metadata

title: ML.ENERGY Leaderboard
python_version: '3.9'
app_file: app.py
sdk: gradio
sdk_version: 3.35.2
pinned: true
tags:
  - energy
  - leaderboard

ML.ENERGY Leaderboard

How much energy do LLMs consume?

This README focuses on explaining how to run the benchmark yourself. The actual leaderboard is here: https://ml.energy/leaderboard.

Setup

Model weights

For models that are directly accessible in Hugging Face Hub, you don't need to do anything.
For other models, convert them to Hugging Face format and put them in /data/leaderboard/weights/lmsys/vicuna-13B, for example. The last two path components (e.g., lmsys/vicuna-13B) are taken as the name of the model.

Docker container

$ git clone https://github.com/ml-energy/leaderboard.git
$ cd leaderboard
$ docker build -t ml-energy:latest .
# Replace /data/leaderboard with your data directory.
$ docker run -it \
    --name leaderboard \
    --gpus all \
    -v /data/leaderboard:/data/leaderboard \
    -v $(pwd):/workspace/leaderboard \
    ml-energy:latest bash

Running the benchmark

We run benchmarks using multiple nodes and GPUs using Pegasus. Take a look at pegasus/ for details.

You can still run benchmarks without Pegasus like this:

# Inside the container
$ cd /workspace/leaderboard
$ python scripts/benchmark.py --model-path /data/leaderboard/weights/lmsys/vicuna-13B --input-file sharegpt/sg_90k_part1_html_cleaned_lang_first_sampled.json
$ python scripts/benchmark.py --model-path databricks/dolly-v2-12b --input-file sharegpt/sg_90k_part1_html_cleaned_lang_first_sampled.json