🤝 Open to Collab

Andrei Ovcharenko

Kreout

1 3

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https://krot.name/

AI & ML interests

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.

Recent Activity

repliedto SeaWolf-AI's post about 7 hours ago

No GPU. No cloud. Just your phone. Most “small” models still beg for a datacenter. Ours fit in your pocket. VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day. Three models, one pocket: 🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power. → https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF 🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast. → https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF 🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk. → https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF The whole family lives here: https://huggingface.co/FINAL-Bench Try our models live in the browser: https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio Private by default. Offline by design. Yours to run. #OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET

reacted to crp4222's post with 🔥 1 day 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

repliedto crp4222's post 1 day 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

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