Update README.md

README.md

@@ -116,10 +116,26 @@ model-index:
 
 # BenchmarkEngineering-F2-7B-slerp
 
+This merge seeks to further improve on the original BenchmarkEngineering by integrating the Westlake-7B-v2 model. It does boost the Winogrande score but at the cost of the other benchmarks.
+
 BenchmarkEngineering-F2-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
 * [weezywitasneezy/BenchmarkEngineering-7B-slerp](https://huggingface.co/weezywitasneezy/BenchmarkEngineering-7B-slerp)
 * [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
 
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__BenchmarkEngineering-F2-7B-slerp)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |75.77|
+|AI2 Reasoning Challenge (25-Shot)|73.46|
+|HellaSwag (10-Shot)              |88.88|
+|MMLU (5-Shot)                    |64.50|
+|TruthfulQA (0-shot)              |72.37|
+|Winogrande (5-shot)              |86.11|
+|GSM8k (5-shot)                   |69.29|
+
 ## 🧩 Configuration
 
 ```yaml
@@ -165,16 +181,5 @@ pipeline = transformers.pipeline(
 outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 print(outputs[0]["generated_text"])
 ```
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__BenchmarkEngineering-F2-7B-slerp)
 
-|             Metric              |Value|
-|---------------------------------|----:|
-|Avg.                             |75.77|
-|AI2 Reasoning Challenge (25-Shot)|73.46|
-|HellaSwag (10-Shot)              |88.88|
-|MMLU (5-Shot)                    |64.50|
-|TruthfulQA (0-shot)              |72.37|
-|Winogrande (5-shot)              |86.11|
-|GSM8k (5-shot)                   |69.29|