Hugging Face is a powerful open-source platform that has gained widespread recognition in the AI and ML communities for its flexibility, robustness, and community-driven approach. Founded in 2017 as a chatbot application aimed at teenagers, Hugging Face has since transformed into a cutting-edge platform that provides a wide range of tools and services for AI development, training, and collaboration. With its user-friendly interface and extensive capabilities, Hugging Face is revolutionizing the way AI models are created and shared. Whether you're a seasoned expert or just starting out in the field, Hugging Face offers something for everyone. In this article, we'll take a closer look at the platform's offerings and explore how it's transforming the AI ecosystem.
Following are the contents of the workshop on the Hugging Face Echo System.
The Hugging Face Ecosystem offers a comprehensive set of features:
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Access Tokens GenerationGenerate Read/Write access tokens for enhanced functionality Access Tokens.
Navigate documentation on Transformers, Datasets, PEFT, Tokenizers, Inference API, and Gradio. See the Hugging Face Docs.
Explore NLP tasks, models like gpt2, Llama-2-7b-chat-hf, and more Text Generation. Dive into discussions on Model Cards, Files and Versions, Training, Deployment, and Usage in Transformers. Access tutorials, including deploying LLM in the Hugging Face Inference Endpoint Notebooks.
Engage with AI applications like Gemini Playground and GPT4 UI. Learn to create a new space with a demonstration of a Streamlit Application.
Get a brief overview of NLP tasks, especially Text Generation.
Access top AI chat platforms, including Llama-2-7b-chat-hf, falcon-180B-chat, and CodeLlama-34b-Instruct-hf. See the Hugging Chat.
Explore educational resources like the NLP Course, stay updated through the NLP blog, and collaborate on GitHub through the Hugging Face repository.
In this section, we will have a hands-on overview of finetuning an LLM model with Custom dataset and deploying on huggingface Hub.
This notebook will give participants a hands-on overview of finetuning the GPT2 model with custom data using PEFT techniques.
Once the model is finetuned, we create the Model Inference using Hugging Face Inference API.