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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-chat-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-chat | 49.38% | 48.22% | | | |
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|ckpt-1600 | 46.51% | 47.44% | -2.87% | -0.78% | |
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|ckpt-1500 | 47.66% | 46.88% | -1.72% | -1.33% | |
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llama2-7b-chat: '0ede8dd71e923db6258295621d817ca8714516d4' |
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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 git@github.com: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) llama2-7b-chat-hf 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": 1.44828412985973, |
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"mmlu_eval_accuracy_professional_accounting": 0.3548387096774194, |
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"mmlu_eval_accuracy_high_school_physics": 0.35294117647058826, |
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"mmlu_eval_accuracy_elementary_mathematics": 0.24390243902439024, |
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"mmlu_eval_accuracy_high_school_statistics": 0.391304347826087, |
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"mmlu_eval_accuracy_machine_learning": 0.36363636363636365, |
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"mmlu_eval_accuracy_sociology": 0.6363636363636364, |
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"mmlu_eval_accuracy_human_sexuality": 0.4166666666666667, |
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"mmlu_eval_accuracy_professional_medicine": 0.3870967741935484, |
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"mmlu_eval_accuracy_computer_security": 0.2727272727272727, |
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"mmlu_eval_accuracy_astronomy": 0.375, |
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"mmlu_eval_accuracy_high_school_computer_science": 0.5555555555555556, |
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"mmlu_eval_accuracy_high_school_world_history": 0.5384615384615384, |
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"mmlu_eval_accuracy_high_school_psychology": 0.7166666666666667, |
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"mmlu_eval_accuracy_professional_law": 0.3176470588235294, |
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"mmlu_eval_accuracy_econometrics": 0.16666666666666666, |
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"mmlu_eval_accuracy_high_school_chemistry": 0.2727272727272727, |
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"mmlu_eval_accuracy_global_facts": 0.5, |
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"mmlu_eval_accuracy_high_school_government_and_politics": 0.5238095238095238, |
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"mmlu_eval_accuracy_electrical_engineering": 0.25, |
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"mmlu_eval_accuracy_college_chemistry": 0.375, |
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"mmlu_eval_accuracy_high_school_biology": 0.4375, |
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"mmlu_eval_accuracy_high_school_microeconomics": 0.4230769230769231, |
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"mmlu_eval_accuracy_public_relations": 0.5833333333333334, |
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"mmlu_eval_accuracy_high_school_macroeconomics": 0.4883720930232558, |
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"mmlu_eval_accuracy_world_religions": 0.7368421052631579, |
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"mmlu_eval_accuracy_medical_genetics": 0.8181818181818182, |
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"mmlu_eval_accuracy_college_biology": 0.375, |
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"mmlu_eval_accuracy_jurisprudence": 0.36363636363636365, |
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"mmlu_eval_accuracy_college_mathematics": 0.36363636363636365, |
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"mmlu_eval_accuracy_miscellaneous": 0.627906976744186, |
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"mmlu_eval_accuracy_high_school_us_history": 0.8181818181818182, |
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"mmlu_eval_accuracy_virology": 0.4444444444444444, |
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"mmlu_eval_accuracy_anatomy": 0.5, |
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"mmlu_eval_accuracy_college_medicine": 0.3181818181818182, |
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"mmlu_eval_accuracy_human_aging": 0.7391304347826086, |
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"mmlu_eval_accuracy_college_physics": 0.45454545454545453, |
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"mmlu_eval_accuracy_philosophy": 0.38235294117647056, |
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"mmlu_eval_accuracy_clinical_knowledge": 0.4482758620689655, |
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"mmlu_eval_accuracy_formal_logic": 0.21428571428571427, |
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"mmlu_eval_accuracy_nutrition": 0.6363636363636364, |
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"mmlu_eval_accuracy_high_school_mathematics": 0.20689655172413793, |
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"mmlu_eval_accuracy_logical_fallacies": 0.6666666666666666, |
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"mmlu_eval_accuracy_professional_psychology": 0.42028985507246375, |
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"mmlu_eval_accuracy_prehistory": 0.5428571428571428, |
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"mmlu_eval_accuracy_high_school_geography": 0.7272727272727273, |
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"mmlu_eval_accuracy_management": 0.6363636363636364, |
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"mmlu_eval_accuracy_marketing": 0.76, |
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"mmlu_eval_accuracy_international_law": 0.9230769230769231, |
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"mmlu_eval_accuracy_us_foreign_policy": 0.7272727272727273, |
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"mmlu_eval_accuracy_moral_scenarios": 0.32, |
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"mmlu_eval_accuracy_high_school_european_history": 0.5, |
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"mmlu_eval_accuracy_business_ethics": 0.45454545454545453, |
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"mmlu_eval_accuracy_moral_disputes": 0.39473684210526316, |
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"mmlu_eval_accuracy_conceptual_physics": 0.38461538461538464, |
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"mmlu_eval_accuracy_security_studies": 0.5925925925925926, |
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"mmlu_eval_accuracy_abstract_algebra": 0.36363636363636365, |
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"mmlu_eval_accuracy_college_computer_science": 0.36363636363636365, |
