Update README.md

README.md

@@ -132,9 +132,11 @@ model.save_pretrained("Meta-Llama-3.1-70B-Instruct-quantized.w4a16")
 
 ## Evaluation
 
-This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, and HumanEval benchmarks.
+This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks.
 In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine.
 Arena-Hard evaluations were conducted using the [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) repository.
+The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4.
+We report below the scores obtained in each judgement and the average.
 OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct).
 This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-70B-Instruct-evals) and a few fixes to OpenLLM v2 tasks.
 HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository.
@@ -144,7 +146,6 @@ Detailed model outputs are available as HuggingFace datasets for [Arena-Hard](ht
 
 ### Accuracy
 
-
 <table>
   <tr>
    <td><strong>Benchmark</strong>