Yet Another New Multimodal Fine-Tuning Recipe 🥧
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Yet Another New Multimodal Fine-Tuning Recipe 🥧
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🧑‍🍳 In this 
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🧑‍🍳 In this 
@HuggingFace
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HuggingFace
 Face Cookbook notebook, we demonstrate how to align a multimodal model (VLM) using Mixed Preference Optimization (MPO) using trl.
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 Face Cookbook notebook, we demonstrate how to align a multimodal model (VLM) using Mixed Preference Optimization (MPO) using trl.
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💡 This recipe is powered by the new MPO support in trl, enabled through a recent upgrade to the DPO trainer!
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💡 This recipe is powered by the new MPO support in trl, enabled through a recent upgrade to the DPO trainer!
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We align the multimodal model using multiple optimization objectives (losses), guided by a preference dataset (chosen vs. rejected multimodal pairs).
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We align the multimodal model using multiple optimization objectives (losses), guided by a preference dataset (chosen vs. rejected multimodal pairs).
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Check it out! ➡️ 
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Check it out! ➡️ 
https://huggingface.co/learn/cookbook/fine_tuning_vlm_mpo
https://huggingface.co/learn/cookbook/fine_tuning_vlm_mpo
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Yet Another New Multimodal Fine-Tuning Recipe 🥧

🧑‍🍳 In this @HuggingFace Face Cookbook notebook, we demonstrate how to align a multimodal model (VLM) using Mixed Preference Optimization (MPO) using trl.

💡 This recipe is powered by the new MPO support in trl, enabled through a recent upgrade to the DPO trainer!

We align the multimodal model using multiple optimization objectives (losses), guided by a preference dataset (chosen vs. rejected multimodal pairs).

Check it out! ➡️ https://huggingface.co/learn/cookbook/fine_tuning_vlm_mpo
🚀
John6666 enpeizhao
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John6666 journey-ai
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Sergio Paniego
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