使用python脚本下载模型首先要安装依赖,安装代码如下:pip install -U openxlab安装完成后使用 download 函数导入模型中心的模型。 from openxlab.model import download download(model_repo='OpenLMLab/InternLM-7b', model_name='InternLM-7b', output='your local path')...
四、本地化测试 from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("你的模型文件存储路径", trust\_remote\_code=True) model = AutoModel.from\_pretrained("你的模型文件存储路径", trust\_remote\_code=True).cuda() response, history = model.chat(tokenizer, ...
from huggingface_hub import snapshot_download model_path = "./download/" #模型下载路径,可以改为你自己的,你可以在colab上创建一个download文件夹,将模型下载于此 snapshot_download(repo_id="bert-base-uncased", local_dir=model_path) 填入前面获取的fresh_token from aligo import Aligo refresh_token = ...
将hugging face的权重下载到本地，然后我们之后称下载到本地的路径为llama_7b_localpath 【
现在指定local_dir就行了
I'm trying to run language model finetuning script (run_language_modeling.py) from huggingface examples with my own tokenizer(just added in several tokens, see the comments). I have problem loading the tokenizer. I think the problem is with AutoTokenizer.from_pretrained('local/path/to/director...
test_file = data_path + "test.json" data_files["test"] = test_file raw_datasets = load_dataset(extension, data_files=data_files) model.resize_token_embeddings(len(tokenizer)) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
config=BertConfig.from_pretrained(dir_path) config.update({'output_hidden_states':True}) # 这里直接更改模型配置 model= BertModel.from_pretrained(dir_path) 成功运行,下图给出对hugging face的模型文件的查找: --- 之所以python代码不能访问并下载huggingface.co的模型和不能web访问huggingface....
downloadmodel_dir=snapshot_download('ZhipuAI/ChatGLM-6B',cache_dir='/path/to/local/dir',...
1. Run: Saving to Local Disk ✅ pipe = pipeline( task="object-detection", model="microsoft/table-transformer-structure-recognition", ) pipe.save_pretrained("./local_model_directory") The following files are saved to./local_model_directory: ...