PeftModel, get_peft_model, prepare_model_for_kbit_training, ) from transformers import ( AutoConfig,AutoModelForCausalLM,AutoTokenizer,BitsAndBytesConfig, ) Loading the model and the tokenizer 下载模型: from modelscope import snapshot_download # 指定模型名称 model_name = "Qwen/Qwen2.5-0.5B" #...
from peft import get_peft_model, LoraConfig, TaskType File "/root/miniconda3/lib/python3.10/site-packages/peft/init.py", line 22, in from .auto import ( File "/root/miniconda3/lib/python3.10/site-packages/peft/auto.py", line 32, in from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPI...
from peft import ( PeftModel, TaskType, LoraConfig, get_peft_model ) from peft import PeftModel, TaskType, LoraConfig, get_peft_model from llmtuner.extras.logging import get_logger from llmtuner.tuner.core.utils import find_all_linear_modules from llmtuner.model.utils import find_all_linear...
Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration withget_peft_model. For the bigscience/mt0-large model, you're only training 0.19% of the parameters! fromtransformersimportAutoModelForSeq2SeqLMfrompeftimportget_peft_config, get_p...
importwhisper_timestampedaswhisperfromtransformersimportAutoProcessor, WhisperForConditionalGenerationfrompeftimportprepare_model_for_kbit_training, LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_modelfrompeftimportPeftModel, PeftConfigimporttorchfromdatasetsimportDataset, AudiofromtransformersimportAutoFeature...
针对你提出的“cannot import name 'prepare_model_for_kbit_training' from 'peft'”问题,以下是根据你提供的提示进行的详细分析和解答: 确认peft库是否正确安装: 首先,确保你已经安装了peft库。你可以通过运行以下命令来检查peft库是否已安装: bash pip show peft 如果未安装,你可以使用以下命令来安装: bash pi...
[peft]ImportError: cannot import name ‘is_npu_available‘ from ‘accelerate.utils‘,卸载后重新安装就可以了。
from transformers import TrainingArguments from peft import LoraConfig from trl import RewardTrainer training_args = TrainingArguments( output_dir="./train_logs", max_steps=1000, per_device_train_batch_size=4, gradient_accumulation_steps=1, learning_rate=1.41e-5, optim="adamw_torch", save_...
model: peft: peft_scheme: "lora" restore_from_path: null In code, the only differences between PEFT and full-parameter fine-tuning are the add_adapter and load_adapters functions.NeMo 2.0 (New Release) In NeMo 2.0, PEFT is enabled by passing in the PEFT method callback to both the tr...
Using the fine-tune API, PEFT is enabled by passing in thepeftflag. The base model and adapter paths can also be specified. fromnemo.collectionsimportllmsft=llm.finetune(...peft=llm.peft.LoRA(target_modules=['linear_qkv','linear_proj'],dim=32),...)sft.resume.import_path="hf://.....