franken_ocr weights

Quantized weight artifacts for franken_ocr — a pure-Rust, memory-safe, CPU-only OCR engine that runs a small family of hand-ported vision-language models with no general ML framework: no PyTorch, no Python, no CUDA, no FFI at inference, no GPU.

These are runtime artifacts in the project's own .focrq container, not Hugging Face transformers checkpoints. They are consumed by the focr CLI, the browser playground at franken-ocr.com, and the FrankenOCR iOS app. They will not load in transformers.

Why this mirror exists

GitHub release assets return 503 under load and cap a single asset at 2 GiB, which is why the largest artifact ships there as byte-split parts. This mirror serves the same bytes from a CDN, with ranged requests and CORS, so a browser can fetch a model directly and a phone can resume an interrupted download.

Every file is pinned by SHA-256 in models/manifest-v2.json and verified byte-for-byte before installation. A mismatched or truncated download can never hydrate.

Files

file	bytes	SHA-256	recipe
`unlimited-ocr.wasm-int4.focrq`	3,003,988,117	`2653831ccd7f481f898f80ae5c95fa1ec7ee2a5a18005d3c927ddf64ed75e187`	`unlimited-ocr-wasm-experts-int4-attn-int8-lmhead-int8-v1`
`tokenizer.json`	9,979,544	`a02f8fd5228c90256bb4f6554c34a579d48f909e5beb232dc4afad870b55a8b4`	—

`unlimited-ocr.wasm-int4.focrq`

Baidu Unlimited-OCR (a DeepSeek-OCR derivative: SAM-ViT-B → 16× conv token compressor → CLIP-L/14 → linear projector → 12-layer DeepSeek-V2 MoE decoder with Reference Sliding Window Attention) transformed from the 6.67 GB bf16 checkpoint to 3.00 GB:

MoE routed + shared experts → int4, group size 16 for gate_proj and down_proj, 32 for up_proj — the measured sensitivity order.
Attention q/k/v/o, lm_head, embed_tokens → int8 per-channel.
Vision tower, projector, MoE router gate, and all norms stay BF16. Quantizing the vision encoder wrecks OCR; every downstream token is conditioned on its 256 outputs, so error there is not local.

Quantization is calibration-aware (importance-weighted clip search plus an AWQ down_proj fold over a 13-page activation-statistics run), measured strictly better than plain round-to-nearest on every corpus page.

Honest accuracy note. int4 costs something real on dense material: on an 1886 newsprint page the measured character error rate is 0.156. Calibration bought a 10.9% relative reduction on the hardest page without regressing any page — a real improvement, not a fix. The per-page receipts live in docs/DISCREPANCIES.md.

This artifact is not the native CLI default. focr pull installs a separate conservative 4.16 GB artifact that keeps attention and lm_head at high precision. This one exists for memory-constrained targets: the browser, where the whole model must fit WebAssembly's 4 GiB linear memory, and phones.

Usage

# CLI
focr pull                      # installs the conservative native default
FOCR_MODEL_PATH=/path/to/unlimited-ocr.wasm-int4.focrq focr ocr page.png
focr ocr book.pdf --pages 3,5-9 -o excerpt.md

The tokenizer sidecar must sit beside the artifact.

Licenses

The weights are a separate matter. This artifact is a quantized derivative of Baidu's openly-licensed model, and its notice travels with it:

Baidu Unlimited-OCR — Copyright (c) 2026 Baidu, MIT License

The original checkpoint is baidu/Unlimited-OCR (source SHA-256 2bc48a7a110061ea58fff65d3169367eebe3aee371ca6968dc2219c1b2855fc6).

Not affiliated with Baidu

This is an independent reimplementation that runs Baidu's openly-licensed weights. No endorsement is implied.

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