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  <img src="https://i.imgur.com/pbPbqq0.jpeg" width="550"/>
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  An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of this model's responses, especially when used in vision use cases*
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- *(Requires [mistral-7b-mmproj-v1.5-Q4_1](https://huggingface.co/koboldcpp/mmproj/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf?download=true) file in Kobold)
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  ### Notes & Methodology
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  * [Excalibur-7b](https://huggingface.co/InferenceIllusionist/Excalibur-7b) fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs
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  * This is a quick experiment to determine the impact of DPO finetuning on the original base model
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- * Executed for a little over an hour on a single A100
 
 
 
 
 
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  <img src="https://i.imgur.com/pbPbqq0.jpeg" width="550"/>
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  An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of this model's responses, especially when used in vision use cases*
 
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  ### Notes & Methodology
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  * [Excalibur-7b](https://huggingface.co/InferenceIllusionist/Excalibur-7b) fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs
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  * This is a quick experiment to determine the impact of DPO finetuning on the original base model
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+ * Executed for a little over an hour on a single A100
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+ *Requires [mistral-7b-mmproj-v1.5-Q4_1](https://huggingface.co/koboldcpp/mmproj/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf?download=true) file to be loaded in Kobold