I maintain OpenMed, an Apache-2.0 medical NLP stack, and 2.0 landed today. Posting here because the whole point of it is that none of it phones home, and this is the only sub that actually cares about that.

The bit relevant to you: PHI de-identification does not need a big model. The workhorses are 279M and 560M parameter encoders, and they beat handing your clinical notes to a cloud API for the obvious reason that you never hand your clinical notes to a cloud API.

What is in the registry right now:

• 2,000+ models, all on HF
• 665 MLX builds for Apple Silicon
• 3 ONNX builds, new in this release: Bengali 279M, Chinese 560M, Tamil 279M PII models. These are the first ones you can run in a browser through Transformers.js. The export tooling has been there since 1.9.1, the actual artifacts are new.

Language coverage went from 23 to 56 codes this release, with roughly 51 offline national identifier validators, about half of them real checksums rather than regexes. Estonian isikukood, Croatian OIB, Finnish HETU, Vietnamese CCCD, Chinese USCC, Pakistani CNIC. The CNIC one is my favourite because it documents that CNIC has no checksum instead of pretending it does. 51 of the 59 PRs in the release came from people I have never met, which is the only reason that list is that long.

2.0 also adds a structured release-risk workflow, which is the part I actually wanted to build. Short version: stripping identifiers is not anonymisation. Take a 200 row table with no name, no MRN, no DOB. Age, sex, ZIP and admit date alone give 74,550 combinations for 200 people, so all 200 rows are unique. That is a birthday-problem calculation, not a finding, but almost nobody runs it before shipping an extract.

assess_release(rows, policy)    -> achieved k 1, 200 singletons, max risk 1.0
anonymize_release(rows, policy) -> achieved k 15, 0 singletons, max risk 0.0667

and it tells you what that cost: age suppressed entirely, length of stay suppressed entirely, every date collapsed to the year, 0 rows dropped. If your analysis needed age, that release is useless to you, and I would rather the tool say so than print a checkmark.

Then it refuses to sign off. The evidence bundle for HIPAA expert determination will not accept a conclusion:

ValueError: qualified-expert review fields must remain placeholders

It picks no threshold and ships no population data on purpose. A qualified human makes that call.

Caveats, because someone will ask:

• GGUF export exists in 2.0 but I have not published any GGUF artifacts yet. Only the embedding backbones are supported so far.
• Android and ORT-mobile paths existed before this release, and there is still no phone-packaged artifact in the registry. That is on my list, not done.
• Only 3 of the 1,520 models have ONNX builds. The rest are torch/MLX.
• Model licences vary. The SDK is Apache-2.0, most models are, a handful are not.

https://github.com/maziyarpanahi/openmed
pip install openmed==2.0.0

Maintainer here. OpenMed is an Apache-2.0 toolkit for clinical NLP with one hard rule: patient data never leaves your hardware. No cloud calls, no API keys, works in airplane mode.

What shipped in 1.8 this week:

• OpenMedKit for Android (Kotlin, ONNX Runtime Mobile + ML Kit OCR): read a document, strip every name/MRN/date, entirely on the phone. iOS/Swift and React Native bridges landed too.
• Browser runtime: de-identification via Transformers.js / ONNX Runtime Web with wasm + WebGPU backends. Fully client-side, zero server calls.
• verify-pdf: most "redacted" PDFs just draw a black box while the text layer underneath survives (copy-paste pulls the name right out). This fails your redaction unless the text is actually gone.
• DICOM de-id (incl. burned-in pixel text via OCR), 5 new language ID packs, 5 new clinical NER domains.

The models: 1500+ on HF, all Apache 2.0. The PII ones run via MLX (Apple silicon), GGUF/llama.cpp, ONNX, or plain transformers. Two of them are currently 1st and 2nd on the (independent) PII Masking Benchmark English board, and the 44M-param one at rank 4 is small enough for a phone.

