Getting Started with Hugging Face Inference Endpoints Published October 11, 2022. Update on GitHub
$ git clone https://github.com/huggingface/optimum-graphcore.git $ cd optimum-graphcore/examples/question-answering Now, we will use run_qa.py to fine-tune the IPU implementation of BERT on the SQUAD1.1 dataset.Run a sample to fine-tune BERT on SQuAD1.1...
spotlight.showopens up spotlight in the browser with the pandas dataframe ready for you to explore. Thedtypeargument specifies custom column types for the browser viewer. Load aHugging Facedataset Huggingface datasets have a rich semantic description of the feature columns. Spotlight can thus par...
pip install git+https://github.com/huggingface/optimum-intel.git pip install git+https://github.com/openvinotoolkit/nncf.git pip install openvino-nightly Running Phi-3 with OpenVINO Like NPU, OpenVINO completes the call of generative AI models by running qua...
Mixed Precision with HuggingFace and other sources Reduced precision math mode via environment variable (introduced in TensorFlow 2.13) This guide describes the options in details. I. Keras mixed precision API Introduction This session describes how to use the Keras mixed precision API to speed up yo...
github地址:https://github.com/liaokongVFX/LangChain-Chinese-Getting-Started-Guide 因为langchain库一直在飞速更新迭代,但该文档写与4月初,并且我个人精力有限,所以colab里面的代码有可能有些已经过时。如果有运行失败的可以先搜索一下当前文档是否有更新,如文档也没更新欢迎提issue,或者修复后直接提pr,感谢~ ...
parser.add_argument( "--train_data", type=str, default=os.environ["SM_CHANNEL_TRAIN"]) parser.add_argument( "--max_length",type=int,default=128) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface...
For example, google/gemma-2b-it at main (huggingface.co). Build the engine with TensorRT-Cloud. trt-cloud build llm --hf-repo="google/gemma-2b-it" --dtype="bfloat16" --gpu RTX4090 --os windows Unzip the downloaded file. The TensorRT engine is saved in build_result/engine. The ...
!huggingface-cli login --token $secret_hf Powered By Loading the Dataset We will load only the first 1000 rows of the dataset. This will reduce the training time and provide us with basic results. #Importing the dataset dataset = load_dataset(dataset_name, split="train[0:1000]") dataset...
:param model: Huggingface OCR model. Returns: generated_text: the OCR'd text string. """ # We can directly perform OCR on cropped images. pixel_values = processor(image, return_tensors='pt').pixel_values.to(device) generated_ids = model.generate(pixel_values) generated_text = processor....