Minima Spellcheck

Packed W1.58A8 adaptation of LiquidAI/LFM2.5-Encoder-350M-Spellchecker, built with SSHDotCodes/minima.

Logical matrix values: {-1, 0, +1} (1.585 bits)
Physical artifact format: I2_S, four trits per byte
Group size: 32; recovery rank: 128
Full encoder context: 8,192 tokens
Weight file: 226.0 MB
CPU demo: Minima Spellcheck

Use

pip install "minima-lfm @ git+https://github.com/SSHDotCodes/minima.git"

from minima import MinimaModel

model = MinimaModel.from_pretrained("ProCreations/minima-spellcheck", device="cpu")
print(model.correct(
    ["I has went to the stor yesterday ."],
    max_iter=4,
    min_error_prob=0.0,
    rerank=False,
))
# ['I went to the store yesterday .']

CPU inference defaults to a one-time FBGEMM INT8 packing of each effective ternary-plus-recovery matrix. Set MINIMA_CPU_BACKEND=i2s to execute the strict 2-bit AVX2/NEON kernel instead.

Validation

The 1,000-step distillation run used held-out corrupted FineWeb text and the LiquidAI model as teacher. With both models' optional dense reranker disabled:

Diagnostic	Result
Tag top-1 agreement within teacher candidates	99.51%
Error-detection top-1 agreement	99.41%
Exact correction agreement, held-out + reference examples	70.0% (14/20)
Exact agreement on the four published-style examples	100% (4/4)

The Space smoke test corrected I has went to the stor yesterday . to I went to the store yesterday. and returned a warmed CPU latency of 110 ms. These are teacher-agreement diagnostics, not an ERRANT benchmark. The upstream optional reranker is deliberately disabled because it contains a separate 1.42 GB dense encoder, which would invalidate this model's CPU memory profile.

See spellcheck_report.json for the full immutable training and evaluation log.