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--- |
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license: llama2 |
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--- |
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## Lazy LoRA |
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### Benefits |
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0. using the updated [Meta's LLaMA-2 models](https://huggingface.co/meta-llama/Llama-2-7b-hf). |
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1. support [4-bit qlora](https://arxiv.org/abs/2305.14314), extreme GPU memory and inference time saving; |
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2. comparable MMLU evaluation dataset results: |
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| | eval | test | comp-eval | comp-test | |
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|---------------|--------|--------|-----------|-----------| |
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|llama2-7b | 46.68% | 46.82% | | | |
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|ckpt-200 | 44.28% | 46.03% | -2.40% | -0.79% | |
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|ckpt-600 | 45.26% | 45.61% | -1.42% | -1.21% | |
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llama2-7b: "4e4d531bcab430a66c4d562b7e89e21c0fa235ea" |
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### Introduction |
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Determine the rank of LoRA layers by the singular values of pretrained weight matrices. |
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Also, combines: |
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1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685) |
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2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.3 |
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53/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.or |
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g/pdf/2110.07602.pdf) |
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3. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) |
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4. LLaMA adapter: [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention] (https://arxiv.org/abs/2303.16199) |
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in one model. |
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This allows you to perform LoRA (additional low rank adapters inserted to each linear layer), and prompt learning (additional virtual tokens attached to the input and to the attention layers acting as `past_key_values`) |
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## Usage: |
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```python |
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import sys |
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sys.path.insert(1, '/workspace/asr/peft/src') |
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# TODO set this path to the lazy-lora source code path, |
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# or you can install it from source code: |
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# TODO, please install lazylora for usage: |
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# git clone [email protected]:Xianchao-Wu/peft.git |
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# cd peft |
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# python setup.py install |
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from transformers import (AutoTokenizer, |
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AutoModelForCausalLM, BitsAndBytesConfig) |
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from peft import PeftModel, PeftConfig |
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import os |
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import torch |
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#import ipdb; ipdb.set_trace() |
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cache_dir="/workspace/asr/peft/qlora" |
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# TODO set this cache_dir to the path where you |
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# stored (or, want to store) llama2-7bhf model |
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lazylora_dir=os.getcwd() |
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# the path that contains 'adapter_config.json' |
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# and 'adapter_model.bin' |
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config = PeftConfig.from_pretrained(lazylora_dir) |
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tokenizer = AutoTokenizer.from_pretrained( |
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config.base_model_name_or_path, |
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cache_dir=cache_dir, |
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use_auth_token=True |
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) |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type='nf4', |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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config.base_model_name_or_path, |
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quantization_config=bnb_config, |
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device_map="auto", |
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cache_dir=cache_dir, |
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use_auth_token=True |
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) |
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#model.print_trainable_parameters() |
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print(sum(p.numel() for p in model.parameters())) |
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# 3,500,412,928 -> half-size of 7B due to 4-bit loading |
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model = PeftModel.from_pretrained(model, lazylora_dir) |
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print('after adding lazy lora parameters:') |
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model.print_trainable_parameters() |
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# trainable params: 0 || all params: 3,660,359,168 || trainable%: 0.0 |
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``` |
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## MMLU result: |
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### MMLU eval result: |
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```json |
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{"mmlu_loss": 1.9065961667247102, |
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"mmlu_eval_accuracy_professional_medicine": 0.3870967741935484, |
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"mmlu_eval_accuracy_college_physics": 0.45454545454545453, |
