AI & ML interests

# m1llionAI (Hugging Face Organization) - AI & ML Interests m1llionAI is a Hugging Face-focused organization dedicated to advancing **practical, efficient, and privacy-first large language models (LLMs)** and multimodal AI systems—with a core focus on making cutting-edge AI accessible to consumer hardware and real-world scenarios. Our work is anchored by the open-source M1llion-35B MoE model, and our AI/ML interests span research, engineering, and open-source community collaboration, as detailed below: ## Core AI & ML Interests ### 1. Efficient Large Language Models (LLMs) & Mixture-of-Experts (MoE) Architecture - Design and optimization of **scalable MoE models** (with a focus on 35B+ parameter backbones and 7B+ active parameters) for balanced performance and efficiency. - Innovation in sparse expert routing (e.g., dynamic jitter routing, load balancing for MoE layers) to reduce inference latency and training costs. - Decoder-only Transformer enhancements for long-context modeling (8k+ tokens) and multilingual capability (256k+ vocabulary size). - Curriculum-based pre-training and fine-tuning strategies for LLMs, emphasizing reasoning, factual accuracy, and agentic behavior alignment. ### 2. Extreme Model Compression & Edge Device Deployment - Proprietary and open-source compression technologies (e.g., QEPQ: Quantum-Entangled Pruning & Quantization) to achieve **<10GB deployment size** for 35B parameter models with minimal performance loss (<0.1%). - Low-precision inference optimization (Int8/2-bit) for consumer GPUs, CPUs, and edge devices (no cloud dependency required). - Trade-off engineering between model size, inference speed, and task performance for real-world edge AI scenarios. - Tools and pipelines for seamless conversion of full-precision LLMs to edge-ready, compressed formats (supporting TensorFlow 2.x and PyTorch 2.x). ### 3. Privacy-First AI Systems & Local-Only Intelligent Memory - **Local-first LLM deployment** to eliminate cloud data transmission, protecting user privacy and sensitive data (no server-side data storage or processing). - Design of contextual memory engines (e.g., Person X Memory Symbiosis Engine) for long-term, secure retention of user habits, preferences, and routines—all stored and processed on-device. - Privacy-preserving fine-tuning (PPFT) for customizing LLMs without exposing user data or model weights. - Zero-trust AI architectures that prevent unauthorized access to model internals and intermediate processing states. ### 4. Multimodal Intelligent Agents & Scene-Aware AI - Integration of vision-language models (VLMs) with **screen recognition and autonomous action capabilities** (e.g., clicking, scrolling, UI navigation) for practical personal assistant use cases. - Vision Perception Module (VPM) optimization for processing images, video, and screen content, with cross-modal fusion for context-aware reasoning. - Emotion-aware AI systems that detect user emotional states and deliver contextually appropriate, actionable advice (beyond keyword matching). - Multi-format input/output support (text, image, video, audio) for end-to-end task automation (e.g., content creation, document processing, repetitive office tasks). ### 5. Trustworthy & Secure LLMs - Hundreds Security Architecture (HSA) development to mitigate LLM vulnerabilities: - Adversarial attack detection (e.g., prompt injection) via Contextual Threat Monitor (CTM). - Real-time model weight integrity verification via Quantum Weight Attestation (QWA). - Encryption of intermediate hidden states for data confidentiality during inference. - Hallucination suppression technologies (e.g., Reality Anchoring) to achieve <1.2% hallucination rates for factual and reasoning tasks. - Auditable and verifiable LLM outputs for high-risk scenarios (e.g., technical decision-making, document validation). ### 6. Open-Source LLM Ecosystem & Community Collaboration - Benchmarking LLMs on industry-standard and custom evaluation suites (MMLU-Pro, HumanEvo, GSM8K, MT-Bench, KoNET) to ensure reproducible performance. - Contributing to Hugging Face Datasets, Transformers, and Evaluate libraries to advance accessible AI. - Collaborating with open-source teams (pure-team, cogent-ai, Arc4, neo-ai-team) to build complementary AI tools and expand MoE/edge AI ecosystems. - Enabling dual-framework support (TensorFlow 2.x / PyTorch 2.x) for broad developer adoption and customization. ### 7. Future-Focused AGI Exploration - Research on Chromos-Fabric (AGI prototype) to advance general-purpose reasoning, cross-domain adaptation, and self-improving AI systems. - Scaling MoE models to higher parameter counts while maintaining edge-deployable efficiency. - Cross-device edge AI collaboration for distributed, privacy-preserving AI workflows. ## Our Commitment to the Hugging Face Community m1llionAI is dedicated to open-sourcing our models, tools, and technical reports to foster collaborative innovation. We welcome researchers, developers, and enthusiasts to: - Explore our M1llion-35B model on Hugging Face Hub. - Contribute to model optimization, compression, and benchmarking via GitHub. - Collaborate on privacy-first and edge AI use cases to expand the reach of practical LLMs. For more details, visit our ArcOffical (https://huggingface.co/ArcOffical)

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