LLM Attention Visualization
I often need to convert times between time zones, so I built this tool to convert between them quickly and efficiently. 1. Add the cities you want to convert between (this is saved in the browser). 2. Drag on the map until the time on the source city is the time you want. 3. Look at the time at the other city. It's even more helpful to use on a phone since it doesn't require typing to convert between time zones or look up the time in another city (assuming you already added the cities you care about). If you need to convert a time far in the future, you can input the date and time in the…
LLM Attention Visualization launched on September 8, 2026 with 172 votes, #22 of 5,442 launches that month and more than 99% of that year's launches.
In plain words
LLM Attention Visualization is an interactive tool that shows how transformer-based language models work during text generation. Users can hover over or tap generated tokens to see which previous tokens influenced their creation through attention weights. The visualization simplifies the attention mechanism to help users understand how large language models selectively draw information from earlier tokens rather than treating all tokens equally. It's designed for anyone interested in understanding the internals of how these AI systems generate text.
written from the facts on this page · September 2026
From the sources
A visualization of the attention mechanism in LLMs.
One interesting thing about transformer-based large language models are that, during the generation phase, it is able to draw information from any of its previous tokens. But it needs to be selective; if every token affects the generation equally, it won't be very effective. This process needs a mechanism to decide how much a token affects the next token. You can tap or hover over any of the generated tokens to see the past tokens that affected* the generation. * "Affected" might not be fully accurate, as this visualization is highly simplified. It's calculating the attention weight, scaled by the magnitude of the value vector, aggregated across all attention heads, and summed across all…from ishamf.dev
In the maker’s words, at launch
I often need to convert times between time zones, so I built this tool to convert between them quickly and efficiently. 1. Add the cities you want to convert between (this is saved in the browser). 2. Drag on the map until the time on the source city is the time you want. 3. Look at the time at the other city. It's…
Pricing, as stated on its site
Free — LLM Attention Visualization is a free tool with no pricing information provided. It appears to be offered at no cost to users.
checked September 2026 · prices change
Does a similar job
all alternatives →- FCFully client-side GPT2 prediction visualizer2023 · perplexity.vercel.app · ▲153
Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
- WVWe've open-sourced our LLM attention visualization library2024 · github.com · ▲197
Inspectus allows you to create interactive visualizations of attention matrices with just a few lines of Python code. It’s designed to run smoothly in Jupyter notebooks through an easy-to-use Python API. Inspectus provides multiple views to help you understand language model behaviors. If you have any questions, feel free to ask!
- CMCreate mind maps to learn new things using AI2024 · github.com · ▲170
Enter a topic and get a learning mind map generated by an LLM with links to learn more about each subtopic. You can use it with local models (through Ollama) or external models. If you have any feedback, please share it! Hope it's useful Demo: https://youtu.be/Y-9He-tG3aM
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