`](./varco_arena/manager.py).
* I want more fields for my llm outputs jsonl files for tailored use, i.e. want more fields beyond `instruction`, `source`, `generated`.
  * It's going to get tricky but let me briefly guide you about this.
    * You might have to edit `varco_arena/eval_utils.py`:`async_eval_w_prompt` (this part calls `PROMPT_OBJ.complete_prompt()`)
    * And all the related codes will require revision.

## Special Thanks to (contributors)
- Minho Lee (@Dialogue Model Team, NCSOFT) [github](https://github.com/minolee/)
  - query wrapper
  - rag prompt
- Jumin Oh (@Generation Model Team, NCSOFT)
  - overall prototyping of the system in haste


## Citation
If you found our work helpful, consider citing our paper!
```
@misc{son2024varcoarenatournamentapproach,
      title={Varco Arena: A Tournament Approach to Reference-Free Benchmarking Large Language Models},
      author={Seonil Son and Ju-Min Oh and Heegon Jin and Cheolhun Jang and Jeongbeom Jeong and Kuntae Kim},
      year={2024},
      eprint={2411.01281},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.01281},
}
```