HuggingFace: Tokenizer Arena, AutoQuizzer, PaliGemma
In this post, we’ll explore three cutting-edge tools that are making waves in the AI and machine learning community. First, dive into the Tokenizer Arena on HuggingFace, where you can compare and visualize tokenization processes across models like GPT-4, Phi-3, and Grok. This tool offers a unique insight into token counts, token IDs, and attention mechanisms, with bar plot comparisons that help you understand how different models handle text input. Next, discover AutoQuizzer, a space that automatically generates quizzes from any URL, allowing you to test your knowledge or let an LLM do the quiz for you, with options for both web browsing and “closed book” evaluations. Finally, explore PaliGemma, Google’s new open vision-language model, fine-tuned on a variety of tasks like question answering and image captioning. You can interact with these models directly, experimenting with text or image inputs. These tools provide powerful ways to engage with and understand the capabilities of today’s most advanced AI models.
Tokenizer Arena
After the Chatbot Arena, it’s the turn of the Tokenizer Arena to be trending on HuggingFace.
This space lets you compare and visualize the tokenizers of different models such as GPT-4, Phi-3 or Grok.
You can select multiple models, type a prompt and have a visualization of the number of tokens, the token IDs, and even the attention mechanism. A comparison of the different models is automatically generated in the form of a bar plot with a score for each model.
AutoQuizzer
This space lets you generate a quiz from a simple copy pasted URL. By feeding a URL, the model will automatically generate a quiz to let you test your knowledge and understanding.
You can play the quiz, or let the LLM play it.
The space also provides evaluations of LLMs playing the quiz, with both access to Web Browsing and in a “closed book” setting.
PaliGemma
PaliGemma is an open vision-language model that was recently unveiled by Google.
This space includes models fine-tuned on top of PaliGemma on a mix of downstream tasks, such as question answering, captioning and so on. The fine-tuning is performed on a series of academic datasets.
You can try out the models directly on this space by proving text or image inputs.
