title	TAF Agent
emoji	🔬
colorFrom	blue
colorTo	green
sdk	static
pinned	true
license	apache-2.0
short_description	Test any transformer LLM in browser before spending GPU/$.
tags	transformer llm diagnostic rope kv-cache long-context viability thermodynamics free browser webgpu
language	en es fr zh

🔬 TAF Agent

Test ANY transformer LLM before you spend GPU/$. Free. Unlimited. Auditable. Runs entirely in your browser.

🌐 Live: https://karlesmarin.github.io/tafagent 📦 Source: https://github.com/karlesmarin/tafagent 📄 Paper: Predicting How Transformers Attend — Marin 2026 🗂️ Dataset: taf-attention-decay (58 measurements, 32 models)

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A note before you read on

This tool was built by one independent researcher, with no funding, no team, no GPUs beyond a single consumer card, and the full collaborative help of large language models as research instruments. It exists because the paper it complements (Predicting How Transformers Attend — Marin 2026) needed a way for any reader to check the framework's predictions on their own model in seconds, without installing anything, without paying anyone, and without trusting a server they don't control.

If it is useful to you — even once — that is enough. If it is wrong about your model, please tell us so we can fix the framework. The point is the common ground, not the artefact.

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What it does

Drop in a model id (or paste any HuggingFace public model), get a falsifiable answer to "will this work?" — backed by the Thermodynamic Attention Framework (TAF) formulas:

Decision recipes

• Will Llama-3-8B serve 32K context with NIAH retrieval? → X-2
• Should I train a custom 7B model or pay for API access? → X-1
• I have $5,000 — what model can I afford to train? → X-3
• Cheapest GPU to serve Llama-70B at 100M tokens/day? → X-5
• Soft KV decay or hard cutoff for compression? → X-19

Diagnostic recipes (NEW v0.4 — sesión 29 findings 2026-04-28)

• How much positional bias did training imprint on this model? → X-21
• Does this model fit the empirical compute-context invariant band? → X-22
• Is this checkpoint pre- or post-induction-head? → X-23

Each as a chain of TAF formulas (paper §17, §19, §20, §24, §26, §28-§30) rendered with full audit trail. Every number is deterministic Python; nothing is hallucinated.

Four ways to use it

• 📇 Profile a model — paste id, get all 5 recipes scored as a unified TAF Card (best starting point)
• 🆚 Compare models — 2-3 candidates side-by-side on the same recipe
• 💬 Ask plain English — free-form question, in-browser LLM picks the right recipe
• 📋 Pick recipe — manual selection with full form control

How it stays free + unlimited

• Static HTML/JS hosted on GitHub Pages (truly unlimited bandwidth)
• Python TAF computation runs in your browser via Pyodide (no server-side compute)
• Plain-English synthesis runs Qwen2.5-0.5B-Instruct in your browser via WebLLM (your GPU/CPU, your electricity, ~350MB cached after first load)
• Model config.json files fetched directly from HuggingFace Hub (free, public, no auth for non-gated models)
• Your data never leaves your browser

If 1 user or 1 million users hit it, our cost stays the same: $0.

Architecture coverage

Supports any model whose config.json is parseable:

Family	Examples	Status
RoPE-MHA	pythia, gpt-j, original LLaMA	✓ supported
RoPE-GQA	Llama-3, Mistral, Qwen2.5, gemma-2	✓ supported
ALiBi	BLOOM, Falcon	✓ supported
AbsPE	gpt2 family	✓ supported
SWA (sliding window)	Mistral, gemma-2, phi-3	✓ supported
SSM	Mamba, Mamba-2	✓ partial (γ doesn't apply, KV does)
Any HF Hub public model	(any)	✓ via 📥 Fetch button

Languages

Interface available in:

• 🇬🇧 English
• 🇪🇸 Español
• 🇫🇷 Français
• 🇨🇳 中文

Click flags top-right to switch.

