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  Merge of top 7B models with DARE method
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- > mergekit is a toolkit for merging pre-trained language models. mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Merge of top 7B models with DARE method
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+ > mergekit is a toolkit for merging pre-trained language models. mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.
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+
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+ ## Eval
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+
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+ ```python
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+ {
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+ "all": {
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+ "acc": 0.6547370286177235,
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+ "acc_stderr": 0.03204709242170183,
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+ "acc_norm": 0.6537337854798912,
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+ "acc_norm_stderr": 0.03272317883588649,
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+ "mc1": 0.5189718482252142,
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+ "mc1_stderr": 0.01749089640576236,
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+ "mc2": 0.6631825155680797,
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+ "mc2_stderr": 0.01527641053841743
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+ },
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+ "harness|arc:challenge|25": {
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+ "acc": 0.6953924914675768,
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+ "acc_stderr": 0.013449522109932485,
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+ "acc_norm": 0.7175767918088737,
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+ "acc_norm_stderr": 0.013155456884097225
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+ },
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+ "harness|hellaswag|10": {
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+ "acc": 0.7120095598486357,
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+ "acc_stderr": 0.004519011688417168,
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+ "acc_norm": 0.8820952001593309,
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+ "acc_norm_stderr": 0.003218362717491129
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+ },
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+ "harness|hendrycksTest-abstract_algebra|5": {
40
+ "acc": 0.33,
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+ "acc_stderr": 0.047258156262526045,
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+ "acc_norm": 0.33,
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+ "acc_norm_stderr": 0.047258156262526045
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+ },
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+ "harness|hendrycksTest-anatomy|5": {
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+ "acc": 0.6296296296296297,
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+ "acc_stderr": 0.041716541613545426,
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+ "acc_norm": 0.6296296296296297,
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+ "acc_norm_stderr": 0.041716541613545426
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+ },
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+ "harness|hendrycksTest-astronomy|5": {
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+ "acc": 0.7105263157894737,
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+ "acc_stderr": 0.03690677986137283,
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+ "acc_norm": 0.7105263157894737,
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+ "acc_norm_stderr": 0.03690677986137283
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+ },
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+ "harness|hendrycksTest-business_ethics|5": {
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+ "acc": 0.64,
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+ "acc_stderr": 0.04824181513244218,
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+ "acc_norm": 0.64,
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+ "acc_norm_stderr": 0.04824181513244218
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+ },
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+ "harness|hendrycksTest-clinical_knowledge|5": {
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+ "acc": 0.7056603773584905,
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+ "acc_norm": 0.7056603773584905,
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+ "acc_norm_stderr": 0.02804918631569525
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+ },
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+ "harness|hendrycksTest-college_biology|5": {
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+ "acc": 0.7638888888888888,
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+ "acc_norm": 0.7638888888888888,
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+ "acc_norm_stderr": 0.03551446610810826
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+ },
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+ "harness|hendrycksTest-college_chemistry|5": {
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+ "acc": 0.48,
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+ "acc_norm": 0.48,
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+ "acc_norm_stderr": 0.050211673156867795
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+ },
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+ "harness|hendrycksTest-college_computer_science|5": {
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+ "acc": 0.54,
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+ "acc_stderr": 0.05009082659620333,
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+ "acc_norm": 0.54,
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+ "acc_norm_stderr": 0.05009082659620333
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+ },
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+ "harness|hendrycksTest-college_mathematics|5": {
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+ "acc": 0.31,
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+ "acc_stderr": 0.04648231987117316,
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+ "acc_norm": 0.31,
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+ "acc_norm_stderr": 0.04648231987117316
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+ },
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+ "harness|hendrycksTest-college_medicine|5": {
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+ "acc": 0.6705202312138728,
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+ "acc_stderr": 0.03583901754736411,
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+ "acc_norm": 0.6705202312138728,
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+ "acc_norm_stderr": 0.03583901754736411
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+ },
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+ "harness|hendrycksTest-college_physics|5": {
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+ "acc": 0.4215686274509804,
