Sign in Sign up huggingface / diffusers Public Notifications Fork 4.3k Star 20.8k Code Issues 278 Pull requests 99 Discussions Actions Projects Security Insights New issue Do from_config and from_pretrained Generate the Same DDIMScheduler?#6253 Open jundanl opened this issue Dec 20, ...
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pip install git+https://github.com/huggingface/transformers.git !pip install bitsandbytes Share Improve this answer Follow answered Mar 31 at 14:17 Lompo Jean Olivier 1122 bronze badges Add a comment 0 Install from the original github repository pip install git+https://github....
它是huggingface发布的库,可以快速访问文本,图像和音频(从hugs的API下载)的机器学习模型。它还提供训练和微调模型的功能,并可以HuggingFace模型中心共享这些模型。库没有像Pytorch或Tensorflow那样从头开始构建神经网络的抽象层和模块,它提供了专门针对模型进行优化的训练和推理api。transformer是用于LLM微调的关键Python库之一,...
I fixed the issue with this pull request:https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized/discussions/5 However, when I save my fine-tuned model, the config.json file doesn't point to that specific pull request, pointing instead to the main branch by defau...
(args.in_huggingface) model_config.output_hidden_states = args.output_hidden_states model = BertModel.from_pretrained( args.in_huggingface, config=model_config ) tokenizer = BertTokenizer.from_pretrained(args.in_huggingface) else: model_config = BertConfig.from_pretrained(ar...
(training_args.output_dir, "trainer_state.json")) # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) tokenizer.save_pretrained(training_args.output_dir) # Evaluation if traini...
Hi I would like to use T5 untrained here is the command I try: model = T5ForConditionalGeneration.from_config(config=config) I am getting this error, could you assist me please? thank you Looks weird error to me, since from_config should work based on documentations. thanks File "finetu...
from huggingface_hub import hf_hub_download from transformers import PretrainedConfiglogger_initialized = {}@@ -255,3 +256,75 @@ async def __tmp(): return __func_warpperreturn __inner# copy from https://github.com/vllm-project/vllm/blob/0650e5935b0f6af35fb2acf71769982c47b804d7/vllm/...
After a bit of googling I found that the issue #1714 from huggingface's github already had "solved" the question. When I try the to run the propose solution : from transformers import GPT2Config, GPT2Model NUMLAYER = 4 NUMHEAD = 4 SIZEREDUCTION = 10 #the factor by which we reduce ...