--- license: llama3 library_name: transformers tags: - mergekit - merge base_model: - failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 model-index: - name: AbL3In-15B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 61.77 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 78.42 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.57 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.53 source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 74.74 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B name: Open LLM Leaderboard --- # merge This is a testing model using the zeroing method used by [elinas/Llama-3-15B-Instruct-zeroed](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed). If this model pans out in the way I hope, Ill heal it then reupload with a custom model card like the others. currently this is just an experiment. In case anyone asks AbL3In-15b literally means: ```yaml Ab = Abliterated L3 = Llama-3 In = Instruct 15b = its 15b perameters ``` ## GGUF's [GGUF by @Mradermacher](https://huggingface.co/mradermacher/AbL3In-15B-GGUF) ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: passthrough slices: - sources: - layer_range: [0, 24] model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 - sources: - layer_range: [8, 24] model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [8, 24] model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [24, 32] model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 ``` # [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_TheSkullery__AbL3In-15B) | Metric |Value| |---------------------------------|----:| |Avg. |67.46| |AI2 Reasoning Challenge (25-Shot)|61.77| |HellaSwag (10-Shot) |78.42| |MMLU (5-Shot) |66.57| |TruthfulQA (0-shot) |52.53| |Winogrande (5-shot) |74.74| |GSM8k (5-shot) |70.74|