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"mmlu_eval_accuracy": 0.4766441930115949} |
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``` |
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### MMLU test result: |
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```json |
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{"mmlu_loss": 1.4452685356679218, |
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"mmlu_test_accuracy_moral_scenarios": 0.23575418994413408, |
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"mmlu_test_accuracy_security_studies": 0.5020408163265306, |
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"mmlu_test_accuracy_astronomy": 0.4934210526315789, |
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"mmlu_test_accuracy_medical_genetics": 0.52, |
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"mmlu_test_accuracy_logical_fallacies": 0.5521472392638037, |
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"mmlu_test_accuracy_professional_psychology": 0.4444444444444444, |
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"mmlu_test_accuracy_high_school_psychology": 0.6110091743119266, |
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"mmlu_test_accuracy_high_school_us_history": 0.6372549019607843, |
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"mmlu_test_accuracy_high_school_physics": 0.33112582781456956, |
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"mmlu_test_accuracy_prehistory": 0.5308641975308642, |
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"mmlu_test_accuracy_human_sexuality": 0.549618320610687, |
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"mmlu_test_accuracy_management": 0.6213592233009708, |
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"mmlu_test_accuracy_international_law": 0.6363636363636364, |
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"mmlu_test_accuracy_moral_disputes": 0.49421965317919075, |
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"mmlu_test_accuracy_conceptual_physics": 0.4127659574468085, |
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"mmlu_test_accuracy_econometrics": 0.3508771929824561, |
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"mmlu_test_accuracy_college_medicine": 0.3815028901734104, |
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"mmlu_test_accuracy_high_school_biology": 0.5064516129032258, |
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"mmlu_test_accuracy_high_school_statistics": 0.27314814814814814, |
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"mmlu_test_accuracy_high_school_macroeconomics": 0.43333333333333335, |
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"mmlu_test_accuracy_college_mathematics": 0.28, |
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"mmlu_test_accuracy_elementary_mathematics": 0.30687830687830686, |
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"mmlu_test_accuracy_public_relations": 0.509090909090909, |
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"mmlu_test_accuracy_high_school_european_history": 0.5515151515151515, |
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"mmlu_test_accuracy_human_aging": 0.5381165919282511, |
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"mmlu_test_accuracy_high_school_geography": 0.5555555555555556, |
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"mmlu_test_accuracy_formal_logic": 0.25396825396825395, |
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"mmlu_test_accuracy_miscellaneous": 0.665389527458493, |
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"mmlu_test_accuracy_high_school_computer_science": 0.4, |
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"mmlu_test_accuracy_global_facts": 0.33, |
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"mmlu_test_accuracy_world_religions": 0.6666666666666666, |
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"mmlu_test_accuracy_machine_learning": 0.33035714285714285, |
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"mmlu_test_accuracy_sociology": 0.6169154228855721, |
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"mmlu_test_accuracy_clinical_knowledge": 0.49433962264150944, |
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"mmlu_test_accuracy_virology": 0.4397590361445783, |
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"mmlu_test_accuracy_high_school_government_and_politics": 0.6839378238341969, |
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"mmlu_test_accuracy_high_school_world_history": 0.6329113924050633, |
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"mmlu_test_accuracy_college_biology": 0.5138888888888888, |
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"mmlu_test_accuracy_philosophy": 0.5627009646302251, |
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"mmlu_test_accuracy_college_physics": 0.2549019607843137, |
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"mmlu_test_accuracy_college_computer_science": 0.34, |
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"mmlu_test_accuracy_high_school_chemistry": 0.3793103448275862, |
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"mmlu_test_accuracy_nutrition": 0.5163398692810458, |
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"mmlu_test_accuracy_professional_accounting": 0.35106382978723405, |
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"mmlu_test_accuracy_jurisprudence": 0.5925925925925926, |
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"mmlu_test_accuracy_high_school_mathematics": 0.25925925925925924, |
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"mmlu_test_accuracy_marketing": 0.6923076923076923, |
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"mmlu_test_accuracy_business_ethics": 0.48, |
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"mmlu_test_accuracy_high_school_microeconomics": 0.4495798319327731, |
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"mmlu_test_accuracy_college_chemistry": 0.3, |
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"mmlu_test_accuracy_us_foreign_policy": 0.72, |
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"mmlu_test_accuracy_computer_security": 0.6, |
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"mmlu_test_accuracy_anatomy": 0.4740740740740741, |
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"mmlu_test_accuracy_professional_law": 0.3220338983050847, |
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"mmlu_test_accuracy_abstract_algebra": 0.27, |
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"mmlu_test_accuracy_electrical_engineering": 0.4827586206896552, |
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"mmlu_test_accuracy_professional_medicine": 0.3897058823529412, |
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"mmlu_test_accuracy": 0.46883545484585126} |
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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-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-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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