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18 following Java backend development, Spring Boot, Kafka, infrastructure and automation. Interested in local AI tools and practical developer workflows. Open-source developer tools: https://github.com/krotname.
reacted to crp4222 'spost with 🔥 about 14 hours ago I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilm replied to crp4222 'spost about 14 hours ago I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilm replied to jiteshkprasad 'spost 1 day ago Okay so, we at https://huggingface.co/udgamlabs just released Lekhani, a Hindi transliterator that is 0.87 MB in total. You type Hindi in English letters, like "kaise ho", and it writes कैसे हो.
The whole thing is a 0.81M-parameter model at 4 bits, a 200,000-word list and a 5 KB JavaScript runner with no dependencies. It runs in the browser or in Node, so nothing you type leaves your device.
On the Aksharantar Hindi test it gets 65.9% of words exactly right, 7 points more than IndicXlit's model on its own (58.9%), which has 14x more parameters. IndicXlit's full package is still ahead at 68.7%, but its word list alone takes 358 MB of memory, about 400 times the size of all of Lekhani. I think for a web page or an app, that difference matters a lot.
It reads one word at a time, so "main" and "mein" can come out the same, and a few common words still come out wrong. The card has the full table, the other open models we measured and where it falls short.
It's Apache-2.0 for the model and runner, and CC0 for the word list. If you type Hindi in English letters, try it and tell me which words it gets wrong.
Model: https://huggingface.co/udgamlabs/Lekhani
Try it: https://udgamlabs.com/models/lekhani
Code: https://github.com/udgamlabs/lekhani View all activity 
