BramVanroy
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README.md
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<em>An open and efficient LLM for Dutch</em>
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<blockquote class="tip">
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<p align="center">
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b">π±ββοΈ Base version</a> -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-instruct">π€ Instruct version</a> (this one) -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat">π¬ Chat version</a> -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat-GGUF">π GGUF of instruct model</a>
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</p>
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</blockquote>
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This is the instruct version of Fietje, an SFT-tuned (instruction-tuned) variant of [the base model](https://huggingface.co/BramVanroy/fietje-2b). Fietje is an adapated version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like [GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).
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<em>An open and efficient LLM for Dutch</em>
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</div>
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<blockquote class="tip" style="padding: 1.5em; border: 0">
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<p align="center">
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b">π±ββοΈ Base version</a> -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-instruct">π€ Instruct version</a> (this one) -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat">π¬ Chat version</a> -
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<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat-GGUF">π GGUF of instruct model</a>
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</p>
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<p align="center" style="text-align: center; margin: 0">
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<a href="https://huggingface.co/spaces/BramVanroy/fietje-2b"><strong>Chat with Fietje here!</strong></a>
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</p>
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</blockquote>
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This is the instruct version of Fietje, an SFT-tuned (instruction-tuned) variant of [the base model](https://huggingface.co/BramVanroy/fietje-2b). Fietje is an adapated version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like [GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).
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