--- base_model: - beomi/Llama-3-KoEn-8B-Instruct-preview - Danielbrdz/Barcenas-Llama3-8b-ORPO - maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 - rombodawg/Llama-3-8B-Instruct-Coder - NousResearch/Meta-Llama-3-8B-Instruct - rombodawg/Llama-3-8B-Base-Coder-v3.5-10k - cognitivecomputations/dolphin-2.9-llama3-8b - asiansoul/Llama-3-Open-Ko-Linear-8B - NousResearch/Meta-Llama-3-8B - aaditya/Llama3-OpenBioLLM-8B library_name: transformers tags: - mergekit - merge --- # Joah-Llama-3-KoEn-8B-Coder-v1 Joah
오늘 부터 서로에게 빛이 되어 줄 여러분의 Merge Model "좋아(Joah)" by AsianSoul ## Merge Details The performance of this merge model doesn't seem to be bad though.-> Just opinion This may not be a model that satisfies you. But if we continue to overcome our shortcomings, Won't we someday find the answer we want? Don't worry even if you don't get the results you want. I'll find the answer for you. ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base. ### Models Merged The following models were included in the merge: * [beomi/Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) * [Danielbrdz/Barcenas-Llama3-8b-ORPO](https://huggingface.co/Danielbrdz/Barcenas-Llama3-8b-ORPO) * [maum-ai/Llama-3-MAAL-8B-Instruct-v0.1](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) * [rombodawg/Llama-3-8B-Instruct-Coder](https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [rombodawg/Llama-3-8B-Base-Coder-v3.5-10k](https://huggingface.co/rombodawg/Llama-3-8B-Base-Coder-v3.5-10k) * [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) * [asiansoul/Llama-3-Open-Ko-Linear-8B](https://huggingface.co/asiansoul/Llama-3-Open-Ko-Linear-8B) * [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NousResearch/Meta-Llama-3-8B # Base model providing a general foundation without specific parameters - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.60 weight: 0.25 - model: beomi/Llama-3-KoEn-8B-Instruct-preview parameters: density: 0.55 weight: 0.15 - model: asiansoul/Llama-3-Open-Ko-Linear-8B parameters: density: 0.55 weight: 0.2 - model: maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 parameters: density: 0.55 weight: 0.1 - model: rombodawg/Llama-3-8B-Instruct-Coder parameters: density: 0.55 weight: 0.1 - model: rombodawg/Llama-3-8B-Base-Coder-v3.5-10k parameters: density: 0.55 weight: 0.1 - model: cognitivecomputations/dolphin-2.9-llama3-8b parameters: density: 0.55 weight: 0.05 - model: Danielbrdz/Barcenas-Llama3-8b-ORPO parameters: density: 0.55 weight: 0.05 - model: aaditya/Llama3-OpenBioLLM-8B parameters: density: 0.55 weight: 0.1 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ```