lenslapse 0.1.2

In-browser logit lens over training checkpoints: conversion pipeline and local probe server

LensLapse ⧖

A fully in-browser time-lapse for the logit lens: scrub across Pythia's public training checkpoints and watch next-token predictions crystallize from noise into knowledge — layer by layer, with zero backend.

• Live demo: https://iamtatsuki05.github.io/lenslapse/ (works in any modern browser; WebGPU used when available, WASM otherwise)
• Project page: https://iamtatsuki05.github.io/lenslapse/about/
• Converted checkpoints (HF): https://huggingface.co/iamtatsuki05/lenslapse-onnx
• Demo video: https://www.youtube.com/watch?v=OBnvG9Ru3j8
• Documentation: https://iamtatsuki05.github.io/lenslapse/docs/ (guides + API reference)

Eleven shipped models, one architecture-generic recipe

Model	Params	Checkpoints	Languages
Pythia 14M / 70M / 160M	14M–160M	20 training steps each	English
GPT-2	124M	final checkpoint	English
MAP-Neo	250M	8 training steps (Hub subfolders)	Chinese/English
BAAI Aquila	135M	6 training steps (Hub subfolders)	Chinese/English
BLOOM	560M	8 training steps (global_step{N} revisions)	Multilingual (46 languages)
SmolLM2	135M	final checkpoint	English
Qwen3	0.6B	final checkpoint	Multilingual
OPT	125M	final checkpoint	English
Gemma 3	270M	final checkpoint	Multilingual

Switchable in the header, each labeled by its documented language support ("Multilingual", "Chinese/English", or untagged for English-only) and offering only the curated prompts it can actually handle. The recipe itself is architecture-generic (GPT-NeoX, GPT-2, Llama-style RMSNorm, Mistral-style RMSNorm, and Gemma-style plus-one-weight RMSNorm models all pass the parity check — see src/lenslapse/check_arch_parity.py); a layout the generic heuristic can't reach registers an explicit override in one line via register_architecture() (src/lenslapse/arch.py), without touching the resolver itself. opt-125m and gemma3-270m carry non-Apache-2.0/MIT licenses — see docs/model-card.md before redistributing or deploying them.

Highlights

• One-click figure export: the current view (grid + trajectory + metadata) downloads as a publication-ready PNG (3× pixel density) or PDF.
• Curated prompts are instant: logit-lens grids across training checkpoints are precomputed (fp32) and served as static JSON.
• Free-text prompts run live, in your browser or on a connected probe server: one click probes every checkpoint automatically (not just the current one), so the same ▶ playback that animates curated prompts works for anything you type, and comparing two models falls back to a live probe when one of them has no precomputed match for the prompt — your prompt never leaves your device unless a probe server is connected. MAP-Neo and Aquila's tokenizers rely on custom Python code with no browser-compatible fast-tokenizer equivalent, so free-text live probing those two specifically needs a connected probe server; their curated prompts are unaffected.

Quick start: probe your own models (no checkout needed)

pip install lenslapse
lenslapse server        # serves the web app AND the probe API on one local port, then opens it

Everything runs on http://localhost:8017/ — the UI is bundled into the package, so this works fully offline once models are downloaded, with no CORS or browser permission prompts. Click ⚙ models in the header, pick a Hugging Face id or press 📁 Browse… to choose a checkpoint folder with your OS's file dialog, and probe it live — no ONNX conversion, no config files. (In a checkout, uv run lenslapse server serves your own web/dist build instead; run scripts/bundle_webapp.sh after changing web/ to refresh the packaged shell.)

Why

• No public, hosted tool lets you interactively inspect a real LLM's internals across training time (Pythia ships 154 checkpoints, but existing views are loss curves and static galleries).
• No logit-lens tool of any kind runs fully client-side; hosted server-side demos rot when their backends die.
• LensLapse makes training time a first-class axis of token-level interpretability, and its zero-backend design means unlimited concurrent users at zero hosting cost — the demo cannot rot.

Architecture

Pythia checkpoint (HF Hub, revision step{N})
   └─ src/lenslapse/export_checkpoints.py
        ├─ backbone.f16.onnx   input_ids → hidden states [L+1, T, H]   (pre-ln, uniform; via forward hooks)
        └─ lens.f16.onnx       hidden [N, H] → logits [N, V]           (final_layer_norm + unembedding)
   └─ src/lenslapse/precompute_lens.py → static JSON shards (top-10 per cell + exact target trajectories)

web/ (Vite, TypeScript)
   ├─ precomputed mode: fetch JSON shard → canvas grid + SVG trajectories (no model download)
   └─ live mode: onnxruntime-web (WebGPU→WASM fallback) + @huggingface/transformers tokenizer

Key property: lens(hidden[-1]) == model logits exactly (validated per checkpoint at export). Weights are stored fp16 and cast to fp32 at session load; dynamic int8 was rejected because its final-layer top-1 agreement with fp32 drops to 52% (per-tensor; 71% per-channel) at late checkpoints (see src/lenslapse/fidelity_eval.py).

Advanced usage

See docs/advanced-usage.md for: local development setup, converting checkpoints & precomputing lens data, adding your own model (Hub or local), running the local probe server for heavy models, scripting every feature from the CLI, probe reproducibility, and benchmarking. The same guides plus a full API reference are rendered at https://iamtatsuki05.github.io/lenslapse/docs/.

Deploy (zero cost)

1. Upload the converted models to a public Hugging Face model repo and set HF_DEFAULT in web/src/live.ts.
2. npm run build, publish web/dist/ to GitHub Pages (workflow in .github/workflows/deploy-pages.yml).
3. There is no step 3 — no server, no keys, no bills. See docs/deployment.md.

License

MIT. Pythia checkpoints are © EleutherAI, Apache-2.0; GPT-2 weights are © OpenAI, MIT (Modified); MAP-Neo and BAAI Aquila checkpoints are Apache-2.0. BLOOM checkpoints are © BigScience Workshop, licensed under the BigScience RAIL License v1.0 — not a plain permissive license like the others here; it attaches use-based behavioral restrictions to downstream recipients. opt-125m (Meta's OPT-175B License Agreement) is non-commercial-research-only — no commercial use is permitted at all. gemma3-270m (Google's Gemma Terms of Use) permits commercial use but requires passing its Prohibited Use Policy on to downstream recipients and remains subject to Google's unilateral right to restrict or terminate use. See docs/model-card.md for the full attribution and license text for every model.