Update README.md

README.md

@@ -114,13 +114,11 @@ This model is [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0
 
 The model was fine-tuned using [Rank-Stabilized LoRA](https://huggingface.co/blog/damjan-k/rslora) and the [LongAlpaca-12K](Yukang/LongAlpaca-12k) dataset. I hope to continue extending the context in future versions and then apply the same methods to my [upscaled versions of OpenChat-3.5](https://huggingface.co/collections/Pretergeek/openchat-35-0106-with-additional-layers-66a8d3262c7c3ebdd7783a29) that were created using Block Expansion instead of Depth UP Scaling.
 
-After fine-tuning, the model was tested using passkey retrieval and achieved a score of 100%. ~~Below you can also find the results of the Open LLM Leaderboard evaluations and I am a bit disappointed with those. The model ended up with a significant reduction in performance compared to the original model in all but one test (MUSR). I expected it to do better than the original model on MUSR since that test benefits from long context understanding but I didn't expect such a negative impact on the other tasks. Anyway, I will be addressing this on a future version.~~ **I have been running the same benchmarks from the Open LLM Leaderboard locally, using the code from their own github repo, and so far the results below are lower than the ones I am getting and the model seems to performs very close to the original.** I used the LongAlpaca-12K dataset because it is small and I have limited computational resources but I might have to try a larger dataset for the next attempt. If you would like to help me, there are links on the top of the model card for my Patreon and Ko-Fi.
+After fine-tuning, the model was tested using passkey retrieval and achieved a score of 100%. Below you can also find the results of the Open LLM Leaderboard evaluations and I am a bit disappointed with those. The model ended up with a significant reduction in performance compared to the original model in all but one test (MUSR). I expected it to do better than the original model on MUSR since that test benefits from long context understanding but I didn't expect such a negative impact on the other tasks. Anyway, I will be addressing this on a future version, probably by using a pre-training dataset instead of a fine-tuning dataset so that upstream task are less affected. I used the LongAlpaca-12K dataset because it is small and I have limited computational resources but I might have to try a larger dataset for the next attempt. If you would like to help me, there are links on the top of the model card for my Patreon and Ko-Fi.
 
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Pretergeek__OpenChat-3.5-0106_32K-PoSE)
 
-**These may or may not be correct, local benchmarks show performance close or identical to the original model. I will be redoing them locally following the same setup from the leaderboard. I would appreciate if someone else interested would also test and give me feedback.**
-
 |      Metric       |Value|
 |-------------------|----:|
 |Avg.               |12.70|