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Create README.md
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README.md
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| 1 |
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---
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| 2 |
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tags:
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| 3 |
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- gptq
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- 4bit
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- int4
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- gptqmodel
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- modelcloud
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---
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This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
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- **bits**: 4
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- **group_size**: 128
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- **desc_act**: true
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- **static_groups**: false
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- **sym**: false
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- **lm_head**: false
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- **damp_percent**: 0.0025
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- **damp_auto_increment**: 0.0015
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- **true_sequential**: true
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- **model_name_or_path**: ""
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- **model_file_base_name**: "model"
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- **quant_method**: "gptq"
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- **checkpoint_format**: "gptq"
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- **meta**οΌ
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- **quantizer**: "gptqmodel:1.0.3-dev0"
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## Example:
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```python
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from transformers import AutoTokenizer
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from gptqmodel import GPTQModel
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model_name = "ModelCloud/GRIN-MoE-gptq-4bit"
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prompt = [
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{"role": "system",
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"content": "You are GRIN-MoE model from microsoft, a helpful assistant."},
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{"role": "user", "content": "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"}
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]
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = GPTQModel.from_quantized(model_name, trust_remote_code=True)
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input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100)
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result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
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print(result)
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```
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## Lm_eval result:
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| Tasks | Metric | | GRIN-MoE | GRIN-MoE-gptq-4bit |
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| ------------------------------------- | ---------- | --- | -------- | ------------------ |
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| 55 |
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| arc_challenge | acc | β | 0.6408 | 0.6425 |
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| 56 |
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| | acc_norm | β | 0.6561 | 0.6587 |
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| 57 |
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| arc_easy | acc | β | 0.8645 | 0.8683 |
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| 58 |
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| | acc_norm | β | 0.8422 | 0.846 |
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| 59 |
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| boolq | acc | β | 0.8820 | 0.8765 |
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| 60 |
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| hellaswag | acc | β | 0.6972 | 0.6891 |
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| 61 |
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| | acc_norm | β | 0.8518 | 0.8486 |
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| 62 |
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| lambada_openai | acc | β | 0.7058 | 0.7068 |
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| 63 |
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| | perplexity | β | 3.4568 | 3.5732 |
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| mmlu | acc | β | 0.7751 | 0.7706 |
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| - humanities | acc | β | 0.7394 | 0.7384 |
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| 66 |
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| - formal_logic | acc | β | 0.6429 | 0.6746 |
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| 67 |
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| - high_school_european_history | acc | β | 0.8606 | 0.8364 |
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| 68 |
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| - high_school_us_history | acc | β | 0.9118 | 0.902 |
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| - high_school_world_history | acc | β | 0.8903 | 0.8734 |
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| - international_law | acc | β | 0.9256 | 0.9091 |
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| 71 |
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| - jurisprudence | acc | β | 0.8426 | 0.8519 |
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| - logical_fallacies | acc | β | 0.8344 | 0.8528 |
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| - moral_disputes | acc | β | 0.7977 | 0.8208 |
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| 74 |
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| - moral_scenarios | acc | β | 0.6961 | 0.6849 |
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| - philosophy | acc | β | 0.8199 | 0.8071 |
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| - prehistory | acc | β | 0.8457 | 0.8426 |
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| - professional_law | acc | β | 0.6173 | 0.6193 |
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| 78 |
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| - world_religions | acc | β | 0.8480 | 0.8655 |
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| - other | acc | β | 0.8130 | 0.805 |
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| - business_ethics | acc | β | 0.8100 | 0.78 |
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| - clinical_knowledge | acc | β | 0.8415 | 0.8302 |
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| - college_medicine | acc | β | 0.7514 | 0.7457 |
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| - global_facts | acc | β | 0.5700 | 0.54 |
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| - human_aging | acc | β | 0.7803 | 0.7668 |
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| - management | acc | β | 0.8447 | 0.8447 |
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| - marketing | acc | β | 0.9145 | 0.9103 |
