--- base_model: [] library_name: transformers tags: - mergekit - merge - code license: apache-2.0 --- # Magic-Dolphin-7b A linear merge of: - [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) - [Locutusque/Hyperion-1.5-Mistral-7B](https://huggingface.co/Locutusque/Hyperion-1.5-Mistral-7B) - [ibm/merlinite-7b](https://huggingface.co/ibm/merlinite-7b) These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO. This was my first experiment with merging models so any feedback is greatly appreciated. Uses Alpaca template.

Sample Question ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) * [Locutusque/Hyperion-1.5-Mistral-7B](https://huggingface.co/Locutusque/Hyperion-1.5-Mistral-7B) * [ibm/merlinite-7b](https://huggingface.co/ibm/merlinite-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: models/dolphin-2.6-mistral-7b-dpo-laser parameters: weight: 1.0 - model: models/Hyperion-1.5-Mistral-7B parameters: weight: 0.3 - model: models/merlinite-7b parameters: weight: 0.5 merge_method: linear dtype: float16 ```