EDGE · WEBGPU · 0.92 MB
A 5.94M-parameter span encoder that answers the fragment you highlight — in the page, on your GPU, with no server in the loop.
● RECONCILING · 110 EDGES
5.94M
PARAMS
8192
BYTE-BPE
6L · 256d
ENCODER
0.92MB
TARGET DISK
<10ms
PER REACTION
0
API CALLS
01 — REACT
The selection is the input. The bubble is the output. Nothing leaves the tab.
SELECT ANY FRAGMENT ABOVE ↑ · INTERACTION PREVIEW, CANNED REACTIONS
INPUT
span + [S] markers + ±150 tokens of context
OUTPUT
≤ 40 tokens, one bubble, one pass
WHY NOT SUMMARISE
Shortening text you already chose to read is not a feature. Explaining what you didn't understand is.
02 — MODEL
A bidirectional pre-norm trunk. One head points at text already on the page. One writes. The trunk is the same either way, so the output head stays a swappable fork.
D_MODEL
256
EMBED RANK
128, factorised
HEADS / D_FF
4 / 1024
MAX SEQ
256
NORM / ACT
LayerNorm pre-norm / GELU
POSITIONS
sinusoidal · 0 bytes
COMPUTE / PASS
≈ 2.5 GFLOP @ 192 tok
FORMAT
.fzm · per-group affine q4
03 — SIZE
Trained from scratch, so the aggressive compression is available from step zero — not bolted on after. Under a megabyte means the weights ship inside the app. No download, no CDN, no cold start.
fp32 baseline
q4 everywhere · shipping today
q4 embed + 1.58-bit encoder
+ 2 shared layers × 3
3.65MB
CHECKPOINT ON DISK NOW
~20ms
LAPTOP iGPU, PER REACTION
f32
DEQUANTISED AT LOAD · NO NEW KERNELS
04 — EDGE
Not a degraded fallback for when the network is down. The model is built to live on devices and edge clusters, reached through WebGPU — a browser API, not a vendor endpoint. Nothing here is lockable behind someone's backend.
01 · NO BACKEND LOCK-IN
Weights bundled, runtime is forge compiled to WASM + WGSL. The only dependency is a GPU the browser already exposes.
02 · FANS OUT TO A CLUSTER
At 0.92 MB and 2.5 GFLOP a reaction, one model instance per node is free. Scale is replication, not routing.
03 · RECONCILES, DOESN'T CALL
Edges report observed task status upward; the centralised network holds the expected state and streams the delta back down. Convergence is continuous and asynchronous — the reaction never waits on it.
04 · DEGRADES HONESTLY
Offline, an edge keeps reacting from the weights it has. The loop resumes when it can reach the centre again.
05 — STATUS
No pretrained checkpoint, no fine-tune of someone else's model. Tokenizer, corpus, encoder and training loop are all in this repo, and stage A is running on a single A5000.
CORPUS
12.3B tok
FineWeb sample-10BT → byte-BPE 8192
MLM LOSS
20.9 → 3.55
unigram floor 7.29 nats
THROUGHPUT
29.7k tok/s
A5000, batch 32 × 256, masked-row head
HARDWARE
1 GPU
24 GB, 2.5 GB used at this batch
P0a
Encoder + pointer head in Rust on forge, .fzm round-trip
DONE
P0b
Byte-level BPE tokenizer + FineWeb preprocessing
DONE
P2
MLM pretraining, batched autograd on WebGPU/Vulkan
RUNNING
P1
Bubble + offscreen WebGPU runtime on real pages
NEXT
P3
Selection mining + teacher labelling, train Head A
PLANNED
P5
Head B copy-decoder · ternary QAT · cross-layer sharing
PLANNED