您可以将 from 和 pretrained 之间的空格去掉，修改为 from_pretrained。Exporter.from model(model) 应为 Exporter.from_model(model)：在导出模型时，Exporter.from model(model) 应为 Exporter.from_model(model)，否则会导致语法错误。您可以将 from 和
from_pretrained("iic/nlp_structbert_zero-shot-classification_chinese-base") # 设置任务参数 task_config = { "task": "zero-shot-classification", "candidate_labels": ["体育", "科技", "娱乐"], } # 应用预处理器 inputs = preprocessor(task_config, text="这是一个关于科技的文章") outputs = ...
( 'AI-ModelScope/bert-base-uncased', revision='v1.0.0') tokenizer = AutoTokenizer.from_pretrained( 'AI-ModelScope/bert-base-uncased', revision='v1.0.0') lora_config = LoRAConfig(target_modules=['query', 'key', 'value']) model = Swift.from_pretrained(model, model_id='./outputs/...
首先,导入相关的库和模块: importtorchfrommodelscopeimportModelfrommodelscope.pipelinesimportpipeline 1. 2. 3. 2. 下载模型 接下来,使用ModelScope提供的API下载所需的模型。例如,我们选择一个预训练的图像分类模型: model=Model.from_pretrained("modelscope/imagenet_resnet50") 1. 3. 创建推理管道 通过pipel...
model = AutoModelForCausalLM.from_pretrained("./AI-ModelScope/CodeLlama-7b-Instruct-hf", pad_token_id=tokenizer.eos_token_id).to(device) prompt_text = "How are you ?" input_ids = tokenizer.encode(prompt_text, return_tensors="pt").to(device) ...
from_pretrained('modelscope/Llama-2-7b-ms', device_map='auto') lora_config = LoRAConfig(target_modules=['q_proj', 'k_proj', 'v_proj']) model: SwiftModel = Swift.prepare_model(model, lora_config) # Do some finetuning here model.save_pretrained(tmp_dir) push_to_hub('my-group/...
996 return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs) 997 elif "model_type" in config_dict: --> 998 config_class = CONFIG_MAPPING[config_dict["model_type"]] 999 return config_class.from_dict(config_dict, **unused_kwargs) ...
from modelscope import AutoModel, AutoTokenizer model_id = 'some_pretrained_model_id' # 替换为实际的预训练模型ID tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModel.from_pretrained(model_id) 5. 如果问题依旧存在 搜索相关错误信息:在互联网上搜索你遇到的错误信息,看看是否有其他人...
model=AutoModelForSequenceClassification.from_pretrained('AI-ModelScope/bert-base-uncased',revision='v1.0.0')tokenizer=AutoTokenizer.from_pretrained('AI-ModelScope/bert-base-uncased',revision='v1.0.0')lora_config=LoRAConfig(target_modules=['query','key','value'])model=Swift.from_pretrained(model...
model_dir=r'chatglm3-6b'tokenizer=AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)model=AutoModel.from_pretrained(model_dir,trust_remote_code=True).half().cuda()model=model.eval() 静候佳音 image.png 3.圆圈转完了,和它交流一下!