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"mmlu_eval_accuracy_conceptual_physics": 0.34615384615384615, |
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"mmlu_eval_accuracy_econometrics": 0.3333333333333333, |
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"mmlu_eval_accuracy_high_school_chemistry": 0.45454545454545453, |
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"mmlu_eval_accuracy_nutrition": 0.5151515151515151, |
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"mmlu_eval_accuracy_high_school_computer_science": 0.5555555555555556, |
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"mmlu_eval_accuracy_security_studies": 0.4444444444444444, |
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"mmlu_eval_accuracy_world_religions": 0.6842105263157895, |
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"mmlu_eval_accuracy_anatomy": 0.5, |
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"mmlu_eval_accuracy_prehistory": 0.42857142857142855, |
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"mmlu_eval_accuracy_high_school_government_and_politics": 0.6666666666666666, |
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"mmlu_eval_accuracy_professional_accounting": 0.3225806451612903, |
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"mmlu_eval_accuracy_philosophy": 0.4411764705882353, |
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"mmlu_eval_accuracy_astronomy": 0.3125, |
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"mmlu_eval_accuracy_medical_genetics": 0.8181818181818182, |
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"mmlu_eval_accuracy_jurisprudence": 0.5454545454545454, |
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"mmlu_eval_accuracy_professional_law": 0.38235294117647056, |
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"mmlu_eval_accuracy_college_chemistry": 0.125, |
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"mmlu_eval_accuracy_moral_disputes": 0.4473684210526316, |
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"mmlu_eval_accuracy_abstract_algebra": 0.36363636363636365, |
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"mmlu_eval_accuracy_computer_security": 0.5454545454545454, |
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"mmlu_eval_accuracy_business_ethics": 0.5454545454545454, |
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"mmlu_eval_accuracy_virology": 0.5, |
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"mmlu_eval_accuracy_electrical_engineering": 0.375, |
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"mmlu_eval_accuracy_high_school_biology": 0.34375, |
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"mmlu_eval_accuracy_public_relations": 0.3333333333333333, |
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"mmlu_eval_accuracy_high_school_physics": 0.35294117647058826, |
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"mmlu_eval_accuracy_high_school_psychology": 0.65, |
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"mmlu_eval_accuracy_college_computer_science": 0.5454545454545454, |
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"mmlu_eval_accuracy_high_school_european_history": 0.7222222222222222, |
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"mmlu_eval_accuracy_international_law": 0.8461538461538461, |
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"mmlu_eval_accuracy_high_school_microeconomics": 0.2692307692307692, |
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"mmlu_eval_accuracy_college_biology": 0.25, |
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"mmlu_eval_accuracy_formal_logic": 0.14285714285714285, |
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"mmlu_eval_accuracy_machine_learning": 0.18181818181818182, |
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"mmlu_eval_accuracy_human_aging": 0.6956521739130435, |
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"mmlu_eval_accuracy_logical_fallacies": 0.5555555555555556, |
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"mmlu_eval_accuracy_clinical_knowledge": 0.41379310344827586, |
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"mmlu_eval_accuracy_high_school_macroeconomics": 0.3488372093023256, |
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"mmlu_eval_accuracy_miscellaneous": 0.5930232558139535, |
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"mmlu_eval_accuracy_sociology": 0.7272727272727273, |
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"mmlu_eval_accuracy_high_school_us_history": 0.6363636363636364, |
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"mmlu_eval_accuracy_college_medicine": 0.4090909090909091, |
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"mmlu_eval_accuracy_high_school_world_history": 0.5, |
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"mmlu_eval_accuracy_marketing": 0.8, |
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"mmlu_eval_accuracy_human_sexuality": 0.4166666666666667, |
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"mmlu_eval_accuracy_professional_psychology": 0.36231884057971014, |
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"mmlu_eval_accuracy_moral_scenarios": 0.24, |
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"mmlu_eval_accuracy_college_mathematics": 0.18181818181818182, |
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"mmlu_eval_accuracy_us_foreign_policy": 0.6363636363636364, |
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"mmlu_eval_accuracy_high_school_geography": 0.6818181818181818, |
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"mmlu_eval_accuracy_high_school_statistics": 0.34782608695652173, |
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"mmlu_eval_accuracy_high_school_mathematics": 0.2413793103448276, |
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"mmlu_eval_accuracy_elementary_mathematics": 0.3170731707317073, |
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"mmlu_eval_accuracy_management": 0.36363636363636365, |
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"mmlu_eval_accuracy_global_facts": 0.2, |
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"mmlu_eval_accuracy": 0.4526436056641111} |
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``` |
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### MMLU test result: |
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```json |
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{"mmlu_loss": 1.925738222594615, |
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"mmlu_test_accuracy_business_ethics": 0.53, |
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"mmlu_test_accuracy_medical_genetics": 0.53, |
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"mmlu_test_accuracy_international_law": 0.628099173553719, |
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"mmlu_test_accuracy_professional_law": 0.3363754889178618, |
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"mmlu_test_accuracy_econometrics": 0.32456140350877194, |
