from transformers import GenerationConfig 如果你的代码中使用了from transformers import generationconfig这样的语句,你需要将其更正为上述正确的形式。 如果GenerationConfig不存在(尽管在最新版本中它应该是存在的),寻找替代的类或方法来实现所需功能: 如果出于某种原因GenerationConfig类在你的环境中不可用,你可能需要...
from transformers import GenerationConfig gen_config = GenerationConfig(cache_implementation="offloaded", # other generation options such as num_beams=4,num_beam_groups=2,num_return_sequences=4,diversity_penalty=1.0,max_new_tokens=50,early_stopping=True) outputs = model.generate(inputs["input_ids...
generation_config.transformers_version = "4.37.0" model.generation_config.repetition_penalty = 1.05 """# 一、SFT - supervised fine-tuning (有监督的微调) ## **1-1、定义SFT阶段模型训练超参** """ from dataclasses import dataclass @dataclass class modelConfig: max_length:int = 1800 batch_...
from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, PreTrainedModel, get_scheduler, ) from huggingface_hub import HfApiapi = HfApi()@dataclass class Args: # common args exp_name: str = os.path.basename(__file__)[: -len(".py")] ...
...fromtransformersimportAutoTokenizer,AutoModelForSeq2SeqLMtokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")model=AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")... 2、使用本地路径加载 ...fromtransformersimportAutoTokenizer,AutoModelForSeq2SeqLMtokenizer=Au...
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以及检查GPU的工具函数。
Import your PyTorch model intoOpenVINO Runtimeto compress model size and increase inference speed. Instantly target Intel CPUs, GPUs (integrated or discrete), NPUs, or FPGAs. Deploy with OpenVINO model server for optimized inference in microservice applications, container-based, or cloud environments...
num_hidden_layers = 4 config.num_key_value_heads = 2 config.vocab_size = 128 我们的模型设置得非常小,因为只是尝试验证。接下来我们把它实例化: from transformers import AutoModel, AutoModelForCausalLM raw_model = AutoModel.from_config(config) # 没带因果头 # raw_model = AutoModelForCausalLM...
fromtransformersimportAutoTokenizer,AutoModelForCausalLM,GenerationConfigimporttorchdevice=torch.device("cuda")iftorch.cuda.is_available()elsetorch.device("cpu")tokenizer=AutoTokenizer.from_pretrained('charent/Phi2-Chinese-0.2B')model=AutoModelForCausalLM.from_pretrained('charent/Phi2-Chinese-0.2B')....
fromtransformersimportAutoConfig,AutoModelForCausalLMmodel_name='togethercomputer/evo-1-8k-base'model_config=AutoConfig.from_pretrained(model_name,trust_remote_code=True)model_config.use_cache=Truemodel=AutoModelForCausalLM.from_pretrained(model_name,config=model_config,trust_remote_code=True, ) ...