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+ "acc_stderr": 0.04913595201274498,
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+ "acc_norm": 0.4215686274509804,
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+ "acc_norm_stderr": 0.04913595201274498
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+ },
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+ "harness|hendrycksTest-computer_security|5": {
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+ "acc": 0.78,
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+ "acc_norm": 0.78,
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+ "acc_norm_stderr": 0.04163331998932263
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+ },
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+ "harness|hendrycksTest-conceptual_physics|5": {
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+ "acc": 0.5787234042553191,
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+ "acc_stderr": 0.03227834510146268,
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+ "acc_norm": 0.5787234042553191,
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+ "acc_norm_stderr": 0.03227834510146268
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+ },
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+ "harness|hendrycksTest-econometrics|5": {
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+ "acc": 0.5,
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+ "acc_norm": 0.5,
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+ "acc_norm_stderr": 0.047036043419179864
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+ },
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+ "harness|hendrycksTest-electrical_engineering|5": {
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+ "acc": 0.5586206896551724,
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+ "acc_stderr": 0.04137931034482758,
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+ "acc_norm": 0.5586206896551724,
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+ "acc_norm_stderr": 0.04137931034482758
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+ },
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+ "harness|hendrycksTest-elementary_mathematics|5": {
130
+ "acc": 0.42857142857142855,
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+ "acc_stderr": 0.02548718714785938,
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+ "acc_norm": 0.42857142857142855,
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+ "acc_norm_stderr": 0.02548718714785938
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+ },
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+ "harness|hendrycksTest-formal_logic|5": {
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+ "acc": 0.47619047619047616,
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+ "acc_norm": 0.47619047619047616,
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+ },
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+ "harness|hendrycksTest-global_facts|5": {
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+ "acc": 0.33,
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+ "acc_norm": 0.33,
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+ "acc_norm_stderr": 0.04725815626252604
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+ },
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+ "harness|hendrycksTest-high_school_biology|5": {
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+ "acc": 0.7903225806451613,
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+ "acc_norm": 0.7903225806451613,
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+ "acc_norm_stderr": 0.023157879349083525
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+ },
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+ "harness|hendrycksTest-high_school_chemistry|5": {
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+ "acc": 0.4876847290640394,
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+ "acc_stderr": 0.035169204442208966,
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+ "acc_norm": 0.4876847290640394,
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+ "acc_norm_stderr": 0.035169204442208966
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+ },
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+ "harness|hendrycksTest-high_school_computer_science|5": {
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+ "acc": 0.68,
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+ "acc_stderr": 0.04688261722621505,
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+ "acc_norm": 0.68,
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+ "acc_norm_stderr": 0.04688261722621505
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+ },
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+ "harness|hendrycksTest-high_school_european_history|5": {
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+ "acc": 0.7878787878787878,
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+ "acc_stderr": 0.03192271569548301,
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+ "acc_norm": 0.7878787878787878,
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+ "acc_norm_stderr": 0.03192271569548301
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+ },
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+ "harness|hendrycksTest-high_school_geography|5": {
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+ "acc": 0.797979797979798,
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+ "acc_stderr": 0.02860620428922987,
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+ "acc_norm": 0.797979797979798,
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+ "acc_norm_stderr": 0.02860620428922987
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+ },
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+ "harness|hendrycksTest-high_school_government_and_politics|5": {
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+ "acc": 0.9015544041450777,
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+ "acc_stderr": 0.021500249576033456,
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+ "acc_norm": 0.9015544041450777,
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+ "acc_norm_stderr": 0.021500249576033456
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+ },
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+ "harness|hendrycksTest-high_school_macroeconomics|5": {
184
+ "acc": 0.6666666666666666,
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+ "acc_stderr": 0.023901157979402538,
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+ "acc_norm": 0.6666666666666666,
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+ "acc_norm_stderr": 0.023901157979402538
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+ },
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+ "harness|hendrycksTest-high_school_mathematics|5": {
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+ "acc": 0.35185185185185186,
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+ "acc_norm_stderr": 0.029116617606083008
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+ },
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+ "harness|hendrycksTest-high_school_microeconomics|5": {
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+ "acc": 0.6722689075630253,
