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🔬 TAF Agent — predict transformer LLM viability before you spend GPU/$
Just shipped TAF Agent, a free browser-based diagnostic tool for transformer LLMs. No server, no auth, no cost. Runs entirely in your browser.
🌐 Try it: https://huggingface.co/spaces/karlexmarin/taf-agent 📦 Source: https://github.com/karlesmarin/tafagent 📄 Paper: Predicting How Transformers Attend
What it answers
- Will Llama-3-8B serve 32K context with NIAH retrieval? ← X-2 recipe
- Should I train custom or use GPT-4o for 50M tokens/month? ← X-1 recipe
- I have $5K — what model can I afford to train? ← X-3 recipe
- Cheapest GPU to serve Llama-70B at 100M tokens/day? ← X-5 recipe
- Soft KV decay or hard cutoff at 32K? ← X-19 recipe
5 cross-section recipes, 5 UI modes, 4 languages (EN/ES/FR/ZH).
Why it's different from "ask ChatGPT"
Every number is deterministic Python (the TAF formulas — closed-form, derivable from RoPE aliasing geometry). No hallucination. The synthesis LLM only reads the chain and writes plain English; it doesn't invent values.
The full computation chain is auditable per click — every step shows formula, inputs, output, paper section reference.
Architecture coverage
✓ RoPE-MHA · ✓ RoPE-GQA · ✓ ALiBi · ✓ AbsPE · ✓ SWA · ✓ SSM ✓ Any HuggingFace public model (paste model id, fetch config.json, profile)
How it stays free + unlimited
- Static HTML/JS on GitHub Pages (unlimited bandwidth)
- Python computation in your browser via Pyodide
- Plain-English synthesis via WebLLM (Qwen2.5-0.5B local, your GPU)
- Configs fetched directly from HF Hub
- Your data never leaves your browser
If 1 user or 1M users hit it, our cost stays at $0/month.
Built by an independent researcher
No funding, no team, no GPUs beyond a single consumer card. Built with the help of large language models as research instruments. Open source. Apache-2.0.
The tool exists because the paper it complements needed a way for any reader to check the framework's predictions on their own model in seconds.
Looking for
- 🧪 Falsifications: run TAF Agent on a model where you have real measurements. If our verdict disagrees, please open a refutation issue.
- 🌐 Translations: 4 languages so far. Add yours via PR (
js/i18n.js). - 💡 New recipes: we shipped 5 of 20 candidate recipes from the paper. Propose more in the registry.
- ➕ Model presets: 11 popular models curated. Add yours.
What this is NOT
- Not a benchmark (we predict from config, don't measure)
- Not a leaderboard (no ranking, just per-model viability)
- Not a replacement for actual evaluation — prediction before measurement
- Not a vendor pitch — there's nothing to buy, ever
The point is to give the community a free, auditable, falsifiable lens for evaluating transformer LLMs before spending compute on them.
If you find it useful even once, that's enough.
#transformer #llm #rope #diagnostic #free #opensource