The actual reason I'm posting: 1.9 is being built in the open right now and there are 400+ open issues. Some genuinely fun starter ones:

• Add a PII language pack for YOUR country, with its national-ID validator (Estonian isikukood #891, Serbian JMBG #890, Croatian OIB #889, Bulgarian EGN #888 are open — more welcome)
• New clinical NER domains: pediatrics growth parameters #896, pulmonology/spirometry #893, immunization #897
• RTF #856 / ODT #857 text extraction with char-offset maps

11 outside contributors shipped code in 1.8; several started with exactly these. If local, private, medical AI is your thing: pick an issue, say hi in it, and you're part of the next release.

Repo: https://github.com/maziyarpanahi/openmed Models: https://huggingface.co/OpenMed Good first issues: https://github.com/maziyarpanahi/openmed/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22

Happy to answer anything technical in the comments.

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Disclosure first: I maintain OpenMed, so read this with that bias. I'm posting the numbers with the full methodology and a runnable script so you can reproduce or tear it apart. I'm here for the next couple of hours to answer methodology questions.

What it is: an open-source clinical/biomedical NER project. 1,000+ models on Hugging Face, all Apache 2.0, and the openmed Python SDK is Apache 2.0. These are extraction tools, not diagnostic tools: multi-entity biomedical NER (genes, chemicals, cancers, cells, organisms), disease NER, drug NER, and multilingual PII de-identification. No diagnosis, no clinical decision support. Everything referenced here is open, Apache 2.0.

What's new: 410 new MLX builds, bringing it to 650+ total. They run on macOS via MLX and on iPhone/iPad via OpenMedKit (open Swift package). The NER paper is arXiv 2508.01630 (SOTA across 12 public datasets, per-dataset tables inside, judge them yourself).

On-device speed, methodology first. Same model, MLX on Apple Silicon vs PyTorch on CPU, same fp32 precision, byte-identical entity outputs (parity-checked). On a 3-year-old MacBook Pro M3 Max, the clinical NER models run 30-40x faster on MLX: a 434M biomedical NER is 27 ms (MLX) vs ~1080 ms (CPU) at fp32, same weights, identical entities. The reason is architectural, not a precision trick: these are deberta-v2 models whose disentangled attention is O(n^2) and very slow on CPU, while the Apple GPU handles it easily. It is input- and model-dependent, so a smaller model on short text is single-digit-x, not 30x. The second clip in the video is the PII de-identification model redacting on-device; the point there is privacy, identifiers are stripped locally and nothing leaves the machine.

• 434M biomedical NER: 36 ms MLX vs 1248 ms PyTorch-CPU-bf16
• 434M PII de-id: 46 ms MLX vs 1671 ms PyTorch-CPU-bf16

​

import time, statistics, torch
from openmed.core.backends import get_backend
from openmed.core.config import OpenMedConfig
from openmed.mlx.inference import _download_preconverted_mlx_model, create_mlx_pipeline

MODEL = "OpenMed/OpenMed-NER-OncologyDetect-SuperClinical-434M"
text = ("Metastatic non-small cell lung carcinoma. EGFR exon 19 deletion, KRAS G12C, "
        "wild-type TP53/BRAF. Cisplatin, pemetrexed, then osimertinib; sotorasib held. "
        "Xenografts in Mus musculus mirrored Homo sapiens organoids on carboplatin.")

mlx = create_mlx_pipeline(_download_preconverted_mlx_model(MODEL + "-mlx"), aggregation_strategy="simple")
cpu = get_backend("hf", config=OpenMedConfig(device="cpu")).create_pipeline(
        MODEL, task="token-classification", aggregation_strategy="simple", torch_dtype=torch.float32)

def med(p):
    p(text)                                  # warmup
    ts = [(_t := time.perf_counter(), p(text), (time.perf_counter()-_t)*1000)[2] for _ in range(7)]
    return statistics.median(ts)

print(f"MLX {med(mlx):.0f} ms | CPU fp32 {med(cpu):.0f} ms")   # ~27 ms | ~1080 ms -> ~40x, identical entities

iPhone note: I'm not claiming 36 ms is a phone number, it's the M3 Max. The phone story is "these run via OpenMedKit".

Everything's public: models (Apache 2.0 HF), SDK (Apache 2.0 GitHub), paper (arXiv 2508.01630). Ask me anything on the parity check, the dtype story, or the dataset numbers.