This documentation describes the integration of MindsDB withHugging Face Inference API. The integration allows for the deployment of Hugging Face models through Inference API within MindsDB, providing the models
Read the Hugging Face Serverless Inference API documentation to learn more. Connect Hugging Face to your other apps Let's get the bad news out of the way: technical skills are required to use everything Hugging Face has to offer. But you can use Zapier's Hugging Face integration to send...
📖 Officialdocumentation Hugie is a Command Line Interface (CLI) for working with the Huggingface Inference Endpoints API (API docs) Getting started The package is pip installable and can be installed from PyPI pipx install hugie ⚠️To get started, you must set your individual or organisati...
7.API Inference:通过推理API可以使用超过30k个模型,并且内置可伸缩性。 8.Accelerate:使用multi-GPU,TPU,混合精度容易的训练和使用PyTorch模型。 9.Amazon SageMaker:使用Amazon SageMaker和Hugging Face DLCs训练和部署Transformer模型。 10.Optimum:容易的使用硬件优化工具,来实现HF Transformers的快速训练和推理。 11.Co...
7.API Inference:通过推理API可以使用超过30k个模型,并且内置可伸缩性。 8.Accelerate:使用multi-GPU,TPU,混合精度容易的训练和使用PyTorch模型。 9.Amazon SageMaker:使用Amazon SageMaker和Hugging Face DLCs训练和部署Transformer模型。 10.Optimum:容易的使用硬件优化工具,来实现HF Transformers的快速训练和推理。
Hi there, I'm Célina from 🤗, This PR introduces support for Hugging Face's serverless Inference Providers (documentation here), allowing users to specify different providers for chat completion and...
If you saved your model to W&B Artifacts withWANDB_LOG_MODEL, you can download your model weights for additional training or to run inference. You just load them back into the same Hugging Face architecture that you used before. # Create a new runwithwandb.init(project="amazon_sentiment_an...
7.API Inference:通过推理API可以使用超过30k个模型,并且内置可伸缩性。 8.Accelerate:使用multi-GPU,TPU,混合精度容易的训练和使用PyTorch模型。 9.Amazon SageMaker:使用Amazon SageMaker和Hugging Face DLCs训练和部署Transformer模型。 10.Optimum:容易的使用硬件优化工具,来实现HF Transformers的快速训练和推理。
Explore the basics of Inference API on the Hugging Face Create Spring Boot project Develop a simple Java client, which will help us to establish connection with the Inference API Pick a model Develop a small piece of the REST API And finally, test all that with some REST Client. ...
The image being used is the same one provided during the illustration from the Hugging Face portal. Make sure to replace the “xxx” with the actual values. import requests API_URL = "https://api-inference.huggingface.co/models/zoumana/beans_health_type_classifier" headers = {"Authorization"...