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## Training Notes
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Trained [Qwen2.5-14B-Instruct] for 2 epochs on [jondurbin/gutenberg-dpo-v0.1] saving different checkpoints along the way.
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[Tanliboy](https://huggingface.co/tanliboy) trained [Qwen2.5-14B-Instruct] for 1 epoch on [HuggingFaceH4/ultrafeedback_binarized]
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*Mass checkpoint merged, Based on Qwen2.5-14B-Instruct.*
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## Merge
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* Merged with a sophosympatheia <b>SLERP</b> *Ultrafeedback-Binarized DPO* and *Gutenberg DPO*
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* Merged
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## Recipe
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## Training Notes
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Trained [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) for 2 epochs on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1), saving different checkpoints along the way.
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[Tanliboy](https://huggingface.co/tanliboy) trained [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) for 1 epoch on [HuggingFaceH4/ultrafeedback_binarized](HuggingFaceH4/ultrafeedback_binarized), (Credit to Tanliboy!)
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*Mass checkpoint merged, Based on Qwen2.5-14B-Instruct (Base Model).*
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## Merge
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* Merged with a sophosympatheia's <b>SLERP</b> gradient *"Ultrafeedback-Binarized DPO"* and *"Gutenberg DPO"*
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* Merged with a sophosympatheia's <b>SLERP</b> gradient *"Qwen2.5-14B-Instruct"* and *"Gutenberg DPO"*
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* Merged all <b>DPO checkpoints</b> and <b>SLERP</b> variations with <b>MODEL_STOCK</b> to analyze geometric properties and get the most performant aspects of all runs/merges.
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## Recipe
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