Google’s "Hope" Model Edges Closer to *Her*-Like AI as Kimi K2 Goes Local with 1-Bit GGUF

AI Research & Breakthroughs

Google’s "Hope" Model: A Precursor to Dynamic, Evolving AI Assistants
Google’s nested learning paper introduces the "Hope" model, a dynamic AI architecture capable of real-time interaction and memory retention during inference. Unlike traditional models, it processes input in chunks and stores patterns, enabling continuous learning from user interactions—potentially paving the way for AI assistants akin to the movie Her.

• The "Hope" model in the nested learning paper from Google is actually a true precursor to "Her"
  • Google Research Blog: Nested Learning

Open-Source & Local AI Models

Kimi K2 Thinking Now Available as 1-Bit GGUF for Local Execution
The Kimi K2 Thinking model has been optimized for local deployment via Unsloth Dynamic 1-bit GGUFs, reducing its RAM footprint to 247GB. The collaboration with Unsloth includes fixes for chat templates and tool-calling support, with performance metrics matching full-precision benchmarks. Instructions for running the model with llama.cpp (including MoE layer offloading) are provided.

• Kimi K2 Thinking 1-bit Unsloth Dynamic GGUFs
  • Hugging Face: Kimi-K2-Thinking Discussions
  • Unsloth Docs: Dynamic GGUFs Guide
  • Hugging Face: Kimi-K2-Thinking-GGUF
  • Unsloth: Local Execution Guide