Update README.md

README.md

@@ -19,24 +19,77 @@ using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datas
 Chocolatine is a general model and can itself be finetuned to be specialized for specific use cases.  
 Window context = 4k tokens  
 
-![image/jpeg](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/chocolatine_visuel_500x500.png?raw=true)  
-
-### Evaluation
+### Benchmarks
 
 Submitted on [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard), results in few days !  
+First version Chocolatine-3B-Instruct-DPO-v1.0 is already one of the best-performing 3B models on the Open LLM Leaderboard  
 
-### Evaluation MT-Bench in French
+### MT-Bench-French
 
 Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french) by Bofeng Huang,  
-used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)
+used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)  
 
-![image/jpeg](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/results_fr_mt_bench_400x900.png?raw=false)
+```
+########## First turn ##########
+                                           score
+model                               turn        
+gpt-3.5-turbo                       1     8.1375
+Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
+Daredevil-8B                        1     7.8875
+Daredevil-8B-abliterated            1     7.8375
+Chocolatine-3B-Instruct-DPO-v1.0    1     7.6875
+NeuralDaredevil-8B-abliterated      1     7.6250
+Phi-3-mini-4k-instruct              1     7.2125
+Meta-Llama-3-8B-Instruct            1     7.1625
+vigostral-7b-chat                   1     6.7875
+Mistral-7B-Instruct-v0.3            1     6.7500
+Mistral-7B-Instruct-v0.2            1     6.2875
+French-Alpaca-7B-Instruct_beta      1     5.6875
+vigogne-2-7b-chat                   1     5.6625
+vigogne-2-7b-instruct               1     5.1375
+
+########## Second turn ##########
+                                             score
+model                               turn          
+Chocolatine-3B-Instruct-DPO-Revised 2     7.937500
+gpt-3.5-turbo                       2     7.679167
+Chocolatine-3B-Instruct-DPO-v1.0    2     7.612500
+NeuralDaredevil-8B-abliterated      2     7.125000
+Daredevil-8B                        2     7.087500
+Daredevil-8B-abliterated            2     6.873418
+Meta-Llama-3-8B-Instruct            2     6.800000
+Mistral-7B-Instruct-v0.2            2     6.512500
+Mistral-7B-Instruct-v0.3            2     6.500000
+Phi-3-mini-4k-instruct              2     6.487500
+vigostral-7b-chat                   2     6.162500
+French-Alpaca-7B-Instruct_beta      2     5.487395
+vigogne-2-7b-chat                   2     2.775000
+vigogne-2-7b-instruct               2     2.240506
+
+########## Average ##########
+                                        score
+model                                        
+Chocolatine-3B-Instruct-DPO-Revised  7.962500
+gpt-3.5-turbo                        7.908333
+Chocolatine-3B-Instruct-DPO-v1.0     7.650000
+Daredevil-8B                         7.487500
+NeuralDaredevil-8B-abliterated       7.375000
+Daredevil-8B-abliterated             7.358491
+Meta-Llama-3-8B-Instruct             6.981250
+Phi-3-mini-4k-instruct               6.850000
+Mistral-7B-Instruct-v0.3             6.625000
+vigostral-7b-chat                    6.475000
+Mistral-7B-Instruct-v0.2             6.400000
+French-Alpaca-7B-Instruct_beta       5.587866
+vigogne-2-7b-chat                    4.218750
+vigogne-2-7b-instruct                3.698113
+```
 
 ### Usage
 
 You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_3B_inference_test_colab.ipynb) 
 
-You can also run this model using the following code:
+You can also run Chocolatine using the following code:
 
 ```python
 import transformers
@@ -71,8 +124,8 @@ print(sequences[0]['generated_text'])
 
 ### Limitations
 
-Chocolatine is a quick demonstration that a base 3B model can be easily fine-tuned to specialize in a particular language.  
-It does not have any moderation mechanisms.
+The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.  
+It does not have any moderation mechanism.  
 
 - **Developed by:** Jonathan Pacifico, 2024
 - **Model type:** LLM