Update README.md

README.md

@@ -32,10 +32,6 @@ This model is a fine-tuned version of [BramVanroy/GEITje-7B-ultra-sft](https://h
 > [!TIP]
 > 🚀 Looking for the fast GGUF version? You can find it, and how to use it with `ollama`, [here](https://huggingface.co/BramVanroy/GEITje-7B-ultra-GGUF). 🚀 
 
-## Model description
-
-This is a Dutch instruction/chat model ultimately based on Mistral and aligned with AI feedback via DPO. It is a DPO continuation of the SFT trained [BramVanroy/GEITje-7B-ultra-sft](https://huggingface.co/BramVanroy/GEITje-7B-ultra-sft), which in turn is based on [Rijgersberg/GEITje-7B](https://huggingface.co/Rijgersberg/GEITje-7B), which in turn is based on Mistral 7B and further pretrained on Dutch data. In (rather naive) [benchmarks](https://huggingface.co/spaces/BramVanroy/open_dutch_llm_leaderboard) it outperforms all the original GEITje models on average (but barely). However, note that these benchmarks should be taken with a massive grain of salt (see the disclaimer below the benchmarks on that page). The best evaluation is to try the models and see for yourself.
-
 ## Citation
 
 If you use GEITje 7B Ultra (SFT) or any of its derivatives or quantizations, place cite the following paper:
@@ -52,6 +48,11 @@ If you use GEITje 7B Ultra (SFT) or any of its derivatives or quantizations, pla
 }
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+## Model description
+
+This is a Dutch instruction/chat model ultimately based on Mistral and aligned with AI feedback via DPO. It is a DPO continuation of the SFT trained [BramVanroy/GEITje-7B-ultra-sft](https://huggingface.co/BramVanroy/GEITje-7B-ultra-sft), which in turn is based on [Rijgersberg/GEITje-7B](https://huggingface.co/Rijgersberg/GEITje-7B), which in turn is based on Mistral 7B and further pretrained on Dutch data. In (rather naive) [benchmarks](https://huggingface.co/spaces/BramVanroy/open_dutch_llm_leaderboard) it outperforms all the original GEITje models on average (but barely). However, note that these benchmarks should be taken with a massive grain of salt (see the disclaimer below the benchmarks on that page). The best evaluation is to try the models and see for yourself.
+
+
 ## Usage
 
 One-off: