MetaModels
Collection
Bringing my ideas to life • 4 items • Updated
Text Generation
Transformers
Safetensors
llama
Merge
mergekit
conversational
text-generation-inference
MetaModel
This model is a merge of the following models made with mergekit:
🧩 Configuration
slices:
- sources:
- model: jeonsworld/CarbonVillain-en-10.7B-v4
layer_range: [0, 48]
- model: kekmodel/StopCarbon-10.7B-v5
layer_range: [0, 48]
merge_method: slerp
base_model: jeonsworld/CarbonVillain-en-10.7B-v4
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
Dataset Card for Evaluation run of gagan3012/MetaModel
Dataset automatically created during the evaluation run of model gagan3012/MetaModel on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_gagan3012__MetaModel",
"harness_winogrande_5",
split="train")
Latest results
These are the latest results from run 2024-01-04T14:09:43.780941(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
{
"all": {
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"acc_stderr": 0.031642195230944255,
"acc_norm": 0.6671639222858992,
"acc_norm_stderr": 0.03228745343467652,
"mc1": 0.5691554467564259,
"mc1_stderr": 0.01733527247533237,
"mc2": 0.7184177934834866,
"mc2_stderr": 0.014995634120330182
},
"harness|arc:challenge|25": {
"acc": 0.6843003412969283,
"acc_stderr": 0.013582571095815291,
"acc_norm": 0.7107508532423208,
"acc_norm_stderr": 0.01325001257939344
},
"harness|hellaswag|10": {
"acc": 0.7132045409281019,
"acc_stderr": 0.004513409114983828,
"acc_norm": 0.8844851623182632,
"acc_norm_stderr": 0.0031898897894046684
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.743421052631579,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.05021167315686781,
"acc_norm": 0.48,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.32,
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},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.5,
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},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.0442626668137991,
"acc_norm": 0.42857142857142855,
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
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"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.72,
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_stderr": 0.029344572500634332,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.029344572500634332
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_stderr": 0.01563002297009246,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.01563002297009246
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.5740740740740741,
"acc_norm_stderr": 0.03372343271653062
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.02450980392156862,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.02450980392156862
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8565400843881856,
"acc_stderr": 0.022818291821017012,
"acc_norm": 0.8565400843881856,
"acc_norm_stderr": 0.022818291821017012
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.03149384670994131,
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"acc_norm_stderr": 0.03149384670994131
},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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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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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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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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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"acc_norm_stderr": 0.038444531817709175
},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03188578017686398
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5691554467564259,
"mc1_stderr": 0.01733527247533237,
"mc2": 0.7184177934834866,
"mc2_stderr": 0.014995634120330182
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.010450899545370632
},
"harness|gsm8k|5": {
"acc": 0.6535253980288097,
"acc_stderr": 0.013107179054313398
}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.4 |
| ARC (25-shot) | 71.08 |
| HellaSwag (10-shot) | 88.45 |
| MMLU (5-shot) | 66.26 |
| TruthfulQA (0-shot) | 71.84 |
| Winogrande (5-shot) | 83.43 |
| GSM8K (5-shot) | 65.35 |
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Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "gagan3012/MetaModel"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gagan3012/MetaModel", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'