update

src/display/about.py

@@ -57,8 +57,10 @@ LLM_BENCHMARKS_DETAILS = f"""
 To reproduce our results, here is the commands you can run, using [this script](https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard/blob/main/backend-cli.py): python backend-cli.py.
 
 Alternatively, if you're interested in evaluating a specific task with a particular model, you can use the [EleutherAI LLM Evaluation Harness library](https://github.com/EleutherAI/lm-evaluation-harness/) as follows:
-`python main.py --model=hf-causal-experimental --model_args="pretrained=<your_model>,revision=<your_model_revision>"`
-` --tasks=<task_list> --num_fewshot=<n_few_shot> --batch_size=1 --output_path=<output_path>` (Note that you may need to add tasks from [here](https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard/tree/main/src/backend/tasks) to [this folder](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463/lm_eval/tasks))
+`python main.py --model=hf-auto --model_args="pretrained=<your_model>,revision=<your_model_revision>,parallelize=True"`
+` --tasks=<task_list> --num_fewshot=<n_few_shot> --batch_size=1 --output_path=<output_path>` 
+
+Note that the Hallucinations Library includes several tasks definitions that are not included in the Harness library -- you can find them at [this link](https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard/tree/main/src/backend/tasks)).
 
 The total batch size we get for models which fit on one A100 node is 8 (8 GPUs * 1). If you don't use parallelism, adapt your batch size to fit. You can expect results to vary slightly for different batch sizes because of padding.