--- library_name: transformers tags: - mergekit - merge base_model: - arcee-ai/SuperNova-Medius - huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 - allura-org/TQ2.5-14B-Aletheia-v1 - EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 - v000000/Qwen2.5-Lumen-14B model-index: - name: Q2.5-Veltha-14B-0.5 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: 77.96 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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: 50.32 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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: 33.84 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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: 15.77 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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: 14.17 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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: 47.72 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B-0.5 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 della_linear merge method using [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius) as a base. ### Models Merged The following models were included in the merge: * [huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2) * [allura-org/TQ2.5-14B-Aletheia-v1](https://huggingface.co/allura-org/TQ2.5-14B-Aletheia-v1) * [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2) * [v000000/Qwen2.5-Lumen-14B](https://huggingface.co/v000000/Qwen2.5-Lumen-14B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: della_linear dtype: float32 out_dtype: bfloat16 parameters: epsilon: 0.04 lambda: 1.05 normalize: true base_model: arcee-ai/SuperNova-Medius tokenizer_source: arcee-ai/SuperNova-Medius models: - model: arcee-ai/SuperNova-Medius parameters: weight: 10 density: 1 - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 parameters: weight: 7 density: 0.5 - model: v000000/Qwen2.5-Lumen-14B parameters: weight: 7 density: 0.4 - model: allura-org/TQ2.5-14B-Aletheia-v1 parameters: weight: 8 density: 0.4 - model: huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 parameters: weight: 8 density: 0.45 ``` # [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/djuna__Q2.5-Veltha-14B-0.5-details) | Metric |Value| |-------------------|----:| |Avg. |39.96| |IFEval (0-Shot) |77.96| |BBH (3-Shot) |50.32| |MATH Lvl 5 (4-Shot)|33.84| |GPQA (0-shot) |15.77| |MuSR (0-shot) |14.17| |MMLU-PRO (5-shot) |47.72|