Local development

Browser application

git clone https://github.com/karlesmarin/tafagent
cd tafagent
python -m http.server 8000
# open http://localhost:8000

CLI diagnostic (for the paper)

The directory cli/diagnose_model.py is the command-line companion described in the paper Predicting How Transformers Attend (Marin 2026). It characterises any causal language model from HuggingFace in minutes on CPU and produces the raw gamma_obs, R², and thermodynamic profile used in the manuscript.

pip install torch transformers numpy
python cli/diagnose_model.py --model EleutherAI/pythia-2.8b --fast --cpu

Reproducibility data

The directory data/ ships every measurement referenced in the paper (343 JSON files, ~5.5 MB). See data/README.md for the layout.

Browser requirements

• Chrome / Edge / Firefox 113+ for WebGPU acceleration (recommended)
• Older browsers fall back to CPU inference (slower but works)
• ~2 GB free RAM for the synthesis LLM
• ~350 MB disk for model cache (one-time)

What's new in v0.4 (2026-04-28)

Three new diagnostic recipes derived from cross-model panel analysis (n=22 LLMs):

X-21 — Imprint Purity Diagnostic

Predicts γ on RANDOM-token input via the learned-imprint formula:

γ_random = γ_pade(θ, T) + ν · log_10(P / 14M)
   ν = −1/(2π) ≈ −0.1592   (DERIVED from RoPE rotation period)

Even on random tokens, weights apply a learned positional bias proportional to log(N_params). The slope ν is fixed (not fitted) — derivable from RoPE's 2π rotation period. Empirical validation: n=22 LLMs, p=0.022, |err|=0.3%.

Use case: detect anomalous training, format conversion (e.g. OLMo native vs HF Δγ=0.30), or fine-tuning drift by comparing predicted vs measured γ_random.

X-22 — Compute-Context Invariant

Computes the empirical Chinchilla×attention invariant:

K = γ × log(N² · D)   where D = 20·N (Chinchilla compute-optimal)
Empirical band: K ∈ [34, 68]   (51.2 ± 16.8, CV=0.329, n=22)

K-outliers indicate scaling/training anomalies. Llama-3-8B with γ=1.045 gives K=74.6 (z=1.39, high-K OUTLIER) — flags supra-Padé attention.

X-23 — IH-Phase Detector

Uses the Δγ probe (cheaper than ICL benchmark):

sign(γ_text − γ_random) > 0   ⟺   post-induction-head formation

Pre-IH (P<400M, n=7): ⟨Δγ⟩=−0.19±0.26 Post-IH (P≥400M, n=15): ⟨Δγ⟩=+0.03±0.26

Use case: monitor training trajectories without running ICL benchmarks; detect anomalous checkpoints.

Other v0.4 additions

• gamma_decompose_v2(...) — 6-axis decomposition with the new imprint axis
• famous_constant_proximity(...) — detects γ-cluster on famous constants (e.g. CodeLlama-13b γ=0.382 ≈ 1−1/φ golden conjugate)

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What's new in v0.5 (2026-05-01) — 🔬 Machine-verified consistency

First transformer-attention framework with formal machine-proof backing.

Sage Groebner basis (algebraic decision procedure) + Lean Mathlib4 (dependent type theory) dual-tool verification of 15 algebraic identities of TAF critical exponents.

verify_algebraic_consistency(γ) — new function

Given measured γ ∈ Phase A (0,1), checks 12 D-SAGE identities derived from TAF exponents (β=γ−1, ν=1/(1−γ), η=γ−1, etc.):

• D-SAGE-1 (★★ core): 2η² + η·γ_χ + 1 = 0 (quadratic identity)
• D-SAGE-2: β·χ = −1 (Phase A)
• D-SAGE-4: α + χ = 2
• D-SAGE-5: α + γ_χ = 2(2 − γ)
• D-SAGE-6: β·γ_χ = −2γ² + 4γ − 3 (factored)
• Rushbrooke + Josephson tautologies (d=1)
• Fisher residual = γ(2γ−3)/(1−γ) (NOT zero generally; corrects "triple closure")
• η=2γ refutation (Phase A residual > 0; paper 1's claim was wrong)
• D-SAGE-7: c · |ν_imprint| · 2π = 3 (dimensional closure)

Pass = framework intact. Fail = bf16 outlier, quantization artifact, or γ measurement noise.

