soup_30 extends v45 with targeted training data for five failure modes (multi-number sentences, year-context drift, disconnected number lists, within-input duplicates, long-form preservation), each generated programmatically and audited with a Qwen3.6-27B judge.

Metric	v45	soup_30
Number accuracy	95.9%	96.5%
Adversarial benchmark (greedy)	76%	86%

See the bf16 model card for the full pipeline and benchmark numbers.

Quantization Recipe

bash

mlx_lm.convert \
  --hf-path juanquivilla/sotto-cleanup-lfm25-350m \
  --mlx-path sotto-cleanup-lfm25-350m-mlx-5bit \
  -q --q-bits 5 --q-group-size 64 \
  --trust-remote-code

Usage

python

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit")
sampler = make_sampler(temp=0.0)

text = "talk about server three sixty"
prompt = f"### Input:\n{text}\n\n### Output:\n"
output = generate(model, tokenizer, prompt=prompt, max_tokens=512, sampler=sampler)
if "###" in output:
    output = output[:output.index("###")].strip()
print(output)

License

MIT