Update README.md

README.md

@@ -12,10 +12,13 @@ The model was trained on a refined DPO dataset. The objective was to train the m
 We then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. 
 Our hypothesis is that these topics encapsulate the main differences between the two answering styles.
 
-This method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).
+This method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset). The Dataset can be found at [bunkalab/topic_based_chatml_dpo_pairs](https://huggingface.co/datasets/bunkalab/topic_based_chatml_dpo_pairs)
 
 Special thanks to [mlabonne](https://huggingface.co/mlabonne) for creating the [colab notebook](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing#scrollTo=YpdkZsMNylvp) that facilitated the DPO Strategy.
 
+Results of the model can be found here: We do as well as similar models with way less data and computing power :)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c13d74f02ef5b95e0e448e/J8b23NeMhLeqXLQMZwxdh.png)
 
 ## Topic Analysis
 
@@ -121,4 +124,3 @@ print(sequences[0]['generated_text'])
 
 
 You can find the results of the running on Weights & Biases: https://wandb.ai/bunka/huggingface/runs/xq59p47g?workspace=user-charlesdedampierre
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