Update README.md

README.md

@@ -30,6 +30,8 @@ It is based on a merge of the following models using [LazyMergekit](https://cola
 
 Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets.
 
+**Try the demo**: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat
+
 ## 🔍 Applications
 
 This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).
@@ -44,7 +46,7 @@ It is one of the very best 7B models in terms of instructing following and reaso
 
 ### Nous
 
-The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
+AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
 
 | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
 |---|---:|---:|---:|---:|---:|
@@ -60,9 +62,9 @@ The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/ll
 
 ### EQ-bench
 
-AlphaMonarch-7B is the second best-performing 7B model on [EQ-bench](https://eqbench.com/) by Samuel J. Peach.
-
+AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations.
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png)
 
 ### MT-Bench
 
@@ -71,32 +73,34 @@ AlphaMonarch-7B is the second best-performing 7B model on [EQ-bench](https://eqb
                                     score
 model                       turn         
 gpt-4                       1     8.95625
-OmniBeagle-7B               1     8.32500 
+OmniBeagle-7B               1     8.31250
 AlphaMonarch-7B             1     8.23750
 claude-v1                   1     8.15000
+NeuralMonarch-7B            1     8.09375
 gpt-3.5-turbo               1     8.07500
 claude-instant-v1           1     7.80000
 
-
 ########## Second turn ##########
                                      score
 model                       turn          
 gpt-4                       2     9.025000
 claude-instant-v1           2     8.012658
+OmniBeagle-7B               2     7.837500
 gpt-3.5-turbo               2     7.812500
 claude-v1                   2     7.650000
 AlphaMonarch-7B             2     7.618750
-OmniBeagle-7B               2     7.587500
+NeuralMonarch-7B            2     7.375000
 
 ########## Average ##########
                                 score
 model                                
 gpt-4                        8.990625
-OmniBeagle-7B                7.956250
+OmniBeagle-7B                8.075000
 gpt-3.5-turbo                7.943750
 AlphaMonarch-7B              7.928125
 claude-instant-v1            7.905660
 claude-v1                    7.900000
+NeuralMonarch-7B             7.734375
 NeuralBeagle14-7B            7.628125
 ```