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"mmlu_test_accuracy_high_school_biology": 0.4806451612903226, |
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"mmlu_test_accuracy_computer_security": 0.57, |
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"mmlu_test_accuracy_global_facts": 0.34, |
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"mmlu_test_accuracy_clinical_knowledge": 0.46037735849056605, |
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"mmlu_test_accuracy_miscellaneous": 0.6347381864623244, |
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"mmlu_test_accuracy_high_school_microeconomics": 0.39915966386554624, |
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"mmlu_test_accuracy_public_relations": 0.5636363636363636, |
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"mmlu_test_accuracy_high_school_computer_science": 0.45, |
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"mmlu_test_accuracy_human_sexuality": 0.5572519083969466, |
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"mmlu_test_accuracy_virology": 0.43373493975903615, |
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"mmlu_test_accuracy_human_aging": 0.5695067264573991, |
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"mmlu_test_accuracy_high_school_world_history": 0.6371308016877637, |
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"mmlu_test_accuracy_college_medicine": 0.3699421965317919, |
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"mmlu_test_accuracy_marketing": 0.6923076923076923, |
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"mmlu_test_accuracy_world_religions": 0.6783625730994152, |
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"mmlu_test_accuracy_college_physics": 0.23529411764705882, |
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"mmlu_test_accuracy_high_school_chemistry": 0.33004926108374383, |
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"mmlu_test_accuracy_elementary_mathematics": 0.2751322751322751, |
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"mmlu_test_accuracy_high_school_psychology": 0.6018348623853211, |
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"mmlu_test_accuracy_sociology": 0.5920398009950248, |
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"mmlu_test_accuracy_astronomy": 0.4342105263157895, |
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"mmlu_test_accuracy_high_school_mathematics": 0.27037037037037037, |
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"mmlu_test_accuracy_high_school_us_history": 0.5343137254901961, |
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"mmlu_test_accuracy_logical_fallacies": 0.49693251533742333, |
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"mmlu_test_accuracy_high_school_statistics": 0.19907407407407407, |
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"mmlu_test_accuracy_management": 0.5825242718446602, |
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"mmlu_test_accuracy_moral_disputes": 0.5057803468208093, |
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"mmlu_test_accuracy_formal_logic": 0.24603174603174602, |
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"mmlu_test_accuracy_college_chemistry": 0.25, |
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"mmlu_test_accuracy_college_mathematics": 0.3, |
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"mmlu_test_accuracy_high_school_geography": 0.5050505050505051, |
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"mmlu_test_accuracy_machine_learning": 0.35714285714285715, |
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"mmlu_test_accuracy_philosophy": 0.5787781350482315, |
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"mmlu_test_accuracy_college_computer_science": 0.32, |
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"mmlu_test_accuracy_security_studies": 0.46938775510204084, |
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"mmlu_test_accuracy_abstract_algebra": 0.27, |
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"mmlu_test_accuracy_professional_psychology": 0.4526143790849673, |
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"mmlu_test_accuracy_college_biology": 0.4444444444444444, |
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"mmlu_test_accuracy_us_foreign_policy": 0.68, |
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"mmlu_test_accuracy_professional_medicine": 0.4522058823529412, |
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"mmlu_test_accuracy_prehistory": 0.48148148148148145, |
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"mmlu_test_accuracy_anatomy": 0.45925925925925926, |
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"mmlu_test_accuracy_moral_scenarios": 0.2346368715083799, |
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"mmlu_test_accuracy_nutrition": 0.4738562091503268, |
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"mmlu_test_accuracy_high_school_macroeconomics": 0.4461538461538462, |
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"mmlu_test_accuracy_high_school_european_history": 0.6181818181818182, |
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"mmlu_test_accuracy_jurisprudence": 0.5370370370370371, |
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"mmlu_test_accuracy_professional_accounting": 0.35815602836879434, |
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"mmlu_test_accuracy_high_school_government_and_politics": 0.6321243523316062, |
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"mmlu_test_accuracy_high_school_physics": 0.32450331125827814, |
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"mmlu_test_accuracy_electrical_engineering": 0.47586206896551725, |
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"mmlu_test_accuracy_conceptual_physics": 0.3872340425531915, |
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"mmlu_test_accuracy": 0.4560969792275357} |
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``` |
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## License and intended use |
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This lazy-lora adapter is based on [Meta's LLaMA-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf), and using the [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1), following [Guanaco](https://huggingface.co/timdettmers/guanaco-65b). |
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lazy lora adapter weights are available under LLAMA-2 license. Note the use of the lazy lora adapter weights, requires access to the LLaMA model weighs. Lazy lora is based on LLaMA and therefore should be used according to the LLaMA license. |
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## Risks and Biases |
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The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs. |
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