Awesome

`Mistral-Pro` evaluation harness

Fork of the Eleuther LM Evaluation Harness used in Mistral-Pro-8B-v0.1.

Environment Setup

You can run the following code to set up the environment.

pip install -r requirements.txt

Running the evaluation

See scripts/leaderboard.sh for an entrypoint to running evaluation on a model from the HuggingFace Hub.

The script can be used through the following command:

bash scripts/leaderboard.sh YOUR_MODEL_PATH YOUR_MODEL_NAME

Refer to lm_eval/tasks directory for their associated implementations.

Tasks Supported

Below, we detail all evaluation benchmarks used in the Open-LLM-Leaderboard.

ARC: 25-shot, arc-challenge (acc_norm)
HellaSwag: 10-shot, hellaswag (acc_norm)
TruthfulQA: 0-shot, truthfulqa-mc (mc2)
MMLU: 5-shot (average of all the results acc)
Winogrande: 5-shot, winogrande (acc)
GSM8k: 5-shot, gsm8k (acc)

Main Difference between Open-LLM-Leaderboard

The main difference between the version used by Open-LLM-Leaderboard is the truncation word in GSM8K. The version used by Open-LLM-Leaderboard will be truncated when the output token is ":" as the following figure's red part shows, while this repo adopts the green part to do the truncation. The truncation word of the green part is from the latest version of lm-evaluation-harness.

truncation word

Here is one example that the output will be truncated too early so that we use the latest version of lm-evaluation-harness's truncation word to run the test.

example