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+ "harness|hendrycksTest-high_school_physics|5": {
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+ },
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+ "harness|hendrycksTest-high_school_psychology|5": {
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+ "acc_norm": 0.8440366972477065,
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+ "acc_norm_stderr": 0.015555802713590167
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+ },
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+ "harness|hendrycksTest-high_school_statistics|5": {
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+ "acc": 0.5092592592592593,
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+ "acc_norm": 0.5092592592592593,
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+ "acc_norm_stderr": 0.034093869469927006
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+ },
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+ "harness|hendrycksTest-high_school_us_history|5": {
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+ "acc": 0.8333333333333334,
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+ "acc_norm": 0.8333333333333334,
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+ "acc_norm_stderr": 0.026156867523931045
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+ },
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+ "harness|hendrycksTest-high_school_world_history|5": {
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+ "acc_norm": 0.7848101265822784,
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+ },
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+ "harness|hendrycksTest-human_aging|5": {
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+ "acc": 0.6905829596412556,
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+ "acc_norm": 0.6905829596412556,
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+ },
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+ "harness|hendrycksTest-human_sexuality|5": {
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+ "acc": 0.7786259541984732,
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+ "acc_norm": 0.7786259541984732,
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+ "acc_norm_stderr": 0.03641297081313729
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+ },
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+ "harness|hendrycksTest-international_law|5": {
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+ "acc": 0.7933884297520661,
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+ "acc_norm": 0.7933884297520661,
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+ "acc_norm_stderr": 0.03695980128098824
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+ },
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+ "harness|hendrycksTest-jurisprudence|5": {
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+ "acc": 0.7870370370370371,
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+ "acc_norm": 0.7870370370370371,
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+ },
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+ "harness|hendrycksTest-logical_fallacies|5": {
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+ "acc": 0.7730061349693251,
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+ "acc_norm": 0.7730061349693251,
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+ "acc_norm_stderr": 0.03291099578615769
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+ },
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+ "harness|hendrycksTest-machine_learning|5": {
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+ "acc_norm": 0.45535714285714285,
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+ },
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+ "harness|hendrycksTest-management|5": {
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+ },
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+ "harness|hendrycksTest-marketing|5": {
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+ "harness|hendrycksTest-medical_genetics|5": {
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+ "harness|hendrycksTest-miscellaneous|5": {
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+ },
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+ "harness|hendrycksTest-moral_disputes|5": {
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+ "acc": 0.7427745664739884,
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+ "acc_norm": 0.7427745664739884,
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+ },
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+ "harness|hendrycksTest-moral_scenarios|5": {
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+ "acc": 0.4312849162011173,
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+ "acc_norm": 0.4312849162011173,
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+ },
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+ "harness|hendrycksTest-nutrition|5": {
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+ },
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+ "harness|hendrycksTest-philosophy|5": {
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+ "acc": 0.7170418006430869,
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+ "acc_norm": 0.7170418006430869,
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+ },
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+ "harness|hendrycksTest-prehistory|5": {
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+ "acc_norm": 0.7438271604938271,
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+ },
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+ "harness|hendrycksTest-professional_accounting|5": {
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+ "harness|hendrycksTest-professional_medicine|5": {
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+ },
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+ "harness|hendrycksTest-professional_psychology|5": {
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+ "harness|hendrycksTest-public_relations|5": {
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+ "harness|hendrycksTest-security_studies|5": {
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+ },
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+ "harness|hendrycksTest-sociology|5": {
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+ "harness|hendrycksTest-us_foreign_policy|5": {
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+ "acc": 0.85,
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+ "harness|hendrycksTest-virology|5": {
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+ "harness|hendrycksTest-world_religions|5": {
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+ "harness|winogrande|5": {
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+ "acc_stderr": 0.01020535179187352
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+ },
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+ "harness|gsm8k|5": {
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+ "acc": 0.7172100075815011,
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+ "acc_stderr": 0.012405020417873619
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+ }
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+ }
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+
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+ ```