--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - cognitivecomputations/dolphin-2.8-experiment26-7b - argilla/CapybaraHermes-2.5-Mistral-7B base_model: - cognitivecomputations/dolphin-2.8-experiment26-7b - argilla/CapybaraHermes-2.5-Mistral-7B --- # J.O.S.I.E.3-Beta11-7B-slerp J.O.S.I.E.3-Beta11-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [cognitivecomputations/dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b) * [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: cognitivecomputations/dolphin-2.8-experiment26-7b layer_range: [0, 32] - model: argilla/CapybaraHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: argilla/CapybaraHermes-2.5-Mistral-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## Evaluation ```json { "all": { "acc": 0.6403971587643947, "acc_stderr": 0.03228725576276735, "acc_norm": 0.6413927640714372, "acc_norm_stderr": 0.03294011331780708, "mc1": 0.39167686658506734, "mc1_stderr": 0.017087795881769622, "mc2": 0.5576866593959974, "mc2_stderr": 0.01554622060467735 }, "harness|arc:challenge|25": { "acc": 0.6186006825938567, "acc_stderr": 0.014194389086685244, "acc_norm": 0.6450511945392492, "acc_norm_stderr": 0.013983036904094087 }, "harness|hellaswag|10": { "acc": 0.6738697470623382, "acc_stderr": 0.004678375103797962, "acc_norm": 0.8499302927703645, "acc_norm_stderr": 0.003564098420387764 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145634, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145634 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.548936170212766, "acc_stderr": 0.032529096196131965, "acc_norm": 0.548936170212766, "acc_norm_stderr": 0.032529096196131965 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778398, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778398 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.0315841532404771, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.0315841532404771 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8181818181818182, "acc_stderr": 0.027479603010538804, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.027479603010538804 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328972, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328972 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.658974358974359, "acc_stderr": 0.024035489676335082, "acc_norm": 0.658974358974359, "acc_norm_stderr": 0.024035489676335082 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473072, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473072 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8348623853211009, "acc_stderr": 0.015919557829976037, "acc_norm": 0.8348623853211009, "acc_norm_stderr": 0.015919557829976037 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437413, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437413 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8143459915611815, "acc_stderr": 0.02531049537694486, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.02531049537694486 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229146, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229146 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8007662835249042, "acc_stderr": 0.014283378044296418, "acc_norm": 0.8007662835249042, "acc_norm_stderr": 0.014283378044296418 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7225433526011561, "acc_stderr": 0.024105712607754307, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.024105712607754307 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.30726256983240224, "acc_stderr": 0.01543015884646961, "acc_norm": 0.30726256983240224, "acc_norm_stderr": 0.01543015884646961 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188933, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188933 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7283950617283951, "acc_stderr": 0.024748624490537368, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.024748624490537368 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4706649282920469, "acc_stderr": 0.01274823839736555, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.01274823839736555 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406762, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406762 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6764705882352942, "acc_stderr": 0.018926082916083376, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.018926082916083376 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.02812342933514278, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.02812342933514278 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.02619392354445412, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.02619392354445412 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.39167686658506734, "mc1_stderr": 0.017087795881769622, "mc2": 0.5576866593959974, "mc2_stderr": 0.01554622060467735 }, "harness|winogrande|5": { "acc": 0.7884767166535123, "acc_stderr": 0.011477747684223188 }, "harness|gsm8k|5": { "acc": 0.6360879454131918, "acc_stderr": 0.013252539227966195 } } ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Isaak-Carter/J.O.S.I.E.3-Beta11-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```