bash :export HF_HOME=/data/model_cache When I did not set the environment variable and downloaded the model to the root directory, I saw a progress bar indicating the download and installation process, and the used storage space increased as expected. After setting this variab...
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Just my /2c, one of the first things I expected the CLI to be able to do afterloginwas to be able to download a model; I was pretty surprised to find out it couldn't do it, and it felt like a weird oversight not to be included. This lead to me having to go down a google ...
I am looking at thisworkbookwhich comes fromhuggingfacecourse. I dont have internet access from my python environment but I could download files and save them in python environment. I copied all file from thisfolderand saved it in the folderbert-base-uncased/. I renamed some of the f...
Task specific parameters # wav2vec2-large-960h-lv60-self的特定参数return_timestampsgenerator=pipeline(model="facebook/wav2vec2-large-960h-lv60-self",return_timestamps="word")generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") ...
得到Meta AI的https://ai.meta.com/resources/models-and-libraries/llama-downloads/ 然后一个小时内会收到邮件 2. 然后是在huggingface上的授权,这里不知道具体怎么操作,但是我的点开https://huggingface.co/meta-llama/Llama-2-7b-chat-hf就有you have been granted access to this model 这句话,不知道是...
You can open a pull request to contribute a new documentation about a new task. Undersrc/taskswe have a folder for every task that contains two files,about.mdanddata.ts.about.mdcontains the markdown part of the page, use cases, resources and minimal code block to infer a model that bel...
As you can see, we assign more weight to theballword using a compel-specific syntax (ball++). You can use other libraries (or your own) to create appropriate embeddings to pass to the pipeline. You can read more details inthe documentation. ...
(output_dir="path/to/save/folder/",+ use_habana=True,+ use_lazy_mode=True,+ gaudi_config_name="Habana/bert-base-uncased",... ) # Initialize the trainer- trainer = Trainer(+ trainer = GaudiTrainer(model=model, args=training_args, train_dataset=train_dataset, ... ) # Use Habana ...
Create an object of your tokenizer that you have used for training the model and save the required files with save_pretrained(): from transformers import GPT2Tokenizer t = GPT2Tokenizer.from_pretrained("gpt2") t.save_pretrained('/SOMEFOLDER/') Output: ('/SOMEFOLDER/tokenizer_config.json'...