quantization_config=bnb_config, device_map=device_map, trust_remote_code=True, use_auth_token=True ) base_model.config.use_cache = False # More info: https://github.com/huggingface/transformers/pull/24906 base_model.config.pretraining_tp = 1 peft_config = LoraConfig( lora_alpha=16, lora_...
我们的示例中使用QLoRa，所以要指定BitsAndBytes配置，下载4位量化的预训练模型，定义LoraConfig。# 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_do...
from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model# 基于 QLoRA 论文来配置 LoRApeft_config = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64, bias="none", task_type="CAUSAL_LM", )# 为训练准备好模型model = prepare_model_for_kbit_training(mo...
pip install -U transformers datasets accelerate peft trl bitsandbytes wandb 安装完毕后,我们可以导入必要的库并登录到 W&B(可选): import gc import os import torch import wandb from datasets import load_dataset from google.colab import userdata from peft import LoraConfig, PeftModel, prepare_model_...
BitsAndBytesConfig, TrainingArguments, pipeline, logging, ) from peft import LoraConfig, PeftModel from trl import SFTTrainer 我们继续分析导入 torch是我们很熟悉的深度学习库,这里我们不需要torch的那些低级功能,但是它是transformers和trl的依赖,在这里我们需要使用torch来获取dtypes(数据类型),比如torch.Float16...
peft_config = LoraConfig( task_type=TaskType.SEQ_CLS, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1,)训练用 Weights & Biases 来记日志，并在 🤗 训练集群上，用 8 卡 A-100，要数小时，最后准确率为 67%。尽管看上去可能低了，但想想这个任务的难度。如下文要...
# 步骤1:导入peft库中Lora相关模块frompeftimport( LoraConfig, PeftModel, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, prepare_model_for_kbit_training, set_peft_model_state_dict, )# 步骤2:lora配置lora_config = LoraConfig(# lora配置r = model_args.lora_r,# ...
peft_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj'] ) # Load tokenizer ...
config, device_map=device_map, trust_remote_code=True, use_auth_token=True)base_model.config.use_cache = False# More info: https://github.com/huggingface/transformers/pull/24906base_model.config.pretraining_tp = 1 peft_config = LoraConfig( lora_alpha=16, lora_dropout=...
配置LoRA:设置LoRA配置。 peft_config = LoraConfig( task_type="CAUSAL_LM", inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) model = get_peft_model(model, peft_config) 训练:使用标准的transformers训练脚本进行训练。 from transformers import Trainer, TrainingArguments ...