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| - medical_genetics | acc | β | 0.9200 | 0.89 |
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| - miscellaneous | acc | β | 0.8966 | 0.8927 |
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| - nutrition | acc | β | 0.8333 | 0.8268 |
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| - professional_accounting | acc | β | 0.6489 | 0.656 |
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| - professional_medicine | acc | β | 0.8750 | 0.8603 |
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| - virology | acc | β | 0.5422 | 0.5361 |
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| 93 |
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| - social sciences | acc | β | 0.8638 | 0.8544 |
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| - econometrics | acc | β | 0.5789 | 0.5789 |
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| - high_school_geography | acc | β | 0.9091 | 0.8788 |
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| - high_school_government_and_politics | acc | β | 0.9585 | 0.943 |
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| - high_school_macroeconomics | acc | β | 0.8308 | 0.8103 |
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| - high_school_microeconomics | acc | β | 0.9328 | 0.9286 |
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| - high_school_psychology | acc | β | 0.9321 | 0.9303 |
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| - human_sexuality | acc | β | 0.8779 | 0.8626 |
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| 101 |
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| - professional_psychology | acc | β | 0.8382 | 0.8219 |
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| 102 |
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| - public_relations | acc | β | 0.7545 | 0.7727 |
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| 103 |
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| - security_studies | acc | β | 0.7878 | 0.7918 |
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| 104 |
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| - sociology | acc | β | 0.8905 | 0.8955 |
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| 105 |
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| - us_foreign_policy | acc | β | 0.9000 | 0.88 |
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| 106 |
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| - stem | acc | β | 0.7044 | 0.7031 |
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| 107 |
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| - abstract_algebra | acc | β | 0.5000 | 0.45 |
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| 108 |
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| - anatomy | acc | β | 0.7407 | 0.7481 |
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| 109 |
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| - astronomy | acc | β | 0.8618 | 0.8618 |
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| - college_biology | acc | β | 0.8889 | 0.875 |
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| - college_chemistry | acc | β | 0.6100 | 0.59 |
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| 112 |
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| - college_computer_science | acc | β | 0.7100 | 0.67 |
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| 113 |
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| - college_mathematics | acc | β | 0.5100 | 0.58 |
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| 114 |
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| - college_physics | acc | β | 0.4608 | 0.4608 |
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| 115 |
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| - computer_security | acc | β | 0.8200 | 0.82 |
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| 116 |
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| - conceptual_physics | acc | β | 0.7787 | 0.766 |
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| 117 |
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| - electrical_engineering | acc | β | 0.6828 | 0.6828 |
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| 118 |
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| - elementary_mathematics | acc | β | 0.7566 | 0.7593 |
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| 119 |
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| - high_school_biology | acc | β | 0.9000 | 0.9097 |
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| 120 |
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| - high_school_chemistry | acc | β | 0.6650 | 0.665 |
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| 121 |
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| - high_school_computer_science | acc | β | 0.8700 | 0.86 |
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| 122 |
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| - high_school_mathematics | acc | β | 0.4370 | 0.4296 |
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| 123 |
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| - high_school_physics | acc | β | 0.5960 | 0.5894 |
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| 124 |
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| - high_school_statistics | acc | β | 0.7176 | 0.7222 |
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| 125 |
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| - machine_learning | acc | β | 0.6071 | 0.6339 |
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| 126 |
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| openbookqa | acc | β | 0.3920 | 0.386 |
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| 127 |
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| | acc_norm | β | 0.4900 | 0.486 |
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| 128 |
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| piqa | acc | β | 0.8183 | 0.8166 |
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| 129 |
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| | acc_norm | β | 0.8205 | 0.8177 |
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| 130 |
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| rte | acc | β | 0.8014 | 0.7834 |
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| 131 |
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| truthfulqa_mc1 | acc | β | 0.3880 | 0.399 |
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| 132 |
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| winogrande | acc | β | 0.7940 | 0.768 |
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| 133 |
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| | | | | |
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| 134 |
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| Groups | Metric | | Value | Value |
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| 135 |
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| mmlu | acc | β | 0.7751 | 0.7706 |
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| 136 |
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| - humanities | acc | β | 0.7394 | 0.7384 |
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| 137 |
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| - other | acc | β | 0.8130 | 0.805 |
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| 138 |
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| - social sciences | acc | β | 0.8638 | 0.8544 |
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| 139 |
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| - stem | acc | β | 0.7044 | 0.7031 |
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