Merged [Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) into [FuseChat-7B-Varm](https://huggingface.co/FuseAI/FuseChat-7B-VaRM) to fix the GPTism The idea was to keep FuseChat's smarts since from my testing it was amazing, just a little stubborn for RP Silly tavern [preset](https://huggingface.co/Virt-io/FuseChat-7B-VaRM-GGUF/tree/main/presets) Merge template copied from [TheProfessor](https://huggingface.co/abacusai/TheProfessor-155b) --- base_model: - FuseAI/FuseChat-7B-VaRM - SanjiWatsuki/Kunoichi-DPO-v2-7B library_name: - transformers tags: - mergekit - merge --- # FUSECHAT-VaRM-Kunoichi-10.7b.v1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## 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: * [FuseAI/FuseChat-7B-VaRM](https://huggingface.co/FuseAI/FuseChat-7B-VaRM) * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear # use linear so we can include multiple models, albeit at a zero weight parameters: weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough slices: - sources: - model: FuseAI/FuseChat-7B-VaRM # embed_tokens comes along with the ride with whatever is the first layer layer_range: [0, 1] - model: SanjiWatsuki/Kunoichi-DPO-v2-7B # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens layer_range: [0, 1] parameters: weight: 0 - sources: - model: FuseAI/FuseChat-7B-VaRM layer_range: [1, 5] - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [5, 7] # 2 layers - sources: - model: FuseAI/FuseChat-7B-VaRM layer_range: [5, 15] - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [15, 27] # 12 layers - sources: - model: FuseAI/FuseChat-7B-VaRM layer_range: [15, 27] - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [27, 29] # 2 layers - sources: - model: FuseAI/FuseChat-7B-VaRM layer_range: [27, 31] - sources: # same as above, but for lm_head with the last layer - model: FuseAI/FuseChat-7B-VaRM layer_range: [31, 32] - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [31, 32] parameters: weight: 0 dtype: float16 tokenizer_source: model:FuseAI/FuseChat-7B-VaRM # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice ```