--- library_name: transformers tags: - mergekit - merge base_model: - djuna/L3-Suze-Vume - Orenguteng/Llama-3.1-8B-Lexi-Uncensored model-index: - name: L3.1-Suze-Vume-calc results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 72.97 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 31.14 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 9.89 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.25 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 8.3 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 27.94 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc name: Open LLM Leaderboard --- # merge 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 [DARE](https://arxiv.org/abs/2311.03099) merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored) as a base. ### Models Merged The following models were included in the merge: * [djuna/L3-Suze-Vume](https://huggingface.co/djuna/L3-Suze-Vume) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: dare_linear models: - model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored parameters: weight: - filter: v_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: o_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: up_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: gate_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: down_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - value: 1 - model: djuna/L3-Suze-Vume parameters: weight: - filter: v_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: o_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: up_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: gate_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: down_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - value: 0 base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored tokenizer_source: base dtype: float32 out_dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_djuna__L3.1-Suze-Vume-calc) | Metric |Value| |-------------------|----:| |Avg. |25.75| |IFEval (0-Shot) |72.97| |BBH (3-Shot) |31.14| |MATH Lvl 5 (4-Shot)| 9.89| |GPQA (0-shot) | 4.25| |MuSR (0-shot) | 8.30| |MMLU-PRO (5-shot) |27.94|