from huggingface_hub import snapshot_download model_name = input("HF HUB 路径,例如 THUDM/chatglm-6b-int4-qe: ") model_path = input("本地存放路径,例如 ./path/modelname: ") snapshot_download( repo_id=model_name, local_dir=model_path, local_dir_use_symlinks=False, revision="main", ...
hfd<repo_id> [--include include_pattern] [--exclude exclude_pattern] [--hf_username username] [--hf_token token] [--tool aria2c|wget] [-x threads] [--dataset] [--local-dirpath] Description: Downloads a model or dataset from Hugging Face using the provided repo ID. Parameters: rep...
下载你需要的模型 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...
model = "Qwen/Qwen-14B-Chat-Int4" #"Qwen/Qwen-7B-Chat-Int4" #"Qwen/Qwen-7B-Chat" url = 'https://huggingface.co/'+model+'/tree/main' # 替换为要分析的网页URL https://huggingface.co/gpt2/tree/main/onnx startString = "/"+model+"/resolve" pathString = "/"+model+"/tree/main/...
model = GPT2LMHeadModel.from_pretrained(model_path) # 定义输入文本 input_text = "Once upon a time, in a land far, far away, there was a kingdom full of" # 编码输入文本 inputs = tokenizer(input_text, return_tensors='pt')
python download-model.py bigcode/starcoder Screenshot No response Logs File “/home/ahnlab/GPT/text-generation-webui/download-model.py”, line 102, in get_download_links_from_huggingface r.raise_for_status() File “/home/ahnlab/miniconda3/envs/vicuna/lib/python3.11/site-packages/requests/mod...
model_name='bert-base-uncased'file_path=download_large_file(model_name)print(f"文件已下载至:{file_path}") 1. 2. 3. 4. 5. 6. 7. 8. 9. 在这个示例中,我们定义了一个download_large_file函数来下载指定名称的模型文件。我们选择了一个常用的预训练模型bert-base-uncased进行演示,你可以根据实际...
# os.environ['HF_ASSETS_CACHE']=MODEL_DIR # CHATTTS_DIR = huggingface_hub.snapshot_download(cache_dir=MODEL_DIR,repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"]) # chat = ChatTTS.Chat() # chat.load_models(compile=True if os.getenv('compile','true').lower()!='...
# 加载预训练字典和分词方法 tokenizer = BertTokenizer.from_pretrained( pretrained_model_name_or_path = 'bert-base-chinese', cache_dir=None, force_download=False, ) sents = [ '选择珠江花园的原因就是方便。', '笔记本的键盘确实爽。', '房间太小。其他的都一般', '今天才知道这书还有第6卷,真...
These modelshave an interesting feature. They run well on the cloud platform, but once you want to run them locally, you have to struggle. You can always see user feedback in the GitHub associated with the project: this model and code , I can't run it locally, it's too troublesome ...