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statim-decide-multilingual-base-pii

Zero-Shot Classification
GGUF
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lora
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pii
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metadata 
license: other
license_name: statim-weights
license_link: >-
  https://huggingface.co/Beko2210/statim-decide-multilingual-base-pii/blob/main/LICENSE-MODEL.md
base_model: Beko2210/statim-decide-multilingual-base
base_model_relation: adapter
library_name: gguf
language:
  - ar
  - de
  - en
  - es
  - fr
  - it
  - ja
  - nl
  - ru
  - sv
  - zh
tags:
  - statim
  - lora
  - adapter
  - gguf
  - pii
pipeline_tag: zero-shot-classification
model-index:
  - name: statim-decide-multilingual-base-pii
    results:
      - task:
          type: text-classification
        dataset:
          name: pii ar (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.9067
      - task:
          type: text-classification
        dataset:
          name: pii de (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.8667
      - task:
          type: text-classification
        dataset:
          name: pii en (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.9267
      - task:
          type: text-classification
        dataset:
          name: pii es (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.8933
      - task:
          type: text-classification
        dataset:
          name: pii fr (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.94
      - task:
          type: text-classification
        dataset:
          name: pii it (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.92
      - task:
          type: text-classification
        dataset:
          name: pii ja (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.9267
      - task:
          type: text-classification
        dataset:
          name: pii nl (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.8667
      - task:
          type: text-classification
        dataset:
          name: pii ru (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.9467
      - task:
          type: text-classification
        dataset:
          name: pii sv (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.8733
      - task:
          type: text-classification
        dataset:
          name: pii zh (n=150, seed=20260927, z=2.0, alpha=0.05)
          type: pii
        metrics:
          - type: accuracy
            value: 0.9467

Statim Decide Multilingual Base: PII adapter

A LoRA adapter that improves the PII decisions of Beko2210/statim-decide-multilingual-base 0.7.0. Checked with Statim 0.8.3: the published f32 file loads it merged at load, the q8_0 file as runtime LoRA. LoRA adapters need Statim 0.8.0 or later.

Quick start 

hf download Beko2210/statim-decide-multilingual-base statim-decide-multilingual-base-q8_0.gguf --local-dir models
hf download Beko2210/statim-decide-multilingual-base-pii statim-decide-multilingual-base-pii.lora.gguf --local-dir models
statim serve -m multilingual=models/statim-decide-multilingual-base-q8_0.gguf --adapter multilingual:pii=models/statim-decide-multilingual-base-pii.lora.gguf --port 8080

curl -s localhost:8080/v1/systemone -d '{"state": {"text": "Hi, this is Anna Schmidt. Call me back at +49 170 1234567."}, "questions": {"pii": {"type": "noul", "instructions": "Does the text contain a phone number?"}}, "adapter": "pii"}'
curl -s localhost:8080/v1/systemone -d '{"state": {"text": "Hi, this is Anna Schmidt. Call me back at +49 170 1234567."}, "questions": {"pii": {"type": "noul", "instructions": "Does the text contain a phone number?"}}, "adapter": "auto"}'

Python (Python SDK 0.8.3 or later):

from statim import Client
client = Client("http://127.0.0.1:8080")
client.decide({"text": "Hi, this is Anna Schmidt. Call me back at +49 170 1234567."}, {"pii": {"type": "noul", "instructions": "Does the text contain a phone number?"}}, adapter="pii")

Results (experiment's f32 run)

Each cell uses 150 items (seed 20260927).

Language Base Adapter Change (points) Verdict Qwen3-8B zero-shot
ar 0.8467 0.9067 +6.00 within noise 0.893
de 0.8400 0.8667 +2.67 within noise 0.893
en 0.8933 0.9267 +3.34 within noise 0.887
es 0.8533 0.8933 +4.00 within noise 0.867
fr 0.8933 0.9400 +4.67 within noise 0.880
it 0.8400 0.9200 +8.00 gain (2 SE) 0.873
ja 0.8467 0.9267 +8.00 gain (2 SE) 0.893
nl 0.7600 0.8667 +10.67 gain (2 SE) 0.800
ru 0.9067 0.9467 +4.00 within noise 0.947
sv 0.8333 0.8733 +4.00 within noise 0.800
zh 0.9000 0.9467 +4.67 within noise 0.920
Mean 0.8558 0.9103 +5.46 — 0.878

The pooled family change is +5.46 points with 2 SE = 2.22 points (gain).

A single cell of 150 items rarely clears 2 SE on its own; the decision uses the pooled family.

Decision rule: promote when the category family gains more than 2 standard errors, pooled by rows or by suites, and nothing regresses. A regression is a pooled drop beyond 2 standard errors under either pooling, or a drop in one language cell that stays significant after Holm-Bonferroni (family-wise 5 %).

