Update README.md

README.md

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 **A conversational model, aligned through AI feedback.**
 
-This model is a fine-tuned version of [BramVanroy/GEITje-ultra-sft](https://huggingface.co/BramVanroy/GEITje-ultra-sft) on a synthetic DPO dataset of around 56M tokens that was generated with gpt-4-turbo and [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat) for Dutch.
+This model is a fine-tuned version of [BramVanroy/GEITje-7B-ultra-sft](https://huggingface.co/BramVanroy/GEITje-7B-ultra-sft) on a synthetic DPO dataset of around 56M tokens that was generated with gpt-4-turbo and [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat) for Dutch.
 
 
 ## 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-ultra-sft](https://huggingface.co/BramVanroy/GEITje-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.
+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