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temsa/
OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16

Name: OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16
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OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16

OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16 is a half-precision GPU-oriented release of temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1.

Use this release when you want the same PPSN masking behavior as the full model with lower memory use and faster inference on ROCm / CUDA GPUs, or on CPU when your serving path is low-batch and short-sequence.

What This Release Is

A standard transformers checkpoint stored in fp16
Derived from temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1
Intended for PPSN masking with the bundled word_aligned decoder
Works on CPU and GPU; it showed the largest speedup on GPU, and a CPU speedup on the current low-batch word_aligned path

Recommended Inference

Use the bundled entrypoint, which loads the checkpoint with dtype=auto so the stored fp16 weights are used directly. This is a good fit for low-batch CPU serving and for GPU inference:

python3 inference_word_aligned.py --ppsn-min-score 0.4 --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-fp16 --ppsn-min-score 0.4 --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, and HSE medical text in English, Irish Gaelic, and additional European / Ukrainian / Russian / Chinese / Japanese examples.

CPU behavior depends on workload shape. The end-to-end word_aligned benchmark below is favorable to fp16, but the batched CPU runtime matrix in eval/runtime_matrix_cpu_fp32_vs_fp16_compact.md shows that fp16 loses to fp32 once batch size and sequence length grow.

For CPU guidance in this model card:

short text: about <= 32 tokenizer tokens
33-63 tokens: gray zone, benchmark your workload
>= 64 tokens: not short for this recommendation
low batch: batch_size = 1
batch_size = 2-4: gray zone, benchmark your workload
batch_size >= 8: not low batch for this recommendation

Practical CPU rule:

Prefer this fp16 repo for batch_size = 1 and about <= 32 tokens
Prefer the canonical fp32 repo once batch size or sequence length grows materially

Variant	Device	Threshold	F1	Precision	Recall	Throughput ex/s	Size
Full fp32	GPU	0.4	0.9704	0.9647	0.9762	57.40	514 MB
fp16	GPU	0.4	0.9704	0.9647	0.9762	224.14	257 MB
Full fp32	CPU	0.4	0.9704	0.9647	0.9762	31.27	514 MB
fp16	CPU	0.4	0.9704	0.9647	0.9762	45.80	257 MB

Small PPSN regression suites with fp16 matched the full model in this workspace:

User raw F1: 0.8000
QA v6 validated F1: 0.6667
QA v8 F1: 0.7385

Tradeoff

Roughly half the model size vs the fp32 checkpoint
Same measured PPSN quality as the fp32 release in these tests
Faster GPU inference on the AMD ROCm setup used here
Faster CPU inference than the fp32 checkpoint on the current low-batch end-to-end path
In a batched CPU forward matrix, fp16 became slower than fp32 once batch size and sequence length increased; see eval/runtime_matrix_cpu_fp32_vs_fp16_compact.md
The int8 release is still the highest-throughput CPU option, but it gives up more PPSN quality

Included Files

Core model:
- model.safetensors
- config.json
- precision.json
- tokenizer.json
- tokenizer_config.json
- special_tokens_map.json
- label_meta.json
- vocab.txt
Inference / QA:
- inference_word_aligned.py
- qa_config.json
- pyproject.toml
Evaluation:
- eval/

