Update README.md

README.md

@@ -21,7 +21,7 @@ tags:
 
 Quantized version of [DeepSeek-Coder-V2-Lite-Instruct](deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct).
 <!-- It achieves an average score of 73.19 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.48. -->
-It achieves an average score of 79.60 on the [HumanEval](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark, whereas the unquantized model achieves 79.33.
+It achieves an average score of 79.60 on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark, whereas the unquantized model achieves 79.33.
 
 ### Model Optimizations
 
@@ -100,7 +100,7 @@ model.save_quantized(quantized_model_dir)
 
 ## Evaluation
 
-The model was evaluated on the [HumanEval](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark with the [Neural Magic fork](https://github.com/neuralmagic/evalplus) of the [EvalPlus implementation of HumanEval](https://github.com/evalplus/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+The model was evaluated on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark with the [Neural Magic fork](https://github.com/neuralmagic/evalplus) of the [EvalPlus implementation of HumanEval+](https://github.com/evalplus/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
 ```
 python codegen/generate.py --model neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8 --temperature 0.2 --n_samples 50 --resume --root ~ --dataset humaneval
 python evalplus/sanitize.py ~/humaneval/neuralmagic--DeepSeek-Coder-V2-Lite-Instruct-FP8_vllm_temp_0.2
@@ -109,7 +109,7 @@ evalplus.evaluate --dataset humaneval --samples ~/humaneval/neuralmagic--DeepSee
 
 ### Accuracy
 
-#### Open LLM Leaderboard evaluation scores
+#### HumanEval+ Leaderboard evaluation scores
 <table>
   <tr>
    <td><strong>Benchmark</strong>