LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='SEQ_CLS', inference_mode=False, r=4, target_modules={'query'}, lora_alpha=32, lora_dropout=0.01, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_...
`num_virtual_tokens`,`2*layers*hidden`)83"""84 def __init__(self, config):# PrefixTuningConfig(peft_type=<PeftType.PREFIX_TUNING: 'PREFIX_TUNING
引进必要的库 from transformers import AutoModelForSeq2SeqLM+ from peft import get_peft_model, LoraConfig, TaskType model_name_or_path = "bigscience/mt0-large" tokenizer_name_or_path = "bigscience/mt0-large"创建PEFT方法对应的配置 peft_config = LoraConfig( task_type=TaskType.SEQ_2_...
CUDA_VISIBLE_DEVICES=0 swift rlhf \ --rlhf_type dpo \ --model Qwen/Qwen2.5-7B-Instruct \ --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \ --train_type lora \ --output_dir output \ ... Inference CUDA_VISIBLE_DEVICES=0 swift infer \ --model Qwen/Qwen2.5-7B-Instruct \ --stre...
per_device_eval_batch_size=1 \ --learning_rate=1e-4 \ --lr_scheduler_type="cosine"\ --weight_decay=0.05 \ --num_train_epochs 1 \ --gradient_accumulation_steps=2 \ --output_dir="results/peft_lora_e5_ecommerce_semantic_search" \ --seed=42 \ --with_tracking \ --use_peft \ -...
LLM+LoRa微调加速技术原理及基于PEFT的动手实践:一些思考和mt0-large+lora完整案例OLLM+LoRa微调加速技术原理及基于PEFT的动手实... LLM+LoRa微调加速技术原理及基于PEFT的动手实践:一些思考和mt0-large+lora完整案例 LLM+LoRa微调加速技术原理及基于PEFT的动手实践:一些思考和mt0-large+lora完整案...
# Get the typecompute_dtype = getattr(torch, bnb_4bit_compute_dtype)# BitsAndBytesConfig int-4 configbnb_config = BitsAndBytesConfig( load_in_4bit=use_4bit, bnb_4bit_use_double_quant=use_double_nested_quant, bnb_4bit_quant_type=bnb_4bit_quant_type, bnb_4bit_compute...
CUDA_VISIBLE_DEVICES=0python src/train_bash.py \--do_train \--model_name_or_path/home/aistudio/work/chatglm2-6b \--dataset self_cognition \--dataset_dirdata\--finetuning_type freeze \--output_dir output/freeze_sft_checkpoint \--overwrite_cache \--per_device_train_batch_size2\--gradi...
task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) 通过调用get_peft_model包装基础 🤗 Transformer 模型 model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) + model = get_peft_model(model, peft_config) ...
task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1) model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) model = get_peft_model(model, peft_config) model.print_trainable_parameters()# output: trainable params: 2359296 || all param...