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@@ -107,7 +107,7 @@ pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=30)
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  The model uses a pre-trained [Llama3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) backbone as well as the encoder part of [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo).
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- Only the multi-modal adapter is trained, while Whisper encoder and Llama are kept frozen.
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  We use a knowledge-distillation loss where Ultravox is trying to match the logits of the text-based Llama backbone.
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  The model uses a pre-trained [Llama3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) backbone as well as the encoder part of [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo).
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+ The multi-modal adapter is trained, the Whisper encoder is fine-tuned, and the Llama model is kept frozen.
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  We use a knowledge-distillation loss where Ultravox is trying to match the logits of the text-based Llama backbone.
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