License

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

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

Repo	Stack	Full Core F1	Q8 Core F1	Q8 Multilingual PPSN F1	Q8 Core ex/s
`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc4`	4-layer GlobalPointer distilled fast student	1.0000	1.0000	0.9333	299.0
`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc3`	4-layer GlobalPointer distilled fast student	1.0000	1.0000	0.9333	317.9
`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc2`	4-layer GlobalPointer distilled fast student	1.0000	1.0000	0.9333	292.5
`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc1`	4-layer GlobalPointer distilled fast student	1.0000	1.0000	0.9333	337.3
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc27`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	270.0
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc25`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	212.1
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc24`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	278.9
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc23`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	237.6
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc22`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	106.8
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc21`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	150.8
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc20`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	181.9
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc19`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	73.1
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc18`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	126.2
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc17`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	125.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc16`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	125.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc15`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	125.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc14`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	119.2
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc13`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	126.1
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc12`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	73.6
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc11`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	94.1
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc10`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	125.8
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc9`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	119.8
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc8`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	128.9
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc7`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	89.0
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc6`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	89.0
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc5`	GlobalPointer raw-only + context labels	1.0000	1.0000	0.9333	84.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc4`	GlobalPointer raw-only + context labels	0.9935	0.9935	0.9333	61.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc3`	GlobalPointer raw-only + context labels	0.9935	0.9935	0.9333	61.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc2`	GlobalPointer raw-only + context labels	0.9935	0.9935	0.9222	61.5
`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc1`	GlobalPointer raw-only + context labels	0.9935	0.9935	0.9222	61.5
`temsa/IrishCore-GlobalPointer-135M-v1-rc4`	GlobalPointer raw-only span-matrix	1.0000	1.0000	0.9333	221.6
`temsa/IrishCore-GlobalPointer-135M-v1-rc3`	GlobalPointer raw-only span-matrix	1.0000	1.0000	0.9213	204.9
`temsa/IrishCore-GlobalPointer-135M-v1-rc2`	GlobalPointer raw-only span-matrix	0.9934	0.9934	0.9326	231.2
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8`	Raw-only token-span	0.9737	0.9737	0.9176	46.1
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7`	Hybrid classifier + generated scanner spec	1.0000	0.9934	1.0000	30.0
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6`	Hybrid classifier + repair decoders	1.0000	0.9934	1.0000	29.5
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc5`	Hybrid classifier + repair decoders	0.9737	0.9669	0.9333	34.4
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc4`	Hybrid classifier + repair decoders	0.9870	0.9740	0.9600	114.2
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3`	Hybrid classifier + repair decoders	0.9806	0.9677	0.9333	44.9
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2`	Hybrid classifier + repair decoders	0.9554	0.9615	0.7887	119.1
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1`	Hybrid classifier baseline	0.9530	0.9333	0.9882	103.3
`temsa/IrishCore-DiffMask-135M-v1-rc6`	DiffMask token-span, scanner-free	0.9801	0.9733	0.9274	130.3
`temsa/IrishCore-DiffMask-135M-v1-rc5`	DiffMask token-span, scanner-free	0.9733	0.9733	0.9379	249.2
`temsa/IrishCore-DiffMask-135M-v1-rc4`	DiffMask token-span, scanner-free	0.9733	0.9733	0.9371	29.5
`temsa/IrishCore-DiffMask-135M-v1-rc3`	DiffMask token-span, scanner-free	0.9664	0.9664	0.9591	30.0
`temsa/IrishCore-DiffMask-135M-v1-rc2`	DiffMask token-span, scanner-free	0.9664	0.9664	0.9212	247.1
`temsa/IrishCore-DiffMask-135M-v1-rc1`	DiffMask token-span, scanner-free	0.9801	0.9934	0.9412	251.2

Irish Core PII: Other Public Checkpoints

Repo	Stack	Full Core F1	Q8 Core F1	Q8 Multilingual PPSN F1	Notes
`temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc1`	Hybrid classifier prototype	0.9487	—	—	Predates the public q8 artifact.

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

Repo	Artifact	Irish Large F1	Multilingual PPSN F1	User Raw F1	QA v8 F1	CPU ex/s
`temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1`	fp32 canonical checkpoint	0.8979	0.9704	0.8000	0.7385	57.4
`temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16`	fp16 CPU/GPU artifact	—	0.9704	0.8000	0.7385	45.8
`temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8`	dynamic int8 CPU artifact	—	0.9040	—	—	132.1

PPSN-Only: Historical Public Checkpoints

Repo	Main Published Metrics	Notes
`temsa/OpenMed-PPSN-mLiteClinical-v1`	same as canonical fp32 repo: multilingual 0.9704, user raw 0.8000	Legacy alias; prefer `temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1`.
`temsa/OpenMed-PPSN-v6-raw-rc2`	irish_reg_v5 0.8750; user_raw 0.8000; qa_v8 0.7385	Raw PPSN-only research checkpoint; no packaged multilingual CPU benchmark row.
`temsa/OpenMed-PPSN-v5_1`	irish_large_v2 raw 0.9285; qa_v6 hybrid strict 1.0000	Hybrid PPSN-only checkpoint; predates the canonical multilingual suite packaging.
`temsa/OpenMed-PPSN-v5`	irish_reg_v5 raw 0.8235; irish_reg_v5 hybrid strict 1.0000	Hybrid PPSN-only checkpoint; predates the canonical multilingual suite packaging.
`temsa/OpenMed-PPSN-v4`	synthetic non-PPSN drift check only	Predates the current PPSN eval suite; no packaged apples-to-apples multilingual CPU row.

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.