Statim is trained on this category, while Qwen3-8B runs zero-shot.

Checked on the published files

Weights Adapter mode Base mean Adapter mean Change (points) Cell for cell as in the experiment
f32 merged at load 0.8558 0.9103 +5.46 yes
q8_0 runtime LoRA 0.8558 0.9109 +5.52 — (experiment: f32)

Training

Source Rows Licence
E3-JSI/synthetic-multi-pii-ner-v1 (default) 2,971 MIT
Wismut/nym-pii-multilingual-data (default) 6,200 MIT
gretelai/gretel-pii-masking-en-v1 (default) 6,200 Apache-2.0
gretelai/synthetic_pii_finance_multilingual (default) 6,200 Apache-2.0
nvidia/Nemotron-PII (default) 6,200 CC-BY-4.0
urchade/synthetic-pii-ner-mistral-v1 (data.json) 6,200 Apache-2.0

LoRA rank 16, alpha 32.0, dropout 0.05; target modules Wqkv, Wo, Wi; 88 wrapped modules and 3,379,200 trainable parameters.

  • Items: 33,571 train, 400 dev.
  • Updates: 1,172.
  • Dev accuracy: 0.8375 before, 0.8725 after.
  • Time: 1631.7 seconds; peak memory: 2,332 MB.

Provenance

  • Adapter GGUF SHA-256: 2991a33b5d9db4f5b679081885faab1f84289d6eae7660830447e790bd168b29
  • PEFT safetensors SHA-256: f8e7e674ab868b312ff32f403552daa436e7437411e83fa0ece60187431a2d6f
  • Training checkpoint model.safetensors SHA-256: c44425f14ac9d55508f73a6f371e4e2802ed59e646287c3abbec10b653a19840
  • Base fingerprint: e7a8fa743b4920850167b587226e339d651be0244fcc6a9e24fff8ed5073bae5
  • Training mixture SHA-256: b8a87e85f3bf72b61509555aba9148358760d3329ecf37a36cebd94f9358cb03
  • Source registry SHA-256: 309ce8ec262886b3bfaa529934151ae0c1879595c26301e4269da276d95cbfcd

Experiment commands (paths relative to the Statim repository):

  • train: '.venv-train/bin/python' 'tools/finetune/train_lora.py' 'models/laya-multilingual-v9' --mixture 'data/mixture-v8.jsonl.gz' --category pii --registry 'tools/finetune/sources/v6-keep.json' --out 'models/lora-exp1/pii' --device cuda --epochs 2
  • convert: '.venv/bin/python' 'tools/convert_lora.py' 'models/lora-exp1/pii' -o 'models/lora-exp1/pii.lora.gguf' --base 'models/laya-multilingual-v9-f32.gguf' --category pii --name pii
  • serve: 'build-vk/statim' serve -m 'multilingual=models/laya-multilingual-v9-f32.gguf' --adapter 'multilingual:pii=models/lora-exp1/pii.lora.gguf' --device vulkan --threads 16 --port 8098 --no-access-log --inference-timeout 600
  • eval_base: '.venv/bin/python' 'bench/eval_categories.py' --strict --url http://127.0.0.1:8098 --model multilingual --suites pii --n 150 --seed 20260927 --exclude-mixture 'data/mixture-v8.jsonl.gz' --out 'models/lora-exp1/pii.base.jsonl'
  • eval_adapter: '.venv/bin/python' 'bench/eval_categories.py' --strict --url http://127.0.0.1:8098 --model multilingual --suites pii --n 150 --seed 20260927 --exclude-mixture 'data/mixture-v8.jsonl.gz' --adapter pii --out 'models/lora-exp1/pii.adapter.jsonl'

Protocol and experiment: docs/ADAPTERS.md. Full reproduction instructions: REPRODUCE.md.

Intended use and limits

  • This adapter only helps its category; route requests with "pii" or "auto".
  • Bound to Beko2210/statim-decide-multilingual-base 0.7.0 by the base fingerprint e7a8fa743b492085… (statim.lora.base_fingerprint, SHA-256 over the checkpoint's norm and bias tensors); Statim refuses the adapter on a base whose fingerprint differs.
  • Languages outside the evaluated list are untested.
  • Each language cell has 150 items.
  • Do not automate decisions about people without human review.

Licence

The weights may be used under any one of: PolyForm Noncommercial 1.0.0, PolyForm Small Business 1.0.0 (free commercial use below 100 people and 1 M USD revenue), PolyForm Free Trial 1.0.0 (any company, fewer than 32 days), or a Statim commercial licence (COMMERCIAL.md). Texts in LICENSE-MODEL.md. The Statim engine is Apache-2.0.

Training data attribution is listed source by source above, with the row count and licence read from the experiment registry.