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---
title: "ML.ENERGY Leaderboard"
emoji: "⚡"
python_version: "3.9"
app_file: "app.py"
sdk: "gradio"
sdk_version: "3.39.0"
pinned: true
tags: ["energy", "leaderboard"]
---
# ML.ENERGY Leaderboard
[](https://ml.energy/leaderboard)
[](https://github.com/ml-energy/leaderboard/actions/workflows/push_spaces.yaml)
[](/LICENSE)
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.
## Colosseum
We instrumented [Hugging Face TGI](https://github.com/huggingface/text-generation-inference) so that it measures and returns GPU energy consumption.
Then, our [controller](/spitfight/colosseum/controller) server receives user prompts from the [Gradio app](/app.py), selects two models randomly, and streams model responses back with energy consumption.
## Setup for benchmarking
### 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
We have our pre-built Docker image published with the tag `mlenergy/leaderboard:latest` ([Dockerfile](/Dockerfile)).
```console
$ docker run -it \
--name leaderboard0 \
--gpus '"device=0"' \
-v /path/to/your/data/dir:/data/leaderboard \
-v $(pwd):/workspace/leaderboard \
mlenergy/leaderboard:latest bash
```
The container internally expects weights to be inside `/data/leaderboard/weights` (e.g., `/data/leaderboard/weights/lmsys/vicuna-7B`), and sets the Hugging Face cache directory to `/data/leaderboard/hfcache`.
If needed, the repository should be mounted to `/workspace/leaderboard` to override the copy of the repository inside the container.
## Running the benchmark
We run benchmarks using multiple nodes and GPUs using [Pegasus](https://github.com/jaywonchung/pegasus). Take a look at [`pegasus/`](/pegasus) for details.
You can still run benchmarks without Pegasus like this:
```console
$ docker exec leaderboard0 python scripts/benchmark.py --model-path /data/leaderboard/weights/lmsys/vicuna-13B --input-file sharegpt/sg_90k_part1_html_cleaned_lang_first_sampled_sorted.json
$ docker exec leaderboard0 python scripts/benchmark.py --model-path databricks/dolly-v2-12b --input-file sharegpt/sg_90k_part1_html_cleaned_lang_first_sampled_sorted.json
```
## Citation
Please refer to our BibTeX file: [`citation.bib`](/docs/citation.bib).
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