HuggingFace Hello World!¶

Table of Contents¶

Overview
Applications
Tools & Frameworks
Hello World!
References

Overview¶

Hugging Face Platform is a collaboration platform for machine learning where the community can share, discover, and experiment with open-source models, datasets, and applications.

Applications¶

The platform hosts over 300k models, 100k applications, and 50k datasets in various domains such as natural language processing, computer vision, and speech recognition.
The platform allows users to easily host a demo app to show their machine learning work with Spaces, a web-based IDE for creating and deploying machine learning applications.
The platform also provides paid Compute and Enterprise solutions for deploying models on optimized inference endpoints, training models automatically with AutoTrain, and collaborating securely with enterprise-grade security and access controls.

Tools & Frameworks¶

The platform is built on top of the Hugging Face open source stack, which includes state-of-the-art libraries and frameworks for machine learning such as Transformers, Diffusers, SafeTensors, and Hub Python Library.
The platform also supports collaborative features such as pull requests, discussions, model cards, and versioning to improve the machine learning workflow and enable peer reviews on models, datasets, and Spaces.
The platform is compliant with GDPR and SOC 2 Type 2, and allows users to pick their storage region for compliance and performance.

Hello World!¶

Here is a code snippet that shows how to use the Transformers library to load a pre-trained model from the Hugging Face Hub and generate text with it:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load a pre-trained model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Encode some input text
input_ids = tokenizer.encode("Hello world!", return_tensors="pt")

# Generate text with the model
output_ids = model.generate(input_ids, max_length=20)

# Decode the output text
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)