Today, we’re diving into Hugging Face’s smolagents – a new development that gives AI models more autonomy. Hugging Face, the open-source AI powerhouse behind technologies like Transformers, has now turned its attention to AI agents – programs where AI models can plan and execute tasks on their own – and their latest library smolagents makes building these agents simpler than ever. In this short episode, I’ll break down what smolagents are, how they work, and why they’re a big deal for developers, businesses, and researchers alike.
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Large Language Model Leaderboards and Benchmarks
Llamas, Alpacas, Koalas, Falcons... there is a veritable zoo of LLMs out there! In today's episode, Caterina Constantinescu breaks down the LLM Leaderboards and evaluation benchmarks to help you pick the right LLM for your use case.
Caterina:
• Is a Principal Data Consultant at GlobalLogic, a full-lifecycle software development services provider with over 25,000 employees worldwide.
• Previously, she worked as a data scientist for financial services and marketing firms.
• Is a key player in data science conferences and Meetups in Scotland.
• Holds a PhD from The University of Edinburgh.
In this episode, Caterina details:
• The best leaderboards (e.g., HELM, Chatbot Arena and the Hugging Face Open LLM Leaderboard) for comparing the quality of both open-source and proprietary Large Language Models (LLMs).
• The advantages and issues associated with LLM evaluation benchmarks (e.g., evaluation dataset contamination is an big issue because the top-performing LLMs are often trained on all the publicly available data they can find... including benchmark-evaluation datasets).
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Jon’s “Generative A.I. with LLMs” Hands-on Training
Today's episode introduces my two-hour "Generative A.I with LLMs" training, which is packed with hands-on Python demos in Colab notebooks. It details open-source LLM (Hugging Face; PyTorch Lightning) and commercial (OpenAI API) options.
Read MoreNLP with Transformers, feat. Hugging Face’s Lewis Tunstall
Lewis Tunstall — brilliant author of the bestseller "NLP with Transformers" and an ML Engineer at Hugging Face — today details how to train and deploy your own LLMs, the race for an open-source ChatGPT, and why RLHF leads to better models.
Dr. Tunstall:
• Is an ML Engineer at Hugging Face, one of the most important companies in data science today because they provide much of the most critical infrastructure for A.I. through open-source projects such as their ubiquitous Transformers library, which has a staggering 100,000 stars on GitHub.
• Is a member of Hugging Face’s prestigious research team, where he is currently focused on bringing us closer to having an open-source equivalent of ChatGPT by building tools that support RLHF (reinforcement learning from human feedback) and large-scale model evaluation.
• Authored “Natural Language Processing with Transformers”, an exceptional bestselling book that was published by O'Reilly last year and covers how to train and deploy Large Language Models (LLMs) using open-source libraries.
• Prior to Hugging Face, was an academic at the University of Bern in Switzerland and held data science roles at several Swiss firms.
• Holds a PhD in theoretical and mathematical physics from Adelaide in Australia.
Today’s episode is definitely on the technical side so will likely appeal most to folks like data scientists and ML engineers, but as usual I made an effort to break down the technical concepts Lewis covered so that anyone who’s keen to be aware of the cutting edge in NLP can follow along.
In the episode, Lewis details:
• What transformers are.
• Why transformers have become the default model architecture in NLP in just a few years.
• How to train NLP models when you have few to no labeled data available.
• How to optimize LLMs for speed when deploying them into production.
• How you can optimally leverage the open-source Hugging Face ecosystem, including their Transformers library and their hub for ML models and data.
• How RLHF aligns LLMs with the outputs users would like.
• How open-source efforts could soon meet or surpass the capabilities of commercial LLMs like ChatGPT.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.