Model Releases
Mreaia v1.0.0: Universal Zero-Token Engine & Hardware-Locked Context Distillation for LLMs (>99.9% Reduction)
Hey everyone! π Feeding multi-gigabyte files (SQL dumps, videos, audio, PDFs, and spreadsheets) directly into LLMs often causes context window overflow, GPU VRAM bottlenecks, massive token bills, and
Hey everyone! π Feeding multi-gigabyte files (SQL dumps, videos, audio, PDFs, and spreadsheets) directly into LLMs often causes context window overflow, GPU VRAM bottlenecks, massive token bills, and data leakage risks. To solve this, I built Mreaia (Ω Ψ±Ψ’ΩΨ§): β‘ Key Capabilities: β’ >99.9% Payload Reduction: Distills multi-megabyte datasets into <300-token high-signal structural schemas. β’ Universal Multimodal Extraction: Native speech-to-text transcription (Whisper Metal) and facial emotion dynamics. β’ Mathematical Anti-Exfiltration: Encrypted with AES-256-GCM and node-locked using PBKDF2 (100,000 rounds) to host hardware UUID + source SHA-256 digest. β’ 100% Volatile RAM Ingestion: Streams into LLM prompt contexts without plaintext disk residue. π Open-source & Available now: β’ GitHub: https://github.com/MostafaRaafat91/Mreaia β’ Hugging Face Hub: https://huggingface.co/mostafaraafat91/Mreaia Conceived & created by Mostafa Raafat. Would love your thoughts, benchmarks, and feedback! β submitted by /u/Ok_Seaweed7886 [link] [comments]
Related
- Context window filling up too fast with local models? Here's what actually wastes the most tokens
- Your model fits in VRAM. Your context doesn't. The KV cache math nobody shows you.
- KIV: 1M token context window on a RTX 4070 (12GB VRAM), no retraining, drop-in HuggingFace cache replacement - Works with any model that uses DynamicCache [P]
- Qwen 3.8 27B at 50 tok/s with 100k Context on a 16GB GPU! (beellama.cpp)
Source: r/ollama | 2026-10-10