Paper 1 erratum

Paper 1 originally claimed η = 2γ. Sage Groebner + Lean Mathlib4 detected this is algebraically wrong (residual (−4γ³+5γ+1)/(1−γ) > 0 ∀γ ∈ Phase A). Correct value: η = γ − 1, satisfying D-SAGE-1.

Reproducibility

# Sage verification
docker run --rm -v "$(pwd)/analysis:/work" sagemath/sagemath:latest \
    sage /work/sage_recursive_sweep_2026-04-30.sage

# Lean verification
docker run --rm -v "$(pwd)/lean_taf:/work" \
    leanprovercommunity/lean:latest \
    -c "cd /work/taf && lake build"

Build success: 1973/1973 jobs (Mathlib4 + 15 TAF theorems), DONE_EXIT=0.

Lean code: lean_taf/taf/Taf/Identities.lean Sage script: analysis/sage_recursive_sweep_2026-04-30.sage

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How you can help

This tool is at v0.5. There's a long way to go.

• 🐛 Report bugs: https://github.com/karlesmarin/tafagent/issues
• 🌐 Translate: add a language to js/i18n.js, send a PR
• 🧪 Falsify a prediction: run the tool on a model where you have ground-truth measurements; if our verdict disagrees with reality, open an issue. We take refutations as seriously as confirmations.
• ➕ New recipe: implement an X-N recipe in python/taf_browser.py following the pattern of X-1...X-19
• ➕ New preset: add a popular model to the PRESETS dict
• 📝 Improve docs / examples: anything that helps the next person

Citation

If this tool helps you — paper or code:

@article{marin2026Predicting How Transformers Atten,
  author  = {Marin, Carles},
  title   = {Predicting How Transformers Attend
Analytic Power-Law Theory, Phase Transitions, and Practical Compression
Tools},
  year    = {2026},
  url     = {https://zenodo.org/records/19826343},
}

@misc{marin2026tafagent,
  author = {Marin, Carles},
  title  = {{TAF Agent}: Browser-Based Transformer Diagnostic Tool},
  year   = {2026},
  url    = {https://karlesmarin.github.io/tafagent},
}

License

Apache-2.0 (this code).

Synthesis model: Qwen2.5-0.5B-Instruct distributed under Apache-2.0.

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Acknowledgements

This tool would not exist without:

• The model commons: EleutherAI, Meta AI, Alibaba Qwen team, Mistral AI, Google DeepMind, Microsoft Research, AI2, BigScience, TII, DeepSeek-AI, HuggingFace SmolLM team, the Mamba authors, the RWKV community, and OpenAI for releasing weights and configs publicly.
• The infrastructure commons: Pyodide, WebLLM, HuggingFace Hub, GitHub Pages, jsdelivr CDN.
• The maintainers of transformers, numpy, scipy, sympy, tokenizers, accelerate, and the dozens of small libraries that make modern ML possible.
• The wider ML community — bloggers, reproducibility checkers, Discord moderators, Stack Overflow answerers, blog post writers (Lilian Weng, Andrej Karpathy, Sebastian Raschka, Jay Alammar, Sasha Rush, Phil Wang, the EleutherAI team, and many more) whose explanations carried the author through every concept this tool uses.
• Large language models as research instruments — Claude (Anthropic), GPT (OpenAI), Gemini (Google DeepMind), Mistral, Llama, DeepSeek, Grok, Qwen-Chat, and Microsoft phi — for the symbolic derivations, sage cross-checks, prose revision, audit work, and long-form co-writing that underlie both this tool and the underlying paper.

The author was the hand that typed; the work itself belongs to the commons that made it possible.