Update README.md

README.md

@@ -14,17 +14,19 @@ tags:
 
 # NeuralBeagle14-7B
 
+**Update 01/16/24: NeuralBeagle14-7B is probably the best 7B model you can find. 🎉**
+
 NeuralBeagle14-7B is a DPO fine-tune of [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) using the [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset and my DPO notebook from [this article](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac).
 
 Thanks [Argilla](https://huggingface.co/argilla) for providing the dataset and the training recipe [here](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp). 💪
 
 ## 🏆 Evaluation
 
-The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite.
+The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. It is the best 7B model to date.
 
 | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
 |---|---:|---:|---:|---:|---:|
-|[**Beagle14-7B**](https://huggingface.co/mlabonne/Beagle14-7B)|  ****|  ****|     ****|   ****|   ****|
+| [**mlabonne/NeuralBeagle14-7B**](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) | **60.25** | **46.06** | **76.77** | **70.32** | **47.86** |
 | [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) [📄](https://gist.github.com/mlabonne/f5a5bf8c0827bbec2f05b97cc62d642c) | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
 | [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B) [📄](https://gist.github.com/mlabonne/cbeb077d1df71cb81c78f742f19f4155) | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
 | [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) [📄](https://gist.github.com/mlabonne/9082c4e59f4d3f3543c5eda3f4807040) | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
@@ -34,6 +36,12 @@ The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/ll
 
 You can find the complete benchmark on [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
 
+It's also on top of the Open LLM Leaderboard:
+
+![](https://i.imgur.com/62gUTFn.png)
+
+Compared to Beagle14, there's no improvement in this benchmark. This might be due to an unlucky run, but I think I might be overexploiting argilla/distilabel-intel-orca-dpo-pairs at this point. Another preference dataset could improve it even further. Note that the Beagle models perform better than Turdus, which is purposely contaminated on Winogrande (very high score).
+
 ## 💻 Usage
 
 ```python