🏗️ Building on HF

Dipankar Sarkar PRO

dipankarsarkar

4 748 149

https://www.dipankar.cc

AI & ML interests

Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.

Recent Activity

reacted to MaziyarPanahi's post with 🔥 less than a minute ago

Announcing: OpenMed Multilingual PII Detection Models Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian. All Apache 2.0 licensed. Free for commercial use. No restrictions. Performance: - French: 97.97% F1 (top model) - German: 97.61% F1 (top model) - Italian: 97.28% F1 (top model) All top-10 models per language exceed 96% F1 Coverage: 55+ PII entity types per language Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian) Language-specific address, phone, and name patterns Training Data: French: 49,580 samples German: 42,250 samples Italian: 40,944 samples Why Multilingual? European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages. Effective de-identification requires: - Native language understanding — not translation - Local ID format recognition — each country has unique patterns - Cultural context awareness — names, addresses, and formats vary - These models deliver production-ready accuracy without requiring data to leave your infrastructure or language. HIPAA & GDPR Compliance Built for US and European privacy regulations: - On-premise deployment: Process data locally with zero external dependencies - Data sovereignty: No API calls, no cloud services, no cross-border transfers - Air-gapped capable: Deploy in fully isolated environments if required - Regulatory-grade accuracy: Supporting Expert Determination standards - HIPAA and GDPR compliance across languages, without compliance gaps. Use Cases - Hospital EHR systems: Automated patient record de-identification - Clinical research: Multilingual dataset preparation for studies - Insurance companies: Claims processing across https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification

reacted to Kseniase's post with 🔥 1 minute ago

10 Latest Preference Optimization Techniques Models need feedback on what makes outputs “good” or “bad.” Policy optimization (PO) turns preferences and rewards into actual training signals. This field is evolving quickly, moving far beyond classics like PPO and GRPO. So here is our overview of 10 newest PO methods: 1. Pref-GRPO → https://huggingface.co/papers/2508.20751 Stabilizes text-to-image reinforcement learning (RL) with pairwise preference rewards and a unified UNIGENBENCH benchmark 2. PVPO (Policy with Value Preference Optimization) → https://huggingface.co/papers/2508.21104 This critic-free RL method uses a pre-trained model as a reference anchor to reduce bias and guide learning, selecting high-value examples through data pre-sampling 3. DCPO (Dynamic Clipping Policy Optimization) → https://huggingface.co/papers/2509.02333 Uses dynamic clipping, which adjusts probability limits per token for better token exploration, and smooth reward standardization to balance rewards over training steps and prevent wasted updates 4. ARPO (Agentic Reinforced Policy Optimization) → https://huggingface.co/papers/2507.19849 Optimizes multi-turn LLM agents that use external tools. It uses an entropy-based adaptive rollout to explore post-tool use and an advantage attribution method to better assign credit across steps, leading to more efficient tool use with fewer resources 5. GRPO-RoC (Group Relative Policy Optimization with Resampling-on-Correct) → https://huggingface.co/papers/2508.20722 Oversamples rollouts, then resamples them to keep diverse mistakes and only the highest-quality correct answers. It reduces noises and ends up with stronger reasoning in a code environment Read further below ⬇️ If you like this, also subscribe to the Turing post: https://www.turingpost.com/subscribe

upvoted a paper 1 minute ago

Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination

View all activity

Organizations

Posts 2

Post

508

Your issue tracker is in the wrong place.

It lives on a server. Your code lives in git. So every time an agent picks up work it makes an API call, burns a token, fights a rate limit, and still cannot see what the other agent just did.

Move the issues into the repo. Append-only event log in git refs. Branches when you branch, merges when you merge, CRDT so two agents never conflict. No server, no database.

The coordination signal that PR-level telemetry misses lives before the pull request. The paper, and a live demo running the real tool:

Before the Pull Request: Mining Multi-Agent Coordination (2606.19616)
https://huggingface.co/spaces/neullabs/grite

If your agents share a repo, where does their shared state actually live right now?