def collate_fn(batch, tokenizer, block_size, device): out_batch = tokenizer( list(batch["input"]), padding="longest", max_length=block_size, truncation=True, return_tensors="pt" ) out_batch["labels"] = out_batch["input_ids"].clone() out_batch = tree.map_structure(lambda x: x.t...
在image_classification_timm_peft_lora模型微调任务时,训练这一步报错:KeyError: 'per_gpu_train_batch_size',但是在args中两句代码是这样的:per_device_train_batch_size=batch_size,per_device_eval_batch_size=batch_size并没有问题。 Environment / 环境信息 (Mandatory / 必填) -- MindSpore version : 2.3....
export MODEL_NAME="runwayml/stable-diffusion-v1-5" export INSTANCE_DIR="/diffusers/examples/dreambooth/dunhuang512" export OUTPUT_DIR="path-to-save-model" cd /diffusers/examples/dreambooth/ accelerate launch train_dreambooth_lora.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_...
针对Delta tuning有可以分为prompt tuning,lora,指定参数更新等方式。目前主要选择prompt tuning以及lora。 Prompt tuning新增模型参数用来表征prompt embedding,固定LLM大模型参数,只更新prompt embedding。 Lora通过重参数化的形式,新增两层低秩矩阵与LLM大模型的FFN层并列,只更新低秩矩阵部分的权重。 Delta tuning也可看做...
training_comment="this_LoRA_model_credit_from_bdsqlsz" # training_comment | 训练介绍,可以写作者名或者使用触发关键词 # Train related params | 训练相关参数 resolution="1024,1024" # image resolution w,h. 图片分辨率,宽,高。支持非正方形,但必须是 64 倍数。 batch_size=8 # batch size vae...
train_batch_size: Batch size (per device) for training. Defaults to4. num_train_epochs: Number of epochs to loop through your training dataset. Defaults to4000. max_train_steps: Number of individual training steps. Takes precedence over num_train_epochs. Defaults to1000. ...
--train_batch_size1\# 定义训练期间使用的批量大小。--num_train_epochs3\# 指定训练时期的数量。--trainersft\# 指定用于训练的训练器。--use_peft\# 使用参数高效的微调--use_int4\# 使用4位量化--lora-r16\# 将“lora-r”参数设置为16--lora-alpha32\# 将“lora-alpha”参数设置为32--lora-...
training_args = TrainingArguments(output_dir="./minimind_dpo", per_device_train_batch_size=1, remove_unused_columns=False) # 3、加载数据集:利用datasets库加载本地的JSON格式数据集,并进行数据集处理。 ### # Dataset ### # 确保路径正确,文件存在 dataset_path = './dataset/dpo/train_data.json...
{'model_id':model_id,# pre-trained model'dataset_path':'/opt/ml/input/data/training',# path where sagemaker will save training dataset'epochs':3,# number of training epochs'per_device_train_batch_size':1,# batch size for training'lr':2e-4,#...
batch_size = 1 block_size = 1024 trainer = "sft" warmup_ratio = 0.1 weight_decay = 0.01 gradient_accumulation = 4 use_fp16 = True use_peft = True use_int4 = True lora_r = 16 lora_alpha = 32 lora_dropout = 0.045 With all the information is ready, we would set up the environ...