view article Article Meta is back with Muse Glimmer: local, agentic, multimodal, and open source +2 pcuenq, merve, burtenshaw, ariG23498 • 25 days ago • 110
Nanbeige4.2-3B MLX Collection MLX conversions of Nanbeige4.2-3B (Looped Transformer): bf16 + 2-8 bit quants • 8 items • Updated 3 days ago • 2
Nanbeige4.2-3B quants (looped transformer) Collection FP8 default, LoopShield 4-bit loop-aware placement, NVFP4A16 speed pick. Gates + failure modes on each card. • 4 items • Updated Jul 23 • 1
view article Article Deploy local agents everywhere with LFM2.5-2.6B LiquidAI • about 1 month ago • 96
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM Paper • 2607.27205 • Published Jul 29 • 140
🎯 Liquid Nanos Collection Library of task-specific models: https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices • 36 items • Updated Aug 3 • 135
VisionPsy Collection SOTA General-Purpose Multimodal Vision-Language Models for Edge deployment • 4 items • Updated Jul 30 • 8
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Paper • 2010.05646 • Published Oct 12, 2020 • 2
NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference Paper • 2508.05835 • Published Aug 7, 2025 • 2
Tmax Collection Data and models associated with "Tmax: A simple recipe for terminal agents". paper: https://arxiv.org/abs/2606.23321 • 23 items • Updated Jun 23 • 19
K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs Paper • 2605.09635 • Published Jul 23 • 64
SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems Paper • 2604.04514 • Published Apr 6 • 8
MIRA World Model Collection Developed with General Intuition (https://www.generalintuition.com/), in collaboration with Epic Games. MIRA is a Multiplayer Interactive World Model. • 2 items • Updated Jul 15 • 1
Cactus Hybrid Collection A small, on-device model is fast and private, but sometimes wrong. We post-train models to know when they are wrong, run on any framework. • 4 items • Updated Jul 22 • 11