model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda() 也可以通过给 from_pretrained 传入 local_files_only=True 参数,同样可以达到从 cache 目录加载模型的目的。
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", ...
The issue only manifests if you're trying to load a local model and the model doesn't have the safetensors weights. Here is how to reproduce: @Narsilhi , could you please tell us more detail about how to mount the model locally? if the parameters are in ~/.cache/huggingface/hub/mo...
repo_type = "dataset", # 'model', 'dataset', 'external_dataset', 'external_metric', 'external_tool', 'external_library' repo_id="Hello-SimpleAI/HC3-Chinese",#huggingface网站上项目目录 local_dir="./HC3-Chinese",#缓存文件默认保存在系统盘\.cache\huggingface\hub\Hello-SimpleAI/HC3-Chinese 中...
When I use the model withtrust_remote_code=True, I cannot directly change these remote codes because everytime I load model it will request new codes from remote hub. So how can I avoid that ? Can I custom these codes in local?
我最近刚开始研究huggingface转换库。当我尝试开始使用模型卡代码时,例如community model from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained我觉得模型卡片表明这三行代码应该足以开始使用。 我使用的是Python3.7和转换器库版本2.1.1和pytorch1.5。
File "/usr/local/lib/python3.9/dist-packages/transformers/utils/hub.py", line 443, in cached_file raise EnvironmentError( OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like sentence-transformers/clip-ViT-B...
腾讯云大数据Elasticsearch Service在最近上线了8.8.1版本。该版本中的核心能力，是为AI革命提供高级搜索能力！该版本特别引入了Elasticsearch Relevance Engine™（ESRE™）—— 一款强大的AI增强搜索引擎，为搜索与分析带来全新的前沿体验。
{step}step_diffusers.safetensors"base="emilianJR/epiCRealism"# Choose to your favorite base model.adapter=MotionAdapter().to(device,dtype)adapter.load_state_dict(load_file(hf_hub_download(repo,ckpt),device=device))pipe=AnimateDiffPipeline.from_pretrained(base,motion_adapter=adapter,torch_dtype=...
模型套件中未正確指定或遺漏的權杖化工具可能會導致OSError: Can't load tokenizer for <model>錯誤。 遺漏程式庫 某些模型需要額外的 Python 程式庫。 您可以在本機執行模型時安裝遺漏的程式庫。 需要標準轉換程式庫以外之特殊程式庫的模型將會失敗,並顯示ModuleNotFoundError或ImportError錯誤。