temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8

OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8

OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8 is a CPU-oriented dynamic int8 export of temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1.

Use this release when you want a smaller, faster CPU deployment and can accept some accuracy loss relative to the full checkpoint.

What This Release Is

• —A dynamic int8 torch export stored as model_quantized.pt
• —Derived from temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1
• —Intended for PPSN masking with the bundled word_aligned decoder
• —Tuned for CPU inference, not GPU inference

Important Limitation

This is not a standard AutoModelForTokenClassification.from_pretrained(...) package. Use the bundled inference_word_aligned.py loader, which knows how to download and load model_quantized.pt.

Recommended Inference

python3 inference_word_aligned.py --ppsn-min-score 0.7 --text "My PPSN is 1234567TW and I need help with my housing grant." --json

To load directly from the Hub:

python3 inference_word_aligned.py --model temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8 --ppsn-min-score 0.7 --text "My PPSN is 1234567TW and I need help with my housing grant." --json

Benchmark Summary

Measured on the multilingual PPSN suite spanning:

• —gov data
• —citizen-to-government chat
• —HSE medical text
• —English, Irish Gaelic, and additional European / Ukrainian / Russian / Chinese / Japanese examples

Tradeoff

• —Q8 is much faster on CPU than the full checkpoint
• —Q8 still clearly outperforms the original base model on PPSN detection
• —Q8 is less accurate than the full checkpoint and needs a higher PPSN threshold

Included Files

• —Quantized model:
• —model_quantized.pt
• —quantization.json
• —Tokenizer / config:
• —config.json
• —tokenizer.json
• —tokenizer_config.json
• —special_tokens_map.json
• —vocab.txt
• —label_meta.json
• —Inference / QA:
• —inference_word_aligned.py
• —qa_config.json
• —pyproject.toml
• —Benchmark artifacts:
• —eval/

License

This quantized derivative is distributed under Apache-2.0, consistent with the canonical full model and the upstream base model. See NOTICE for attribution.

<!-- portfolio-comparison:start -->

Portfolio Comparison

Updated: 2026-03-16.

Use this section for the fastest public comparison across the temsa PII masking portfolio.

• —The first core table only includes public checkpoints that ship both comparable q8 accuracy and q8 CPU throughput.
• —The first PPSN table only includes public artifacts that ship comparable PPSN accuracy and CPU throughput.
• —Missing cells in the archive tables mean the older release did not ship that metric in its public bundle.
• —DiffMask rows use the reconciled clean_single_pass harness that matches the deployed runtime.
• —GlobalPointer rows use the public raw-only span-matrix release bundle and its packaged q8 ONNX artifact.
• —The same content is shipped as PORTFOLIO_COMPARISON.md inside each public model repo.

Irish Core PII: Comparable Public Checkpoints

Irish Core PII: Other Public Checkpoints

Finance-boundary q8 F1 is 1.0000 for OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6, OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7, OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8, and all public IrishCore-DiffMask releases from rc1 to rc6. OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc5 ships 0.8750 on that public q8 suite.

PPSN-Only: Comparable Public Artifacts

PPSN-Only: Historical Public Checkpoints

If you need the strongest current raw-only Irish core model, start with IrishCore-GlobalPointer-135M-v1-rc4. If you need the fastest CPU-first raw-only line, compare it against IrishCore-DiffMask-135M-v1-rc6. If you need a PPSN-only artifact, compare the canonical fp32, fp16, and q8 variants of OpenMed-mLiteClinical-IrishPPSN-135M-v1 directly in the table above. <!-- portfolio-comparison:end -->